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Honerlaw J, Ho YL, Fontin F, Murray M, Galloway A, Heise D, Connatser K, Davies L, Gosian J, Maripuri M, Russo J, Sangar R, Tanukonda V, Zielinski E, Dubreuil M, Zimolzak AJ, Panickan VA, Cheng SC, Whitbourne SB, Gagnon DR, Cai T, Liao KP, Ramoni RB, Gaziano JM, Muralidhar S, Cho K. Centralized Interactive Phenomics Resource: an integrated online phenomics knowledgebase for health data users. J Am Med Inform Assoc 2024; 31:1126-1134. [PMID: 38481028 PMCID: PMC11031216 DOI: 10.1093/jamia/ocae042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Accepted: 02/21/2024] [Indexed: 04/21/2024] Open
Abstract
OBJECTIVE Development of clinical phenotypes from electronic health records (EHRs) can be resource intensive. Several phenotype libraries have been created to facilitate reuse of definitions. However, these platforms vary in target audience and utility. We describe the development of the Centralized Interactive Phenomics Resource (CIPHER) knowledgebase, a comprehensive public-facing phenotype library, which aims to facilitate clinical and health services research. MATERIALS AND METHODS The platform was designed to collect and catalog EHR-based computable phenotype algorithms from any healthcare system, scale metadata management, facilitate phenotype discovery, and allow for integration of tools and user workflows. Phenomics experts were engaged in the development and testing of the site. RESULTS The knowledgebase stores phenotype metadata using the CIPHER standard, and definitions are accessible through complex searching. Phenotypes are contributed to the knowledgebase via webform, allowing metadata validation. Data visualization tools linking to the knowledgebase enhance user interaction with content and accelerate phenotype development. DISCUSSION The CIPHER knowledgebase was developed in the largest healthcare system in the United States and piloted with external partners. The design of the CIPHER website supports a variety of front-end tools and features to facilitate phenotype development and reuse. Health data users are encouraged to contribute their algorithms to the knowledgebase for wider dissemination to the research community, and to use the platform as a springboard for phenotyping. CONCLUSION CIPHER is a public resource for all health data users available at https://phenomics.va.ornl.gov/ which facilitates phenotype reuse, development, and dissemination of phenotyping knowledge.
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Affiliation(s)
- Jacqueline Honerlaw
- Centralized Interactive Phenomics Resource (CIPHER), Office of Research and Development, Veterans Health Administration, Washington, DC 20002, United States
- VA Boston Healthcare System, Boston, MA 02111, United States
| | - Yuk-Lam Ho
- Centralized Interactive Phenomics Resource (CIPHER), Office of Research and Development, Veterans Health Administration, Washington, DC 20002, United States
- VA Boston Healthcare System, Boston, MA 02111, United States
| | - Francesca Fontin
- Centralized Interactive Phenomics Resource (CIPHER), Office of Research and Development, Veterans Health Administration, Washington, DC 20002, United States
- VA Boston Healthcare System, Boston, MA 02111, United States
| | - Michael Murray
- Centralized Interactive Phenomics Resource (CIPHER), Office of Research and Development, Veterans Health Administration, Washington, DC 20002, United States
- VA Boston Healthcare System, Boston, MA 02111, United States
| | - Ashley Galloway
- Centralized Interactive Phenomics Resource (CIPHER), Office of Research and Development, Veterans Health Administration, Washington, DC 20002, United States
- VA Boston Healthcare System, Boston, MA 02111, United States
| | - David Heise
- Oak Ridge National Laboratory (ORNL), Oak Ridge, TN 37830, United States
| | - Keith Connatser
- Oak Ridge National Laboratory (ORNL), Oak Ridge, TN 37830, United States
| | - Laura Davies
- Oak Ridge National Laboratory (ORNL), Oak Ridge, TN 37830, United States
| | - Jeffrey Gosian
- Centralized Interactive Phenomics Resource (CIPHER), Office of Research and Development, Veterans Health Administration, Washington, DC 20002, United States
- VA Boston Healthcare System, Boston, MA 02111, United States
| | - Monika Maripuri
- Centralized Interactive Phenomics Resource (CIPHER), Office of Research and Development, Veterans Health Administration, Washington, DC 20002, United States
- VA Boston Healthcare System, Boston, MA 02111, United States
| | - John Russo
- Centralized Interactive Phenomics Resource (CIPHER), Office of Research and Development, Veterans Health Administration, Washington, DC 20002, United States
- VA Boston Healthcare System, Boston, MA 02111, United States
- Department of Computer Science, Landmark College, Putney, VT 05346, United States
| | - Rahul Sangar
- Centralized Interactive Phenomics Resource (CIPHER), Office of Research and Development, Veterans Health Administration, Washington, DC 20002, United States
- VA Boston Healthcare System, Boston, MA 02111, United States
| | - Vidisha Tanukonda
- Centralized Interactive Phenomics Resource (CIPHER), Office of Research and Development, Veterans Health Administration, Washington, DC 20002, United States
- VA Atlanta Healthcare System, Decatur, GA 30033, United States
| | - Edward Zielinski
- Centralized Interactive Phenomics Resource (CIPHER), Office of Research and Development, Veterans Health Administration, Washington, DC 20002, United States
- VA Boston Healthcare System, Boston, MA 02111, United States
| | - Maureen Dubreuil
- VA Boston Healthcare System, Boston, MA 02111, United States
- Section of Rheumatology, Boston University Chobanian and Avedisian School of Medicine, Boston, MA 02118, United States
| | - Andrew J Zimolzak
- Center for Innovations in Quality, Effectiveness and Safety, Michael E. DeBakey VA Medical Center, Houston, TX 77030, United States
- Department of Medicine, Baylor College of Medicine, Houston, TX 77030, United States
| | - Vidul A Panickan
- VA Boston Healthcare System, Boston, MA 02111, United States
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, United States
| | - Su-Chun Cheng
- VA Boston Healthcare System, Boston, MA 02111, United States
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, United States
| | - Stacey B Whitbourne
- VA Boston Healthcare System, Boston, MA 02111, United States
- Million Veteran Program (MVP) Coordinating Center, VA Boston, Boston, MA 02111, United States
- Division of Aging, Department of Medicine, Brigham and Women’s Hospital, Boston, MA 02115, United States
- Department of Medicine, Harvard Medical School, Boston, MA 02115, United States
| | - David R Gagnon
- VA Boston Healthcare System, Boston, MA 02111, United States
- Department of Biostatistics, Boston University School of Public Health, Boston, MA 02118, United States
| | - Tianxi Cai
- VA Boston Healthcare System, Boston, MA 02111, United States
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, United States
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA 02115, United States
| | - Katherine P Liao
- VA Boston Healthcare System, Boston, MA 02111, United States
- Department of Medicine, Harvard Medical School, Boston, MA 02115, United States
- Division of Rheumatology, Inflammation, and Immunity, Brigham and Women’s Hospital, Boston, MA 02115, United States
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, United States
| | - Rachel B Ramoni
- Office of Research and Development, Veterans Health Administration, Washington, DC 20002, United States
| | - J Michael Gaziano
- VA Boston Healthcare System, Boston, MA 02111, United States
- Million Veteran Program (MVP) Coordinating Center, VA Boston, Boston, MA 02111, United States
- Division of Aging, Department of Medicine, Brigham and Women’s Hospital, Boston, MA 02115, United States
- Department of Medicine, Harvard Medical School, Boston, MA 02115, United States
| | - Sumitra Muralidhar
- Office of Research and Development, Veterans Health Administration, Washington, DC 20002, United States
| | - Kelly Cho
- Centralized Interactive Phenomics Resource (CIPHER), Office of Research and Development, Veterans Health Administration, Washington, DC 20002, United States
- VA Boston Healthcare System, Boston, MA 02111, United States
- Million Veteran Program (MVP) Coordinating Center, VA Boston, Boston, MA 02111, United States
- Division of Aging, Department of Medicine, Brigham and Women’s Hospital, Boston, MA 02115, United States
- Department of Medicine, Harvard Medical School, Boston, MA 02115, United States
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Orkaby AR, Lu B, Ho YL, Treu T, Galloway A, Wilson PW, Cho K, Gaziano JM, Alexander KP, Gagnon DR, Djousse L, Forman DE, Driver JA. New statin use, mortality, and first cardiovascular events in older US Veterans by frailty status. J Am Geriatr Soc 2024; 72:410-422. [PMID: 38055194 PMCID: PMC10922314 DOI: 10.1111/jgs.18700] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Revised: 10/27/2023] [Accepted: 10/28/2023] [Indexed: 12/07/2023]
Abstract
BACKGROUND Statins are part of long-term medical regimens for many older adults. Whether frailty modifies the protective relationship between statins, mortality, and major adverse cardiovascular events (MACE) is unknown. METHODS This was a retrospective study of US Veterans ≥65, without CVD or prior statin use seen in 2002-2012, followed through 2017. A 31-item frailty index was used. The co-primary endpoint was all-cause mortality or MACE (MI, stroke/TIA, revascularization, or cardiovascular death). Cox proportional hazards models were developed to evaluate the association of statin use with outcomes; propensity score overlap weighting accounted for confounding by indication. RESULTS We identified 710,313 Veterans (mean age (SD) 75.3(6.5), 98% male, 89% white); 86,327 (12.1%) were frail. Over mean follow-up of 8 (5) years, there were 48.6 and 72.6 deaths per 1000 person-years (PY) among non-frail statin-users vs nonusers (weighted Incidence Rate Difference (wIRD)/1000 person years (PY), -24.0[95% CI, -24.5 to -23.6]), and 90.4 and 130.4 deaths per 1000PY among frail statin-users vs nonusers (wIRD/1000PY, -40.0[95% CI, -41.8 to -38.2]). There were 51.7 and 60.8 MACE per 1000PY among non-frail statin-users vs nonusers (wIRD/1000PY, -9.1[95% CI, -9.7 to -8.5]), and 88.2 and 102.0 MACE per 1000PY among frail statin-users vs nonusers (wIRD/1000PY, -13.8[95% CI, -16.2 to -11.4]). There were no significant interactions by frailty for statin users vs non-users by either mortality or MACE outcomes, p-interaction 0.770 and 0.319, respectively. Statin use was associated with lower risk of all-cause mortality (HR, 0.61 (0.60-0.61)) and MACE (HR 0.86 (0.85-0.87)). CONCLUSIONS New statin use is associated with a lower risk of mortality and MACE, independent of frailty. These findings should be confirmed in a randomized clinical trial.
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Affiliation(s)
- Ariela R. Orkaby
- New England GRECC (Geriatric Research, Education, and Clinical Center) VA Boston Healthcare System, 150 S Huntington St, Boston, MA 02130
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, 2 2 Avenue de Lafayette, Boston, MA 02111
- Division of Aging, Brigham & Women’s Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115
| | - Bing Lu
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, 2 2 Avenue de Lafayette, Boston, MA 02111
- Department of Public Health, University of Connecticut Health Center, 263 Farmington Ave, Farmington, CT 06030
| | - Yuk-Lam Ho
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, 2 2 Avenue de Lafayette, Boston, MA 02111
| | - Timothy Treu
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, 2 2 Avenue de Lafayette, Boston, MA 02111
| | - Ashley Galloway
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, 2 2 Avenue de Lafayette, Boston, MA 02111
| | - Peter W.F. Wilson
- Atlanta VA Healthcare System, 1670 Clairmont Road, Decatur, GA 30033
- Emory Clinical Cardiology Research Institute, 1462 Clifton Rd NE, 5 Floor, Atlanta, GA 30322
| | - Kelly Cho
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, 2 2 Avenue de Lafayette, Boston, MA 02111
- Division of Aging, Brigham & Women’s Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115
- Department of Public Health, University of Connecticut Health Center, 263 Farmington Ave, Farmington, CT 06030
| | - J. Michael Gaziano
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, 2 2 Avenue de Lafayette, Boston, MA 02111
- Division of Aging, Brigham & Women’s Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115
- Department of Public Health, University of Connecticut Health Center, 263 Farmington Ave, Farmington, CT 06030
| | - Karen P. Alexander
- Division of Cardiology, Duke University Medical Center, 10 Duke Medicine Cir, Durham, NC 27710
- Duke Clinical Research Institute, Duke University, 200 Morris Street, Durham, NC 27701
| | - David R. Gagnon
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, 2 2 Avenue de Lafayette, Boston, MA 02111
| | - Luc Djousse
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, 2 2 Avenue de Lafayette, Boston, MA 02111
- Division of Aging, Brigham & Women’s Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115
| | - Daniel E. Forman
- Section of Geriatric Cardiology, University of Pittsburgh Medical Center, 3471 Fifth Ave, Ste 500 Pittsburgh, PA 15213
- Geriatric Research, Education, and Clinical Center, VA Pittsburgh Healthcare System, 4100 Allequippa St, Pittsburgh, PA 15240
| | - Jane A. Driver
- New England GRECC (Geriatric Research, Education, and Clinical Center) VA Boston Healthcare System, 150 S Huntington St, Boston, MA 02130
- Division of Aging, Brigham & Women’s Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115
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3
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Ko D, Treu TM, Tarko L, Ho YL, Preis SR, Trinquart L, Gagnon DR, Monahan KM, Helm RH, Orkaby AR, Lubitz SA, Bosch NA, Walkey AJ, Cho K, Wilson PWF, Benjamin EJ. Incidence and prognostic significance of newly-diagnosed atrial fibrillation among older U.S. veterans hospitalized with COVID-19. Sci Rep 2024; 14:952. [PMID: 38200186 PMCID: PMC10781702 DOI: 10.1038/s41598-024-51177-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2023] [Accepted: 01/01/2024] [Indexed: 01/12/2024] Open
Abstract
Most prior studies on the prognostic significance of newly-diagnosed atrial fibrillation (AF) in COVID-19 did not differentiate newly-diagnosed AF from pre-existing AF. To determine the association between newly-diagnosed AF and in-hospital and 30-day mortality among regular users of Veterans Health Administration using data linked to Medicare. We identified Veterans aged ≥ 65 years who were hospitalized for ≥ 24 h with COVID-19 from 06/01/2020 to 1/31/2022 and had ≥ 2 primary care visits within 24 months prior to the index hospitalization. We performed multivariable logistic regression analyses to estimate adjusted risks, risk differences (RD), and odds ratios (OR) for the association between newly-diagnosed AF and the mortality outcomes adjusting for patient demographics, baseline comorbidities, and presence of acute organ dysfunction on admission. Of 23,299 patients in the study cohort, 5.3% had newly-diagnosed AF, and 29.2% had pre-existing AF. In newly-diagnosed AF adjusted in-hospital and 30-day mortality were 16.5% and 22.7%, respectively. Newly-diagnosed AF was associated with increased mortality compared to pre-existing AF (in-hospital: OR 2.02, 95% confidence interval [CI] 1.72-2.37; RD 7.58%, 95% CI 5.54-9.62) (30-day: OR 1.86; 95% CI 1.60-2.16; RD 9.04%, 95% CI 6.61-11.5) or no AF (in-hospital: OR 2.24, 95% CI 1.93-2.60; RD 8.40%, 95% CI 6.44-10.4) (30-day: 2.07, 95% CI 1.80-2.37; RD 10.2%, 95% CI 7.89-12.6). There was a smaller association between pre-existing AF and the mortality outcomes. Newly-diagnosed AF is an important prognostic marker for patients hospitalized with COVID-19. Whether prevention or treatment of AF improves clinical outcomes in these patients remains unknown.
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Affiliation(s)
- Darae Ko
- Section of Cardiovascular Medicine, Boston Medical Center, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, USA.
- Hinda and Arthur Marcus Institute for Aging Research, Hebrew SeniorLife, Harvard Medical School, 1200 Center Street, Boston, MA, 02131, USA.
| | - Timothy M Treu
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA
| | - Laura Tarko
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA
| | - Yuk-Lam Ho
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA
| | - Sarah R Preis
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Ludovic Trinquart
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
- Tufts Clinical and Translational Science Institute, Tufts University, Boston, MA, USA
- Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, MA, USA
| | - David R Gagnon
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Kevin M Monahan
- Section of Cardiovascular Medicine, Boston Medical Center, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, USA
| | - Robert H Helm
- Section of Cardiovascular Medicine, Boston Medical Center, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, USA
| | - Ariela R Orkaby
- Division of Aging, Department of Medicine, Brigham & Women's Hospital, Harvard Medical School, Boston, MA, USA
- New England GRECC (Geriatric Research, Education, and Clinical Center), VA Boston Healthcare System, Boston, MA, USA
| | - Steven A Lubitz
- Cardiac Arrhythmia Service, Massachusetts General Hospital, Boston, MA, USA
| | - Nicholas A Bosch
- Section of Pulmonary, Allergy, Sleep, and Critical Care Medicine, Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Allan J Walkey
- Section of Pulmonary, Allergy, Sleep, and Critical Care Medicine, Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Kelly Cho
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA
- Division of Aging, Department of Medicine, Brigham & Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Peter W F Wilson
- Atlanta VA Medical Center, Decatur, GA, USA
- Division of Cardiology, Department of Medicine, Emory University School of Medicine, Atlanta, GA, USA
| | - Emelia J Benjamin
- Section of Cardiovascular Medicine, Boston Medical Center, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, USA
- Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA
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4
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Barayev O, Hawley CE, Wellman H, Gerlovin H, Hsu W, Paik JM, Mandel EI, Liu CK, Djoussé L, Gaziano JM, Gagnon DR, Orkaby AR. Statins, Mortality, and Major Adverse Cardiovascular Events Among US Veterans With Chronic Kidney Disease. JAMA Netw Open 2023; 6:e2346373. [PMID: 38055276 DOI: 10.1001/jamanetworkopen.2023.46373] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/07/2023] Open
Abstract
Importance There are limited data for the utility of statins for primary prevention of atherosclerotic cardiovascular disease (ASCVD) and death in adults with chronic kidney disease (CKD). Objective To evaluate the association of statin use with all-cause mortality and major adverse cardiovascular events (MACE) among US veterans older than 65 years with CKD stages 3 to 4. Design, Setting, and Participants This cohort study used a target trial emulation design for statin initiation among veterans with moderate CKD (stages 3 or 4) using nested trials with a propensity weighting approach. Linked Veterans Affairs (VA) Healthcare System, Medicare, and Medicaid data were used. This study considered veterans newly diagnosed with moderate CKD between 2005 and 2015 in the VA, with follow-up through December 31, 2017. Veterans were older than 65 years, within 5 years of CKD diagnosis, had no prior ASCVD or statin use, and had at least 1 clinical visit in the year prior to trial baseline. Eligibility criteria were assessed for each nested trial, and Cox proportional hazards models with bootstrapping were run. Analysis was conducted from July 2021 to October 2023. Exposure Statin initiation vs none. Main Outcomes and Measures Primary outcome was all-cause mortality; secondary outcome was time to first MACE (myocardial infarction, transient ischemic attack, stroke, revascularization, or mortality). Results Included in the analysis were 14 828 veterans. Mean (SD) age at CKD diagnosis was 76.9 (8.2) years, 14 616 (99%) were men, 10 539 (72%) White, and 2568 (17%) Black. After expanding to person-trials and assessing eligibility at each baseline, there were 151 243 person-trials (14 685 individuals) of nonstatin initiators and 2924 person-trials (2924 individuals) of statin initiators included. Propensity score adjustment via overlap weighting with nonparametric bootstrapping resulted in covariate balance, with mean (SD) follow-up of 3.6 (2.7) years. The hazard ratio for all-cause mortality was 0.91 (95% CI, 0.85-0.97) comparing statin initiators to noninitiators. The hazard ratio for MACE was 0.96 (95% CI, 0.91-1.02). Results remained consistent in prespecified subgroup analyses. Conclusions and Relevance In this target trial emulation of statin initiation in US veterans older than 65 years with CKD stages 3 to 4 and no prior ASCVD, statin initiation was significantly associated with a lower risk of all-cause mortality but not MACE. Results should be confirmed in a randomized clinical trial.
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Affiliation(s)
- Odeya Barayev
- Ben Gurion University of the Negev, Be'er Sheva, Israel
| | - Chelsea E Hawley
- New England Geriatric Research Education and Clinical Center, Bedford and Boston, Massachusetts
| | - Helen Wellman
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston
| | - Hanna Gerlovin
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston
| | - Whitney Hsu
- VA Boston Healthcare System, Department of Pharmacy, Boston, Massachusetts
| | - Julie M Paik
- New England Geriatric Research Education and Clinical Center, Bedford and Boston, Massachusetts
- Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
- Division of Renal Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
| | - Ernest I Mandel
- Division of Renal Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
| | - Christine K Liu
- Section of Geriatrics, Department of Medicine, Stanford University School of Medicine, Stanford, California
- Geriatric Research Education and Clinical Center, Palo Alto VA Medical Center, Palo Alto, California
| | - Luc Djoussé
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston
- Division of Aging, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - J Michael Gaziano
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston
- Division of Aging, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - David R Gagnon
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts
| | - Ariela R Orkaby
- New England Geriatric Research Education and Clinical Center, Bedford and Boston, Massachusetts
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston
- Division of Aging, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
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5
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Nguyen XT, Ho Y, Li Y, Song RJ, Leung KH, Rahman SU, Orkaby AR, Vassy JL, Gagnon DR, Cho K, Gaziano JM, Wilson PWF. Serum Cholesterol and Impact of Age on Coronary Heart Disease Death in More Than 4 Million Veterans. J Am Heart Assoc 2023; 12:e030496. [PMID: 37889207 PMCID: PMC10727410 DOI: 10.1161/jaha.123.030496] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Accepted: 09/29/2023] [Indexed: 10/28/2023]
Abstract
Background The lipid hypothesis postulates that lower blood cholesterol is associated with reduced coronary heart disease (CHD) risk, which has been challenged by reports of a U-shaped relation between cholesterol and death in recent studies. We sought to examine whether the U-shaped relationship is true and to assess the impact of age on this association. Method and Results We conducted a prospective cohort study of 4 467 942 veterans aged >18 years, with baseline outpatient visits from 2002 to 2007 and follow-up to December 30, 2018, in the Veterans Health Administration electronic health record system. We observed a J-shaped relation between total cholesterol (TC) and CHD mortality after a comprehensive adjustment of confounding factors: flat for TC <180 mg/dL, and greater risk was present at higher cholesterol levels. Compared with veterans with TC between 180 and 199 mg/dL, the multiadjusted hazard ratios (HRs) for CHD death were 1.03 (95% CI, 1.02-1.04), 1.07 (95% CI, 1.06-1.09), 1.15 (95% CI, 1.13-1.18), 1.25 (95% CI, 1.22-1.28), and 1.45 (95% CI, 1.42-1.49) times greater among veterans with TC (mg/dL) of 200 to 219, 220 to 239, 140 to 259, 260 to 279 and ≥280, respectively. Similar J-shaped TC-CHD mortality patterns were observed among veterans with and without statin use at or before baseline. Conclusions The cholesterol paradox, for example, higher CHD death in patients with a low cholesterol level, was a reflection of reverse causality, especially among older participants. Our results support the lipid hypothesis that lower blood cholesterol is associated with reduced CHD. Furthermore, the hypothesis remained true when TC was low due to use of statins or other lipid-lowering medication.
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Affiliation(s)
- Xuan‐Mai T. Nguyen
- MAVERIC VA Boston Healthcare SystemBostonMAUSA
- Carle Illinois College of MedicineUniversity of Illinois Urbana ChampaignChampaignILUSA
| | - Yuk‐Lam Ho
- MAVERIC VA Boston Healthcare SystemBostonMAUSA
| | - Yanping Li
- MAVERIC VA Boston Healthcare SystemBostonMAUSA
| | | | - Kenneth H. Leung
- Carle Illinois College of MedicineUniversity of Illinois Urbana ChampaignChampaignILUSA
| | - Saad Ur Rahman
- Carle Illinois College of MedicineUniversity of Illinois Urbana ChampaignChampaignILUSA
| | - Ariela R. Orkaby
- MAVERIC VA Boston Healthcare SystemBostonMAUSA
- Division on Aging, Department of MedicineBrigham and Women’s HospitalBostonMAUSA
- Department of MedicineHarvard Medical SchoolBostonMAUSA
| | - Jason L. Vassy
- MAVERIC VA Boston Healthcare SystemBostonMAUSA
- Division of General Internal MedicineBrigham and Women’s HospitalBostonMAUSA
| | - David R. Gagnon
- MAVERIC VA Boston Healthcare SystemBostonMAUSA
- Boston University School of Public HealthBostonMAUSA
| | - Kelly Cho
- MAVERIC VA Boston Healthcare SystemBostonMAUSA
- Division on Aging, Department of MedicineBrigham and Women’s HospitalBostonMAUSA
- Department of MedicineHarvard Medical SchoolBostonMAUSA
| | - J. Michael Gaziano
- MAVERIC VA Boston Healthcare SystemBostonMAUSA
- Division on Aging, Department of MedicineBrigham and Women’s HospitalBostonMAUSA
- Department of MedicineHarvard Medical SchoolBostonMAUSA
| | - Peter W. F. Wilson
- Atlanta VA Medical CenterDecaturGAUSA
- Emory University Schools of Medicine and Public HealthAtlantaGAUSA
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6
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Guardino ET, Tarko L, Wilson PWF, Gaziano JM, Cho K, Gagnon DR, Orkaby AR. Predictive value of ASCVD risk score for mortality and major adverse cardiovascular events in the year following a COVID-19 infection among US Veterans. Int J Cardiol 2023; 387:131120. [PMID: 37330018 PMCID: PMC10270727 DOI: 10.1016/j.ijcard.2023.131120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Accepted: 06/13/2023] [Indexed: 06/19/2023]
Abstract
BACKGROUND Morbidity and mortality following COVID-19 infection may be influenced by baseline atherosclerotic cardiovascular disease (ASCVD) risk, yet limited data are available to identify those at highest risk. We examined the association between baseline ASCVD risk with mortality and major adverse cardiovascular events (MACE) in the year following COVID-19 infection. METHODS We evaluated a nationwide retrospective cohort of US Veterans free of ASCVD who were tested for COVID-19. The primary outcome was absolute risk of all-cause mortality in the year following a COVID-19 test among those hospitalized vs. not stratified by baseline VA-ASCVD risk scores. Secondarily, risk of MACE was examined. RESULTS There were 393,683 Veterans tested for COVID-19 and 72,840 tested positive. Mean age was 57 years, 86% were male, and 68% were white. Within 30 days following infection, hospitalized Veterans with VA-ASCVD scores >20% had an absolute risk of death of 24.6% vs. 9.7% (P ≤0.0001) for those who tested positive and negative for COVID-19 respectively. In the year following infection, risk of mortality attenuated with no difference in risk after 60 days. The absolute risk of MACE was similar for Veterans who tested positive or negative for COVID-19. CONCLUSIONS Veterans without clinical ASCVD experienced an increased absolute risk of death within 30 days of a COVID-19 infection compared to Veterans with the same VA-ASCVD risk score who tested negative, but this risk attenuated after 60 days. Whether cardiovascular preventive medications can lower the risk of mortality and MACE in the acute period following COVID-19 infection should be evaluated.
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Affiliation(s)
- Eric T Guardino
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, 2 2 Avenue de Lafayette, Boston, MA 02111, USA; Division of Aging, Brigham & Women's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115, USA.
| | - Laura Tarko
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, 2 2 Avenue de Lafayette, Boston, MA 02111, USA
| | - Peter W F Wilson
- Atlanta VA Healthcare System, 1670 Clairmont Road, Decatur, GA 30033, USA; Emory Clinical Cardiology Research Institute, 1462 Clifton Rd NE, 5(th) Floor, Atlanta, GA 30322, USA
| | - J Michael Gaziano
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, 2 2 Avenue de Lafayette, Boston, MA 02111, USA; Division of Aging, Brigham & Women's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115, USA
| | - Kelly Cho
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, 2 2 Avenue de Lafayette, Boston, MA 02111, USA
| | - David R Gagnon
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, 2 2 Avenue de Lafayette, Boston, MA 02111, USA; Boston University School of Public Health, Department of Biostatistics, Boston, MA 02118, USA
| | - Ariela R Orkaby
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, 2 2 Avenue de Lafayette, Boston, MA 02111, USA; Division of Aging, Brigham & Women's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115, USA; New England GRECC (Geriatric Research, Education, and Clinical Center) VA Boston Healthcare System, 150 S Huntington St, Boston, MA 02130, USA
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7
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Vassy JL, Posner DC, Ho YL, Gagnon DR, Galloway A, Tanukonda V, Houghton SC, Madduri RK, McMahon BH, Tsao PS, Damrauer SM, O’Donnell CJ, Assimes TL, Casas JP, Gaziano JM, Pencina MJ, Sun YV, Cho K, Wilson PW. Cardiovascular Disease Risk Assessment Using Traditional Risk Factors and Polygenic Risk Scores in the Million Veteran Program. JAMA Cardiol 2023; 8:564-574. [PMID: 37133828 PMCID: PMC10157509 DOI: 10.1001/jamacardio.2023.0857] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Accepted: 03/09/2023] [Indexed: 05/04/2023]
Abstract
Importance Primary prevention of atherosclerotic cardiovascular disease (ASCVD) relies on risk stratification. Genome-wide polygenic risk scores (PRSs) are proposed to improve ASCVD risk estimation. Objective To determine whether genome-wide PRSs for coronary artery disease (CAD) and acute ischemic stroke improve ASCVD risk estimation with traditional clinical risk factors in an ancestrally diverse midlife population. Design, Setting, and Participants This was a prognostic analysis of incident events in a retrospectively defined longitudinal cohort conducted from January 1, 2011, to December 31, 2018. Included in the study were adults free of ASCVD and statin naive at baseline from the Million Veteran Program (MVP), a mega biobank with genetic, survey, and electronic health record data from a large US health care system. Data were analyzed from March 15, 2021, to January 5, 2023. Exposures PRSs for CAD and ischemic stroke derived from cohorts of largely European descent and risk factors, including age, sex, systolic blood pressure, total cholesterol, high-density lipoprotein (HDL) cholesterol, smoking, and diabetes status. Main Outcomes and Measures Incident nonfatal myocardial infarction (MI), ischemic stroke, ASCVD death, and composite ASCVD events. Results A total of 79 151 participants (mean [SD] age, 57.8 [13.7] years; 68 503 male [86.5%]) were included in the study. The cohort included participants from the following harmonized genetic ancestry and race and ethnicity categories: 18 505 non-Hispanic Black (23.4%), 6785 Hispanic (8.6%), and 53 861 non-Hispanic White (68.0%) with a median (5th-95th percentile) follow-up of 4.3 (0.7-6.9) years. From 2011 to 2018, 3186 MIs (4.0%), 1933 ischemic strokes (2.4%), 867 ASCVD deaths (1.1%), and 5485 composite ASCVD events (6.9%) were observed. CAD PRS was associated with incident MI in non-Hispanic Black (hazard ratio [HR], 1.10; 95% CI, 1.02-1.19), Hispanic (HR, 1.26; 95% CI, 1.09-1.46), and non-Hispanic White (HR, 1.23; 95% CI, 1.18-1.29) participants. Stroke PRS was associated with incident stroke in non-Hispanic White participants (HR, 1.15; 95% CI, 1.08-1.21). A combined CAD plus stroke PRS was associated with ASCVD deaths among non-Hispanic Black (HR, 1.19; 95% CI, 1.03-1.17) and non-Hispanic (HR, 1.11; 95% CI, 1.03-1.21) participants. The combined PRS was also associated with composite ASCVD across all ancestry groups but greater among non-Hispanic White (HR, 1.20; 95% CI, 1.16-1.24) than non-Hispanic Black (HR, 1.11; 95% CI, 1.05-1.17) and Hispanic (HR, 1.12; 95% CI, 1.00-1.25) participants. Net reclassification improvement from adding PRS to a traditional risk model was modest for the intermediate risk group for composite CVD among men (5-year risk >3.75%, 0.38%; 95% CI, 0.07%-0.68%), among women, (6.79%; 95% CI, 3.01%-10.58%), for age older than 55 years (0.25%; 95% CI, 0.03%-0.47%), and for ages 40 to 55 years (1.61%; 95% CI, -0.07% to 3.30%). Conclusions and Relevance Study results suggest that PRSs derived predominantly in European samples were statistically significantly associated with ASCVD in the multiancestry midlife and older-age MVP cohort. Overall, modest improvement in discrimination metrics were observed with addition of PRSs to traditional risk factors with greater magnitude in women and younger age groups.
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Affiliation(s)
- Jason L. Vassy
- Veterans Affairs Boston Healthcare System, Boston, Massachusetts
- Department of Medicine, Brigham & Women’s Hospital and Harvard Medical School, Boston, Massachusetts
| | - Daniel C. Posner
- Veterans Affairs Boston Healthcare System, Boston, Massachusetts
| | - Yuk-Lam Ho
- Veterans Affairs Boston Healthcare System, Boston, Massachusetts
| | - David R. Gagnon
- Veterans Affairs Boston Healthcare System, Boston, Massachusetts
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts
| | - Ashley Galloway
- Veterans Affairs Boston Healthcare System, Boston, Massachusetts
| | | | | | - Ravi K. Madduri
- Data Science and Learning Division, Argonne National Laboratory, Lemont, Illinois
- University of Chicago Consortium for Advanced Science and Engineering, The University of Chicago, Chicago, Illinois
| | - Benjamin H. McMahon
- Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, New Mexico
| | - Philip S. Tsao
- Palo Alto VA Healthcare System, Palo Alto, California
- Stanford Cardiovascular Institute, Stanford University, Stanford, California
| | - Scott M. Damrauer
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, Pennsylvania
- Department of Surgery, University of Pennsylvania Perelman School of Medicine, Philadelphia
| | | | - Themistocles L. Assimes
- Palo Alto VA Healthcare System, Palo Alto, California
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, California
- Stanford Cardiovascular Institute, Stanford University, Stanford, California
| | - Juan P. Casas
- Veterans Affairs Boston Healthcare System, Boston, Massachusetts
- Department of Medicine, Brigham & Women’s Hospital and Harvard Medical School, Boston, Massachusetts
| | - J. Michael Gaziano
- Veterans Affairs Boston Healthcare System, Boston, Massachusetts
- Division of Aging, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
| | - Michael J. Pencina
- Department of Biostatistics, Duke University Medical Center, Durham, North Carolina
| | - Yan V. Sun
- Veterans Affairs Atlanta Healthcare System, Decatur, Georgia
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, Georgia
| | - Kelly Cho
- Veterans Affairs Boston Healthcare System, Boston, Massachusetts
- Department of Medicine, Brigham & Women’s Hospital and Harvard Medical School, Boston, Massachusetts
| | - Peter W.F. Wilson
- Veterans Affairs Atlanta Healthcare System, Decatur, Georgia
- Division of Cardiology, Emory University School of Medicine, Atlanta, Georgia
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, Georgia
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8
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Quach LT, Pedersen MM, Ogawa E, Ward RE, Gagnon DR, Spiro A, Burr JA, Driver JA, Gaziano M, Dhand A, Bean JF. Mild Neurocognitive Disorder, Social Engagement, and Falls Among Older Primary Care Patients. Arch Phys Med Rehabil 2023; 104:541-546. [PMID: 36513122 PMCID: PMC10073260 DOI: 10.1016/j.apmr.2022.10.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 10/18/2022] [Accepted: 10/22/2022] [Indexed: 12/14/2022]
Abstract
OBJECTIVES (1) To estimate the association between social engagement (SE) and falls; (2) To examine the relation between mild neurocognitive disorder (MNCD) and falls by different levels of SE. DESIGN We performed a secondary data analysis using prospective cohort study design. SETTING Primary care. PARTICIPANTS A total of 425 older adult primary care patients at risk for mobility decline (N=425). As previously reported, at baseline, 42% of participants exhibit MNCD. MAIN OUTCOME MEASURES The outcome variable was the number of falls during 2 years of follow-up. Exposure variables at baseline included (1) MNCD identified using a cut-off of 1.5 SD below the age-adjusted mean on at least 2 measures within a cognitive performance battery and (2) SE, which was assessed using the social component of the Late-Life Function and Disability Instrument. High SE was defined as having a score ≥ median value (≥49 out of 100). All models were adjusted for age, sex, education, marital status, comorbidities, and pain status. RESULTS Over 2 years of follow-up, 48% of participants fell at least once. MNCD was associated with a higher rate of falls, adjusting for the covariates (Incidence Rate Ratio=1.6, 95% confidence interval: 1.1-2.3). There was no significant association between MNCD and the rate of falls among people with high SE. In participants with low SE (having a score less than 49.5 out 100), MNCD was associated with a higher rate of falls as compared with participants with no neurocognitive disorder (No-NCD). CONCLUSIONS Among participants with low SE, MNCD was associated with a higher rate of falls, but not among participants with high SE. The findings suggest that high SE may be protective against falls among older primary care patients with MNCD.
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Affiliation(s)
- Lien T Quach
- Massachusetts Veterans Epidemiology and Research Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA; Department of Gerontology, University of Massachusetts Boston, Boston, MA; Medical Practice Evaluation Center and Center for Aging and Serious Illness, Mongan Institute, Department of Medicine, Massachusetts General Hospital, Boston, MA.
| | - Mette M Pedersen
- Department of Clinical Research, Copenhagen University Hospital, Hvidovre, Denmark; Department of Clinical Medicine, University of Copenhagen, Denmark
| | - Elisa Ogawa
- New England Geriatric Research Education and Clinical Center, VA Boston Healthcare System, Boston, MA; Harvard Medical School, Boston, MA
| | - Rachel E Ward
- Massachusetts Veterans Epidemiology and Research Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA; New England Geriatric Research Education and Clinical Center, VA Boston Healthcare System, Boston, MA; Harvard Medical School, Boston, MA
| | - David R Gagnon
- Department of Gerontology, University of Massachusetts Boston, Boston, MA; Boston University, Boston, MA
| | - Avron Spiro
- Massachusetts Veterans Epidemiology and Research Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA; Boston University, Boston, MA
| | - Jeffrey A Burr
- Department of Gerontology, University of Massachusetts Boston, Boston, MA
| | - Jane A Driver
- New England Geriatric Research Education and Clinical Center, VA Boston Healthcare System, Boston, MA; Harvard Medical School, Boston, MA; Brigham and Women's Hospital, Boston, MA; Geriatrics and Extended Care, VA Boston Healthcare System, Boston, MA
| | - Michael Gaziano
- Massachusetts Veterans Epidemiology and Research Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA; Harvard Medical School, Boston, MA; Brigham and Women's Hospital, Boston, MA
| | - Amar Dhand
- Harvard Medical School, Boston, MA; Brigham and Women's Hospital, Boston, MA
| | - Jonathan F Bean
- New England Geriatric Research Education and Clinical Center, VA Boston Healthcare System, Boston, MA; Harvard Medical School, Boston, MA; Spaulding Rehabilitation Hospital, Boston, MA
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9
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Honerlaw J, Ho YL, Fontin F, Gosian J, Maripuri M, Murray M, Sangar R, Galloway A, Zimolzak AJ, Whitbourne SB, Casas JP, Ramoni RB, Gagnon DR, Cai T, Liao KP, Gaziano JM, Muralidhar S, Cho K. Framework of the Centralized Interactive Phenomics Resource (CIPHER) standard for electronic health data-based phenomics knowledgebase. J Am Med Inform Assoc 2023; 30:958-964. [PMID: 36882092 PMCID: PMC10114031 DOI: 10.1093/jamia/ocad030] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 02/14/2023] [Accepted: 02/23/2023] [Indexed: 03/09/2023] Open
Abstract
The development of phenotypes using electronic health records is a resource-intensive process. Therefore, the cataloging of phenotype algorithm metadata for reuse is critical to accelerate clinical research. The Department of Veterans Affairs (VA) has developed a standard for phenotype metadata collection which is currently used in the VA phenomics knowledgebase library, CIPHER (Centralized Interactive Phenomics Resource), to capture over 5000 phenotypes. The CIPHER standard improves upon existing phenotype library metadata collection by capturing the context of algorithm development, phenotyping method used, and approach to validation. While the standard was iteratively developed with VA phenomics experts, it is applicable to the capture of phenotypes across healthcare systems. We describe the framework of the CIPHER standard for phenotype metadata collection, the rationale for its development, and its current application to the largest healthcare system in the United States.
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Affiliation(s)
- Jacqueline Honerlaw
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts, USA
| | - Yuk-Lam Ho
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts, USA
| | - Francesca Fontin
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts, USA
| | - Jeffrey Gosian
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts, USA
| | - Monika Maripuri
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts, USA
| | - Michael Murray
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts, USA
| | - Rahul Sangar
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts, USA
| | - Ashley Galloway
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts, USA
| | - Andrew J Zimolzak
- Center for Innovations in Quality, Effectiveness and Safety, Michael E. DeBakey VA Medical Center, Houston, Texas, USA.,Department of Medicine, Baylor College of Medicine, Houston, Texas, USA
| | - Stacey B Whitbourne
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts, USA.,Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA.,Division of Aging, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Juan P Casas
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts, USA.,Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA.,Division of Aging, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Rachel B Ramoni
- Office of Research and Development, Veterans Health Administration, Washington, District of Columbia, USA
| | - David R Gagnon
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts, USA.,Department of Biostatistics, School of Public Health, Boston University, Boston, Massachusetts, USA
| | - Tianxi Cai
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts, USA.,Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA.,Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | - Katherine P Liao
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts, USA.,Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA.,Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA.,Division of Rheumatology, Inflammation, and Immunity, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - J Michael Gaziano
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts, USA.,Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA.,Division of Aging, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Sumitra Muralidhar
- Office of Research and Development, Veterans Health Administration, Washington, District of Columbia, USA
| | - Kelly Cho
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts, USA.,Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA.,Division of Aging, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
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10
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Polonsky TS, Posner DC, Ho YL, Serra MC, Houghton SC, Ivey KL, Li Y, Gagnon DR, Djousse L, Cho KM, Wilson PW. Abstract P271: Spatiotemporal Trends of Cardiovascular Incidence in U.S. Veterans From 2003-2018. Circulation 2023. [DOI: 10.1161/circ.147.suppl_1.p271] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/18/2023]
Abstract
Introduction:
Cardiovascular disease (CVD) is the leading cause of mortality and disability in the United States. Spatiotemporal modeling of disease incidence may guide prevention efforts toward regions most at risk in the future.
Methods:
We examined spatiotemporal trends in composite CVD events, defined as myocardial infarction, ischemic stroke, atrial fibrillation, heart failure, coronary artery disease, or cardiovascular death, using electronic health records from 9,718,107 U.S. Veterans who used VA healthcare facilities from 2003-2018. Age-standardized annual incidence by state was modeled using the Bernardinelli model, a Bayesian Poisson regression with linear temporal trends for the US and individual states.
Results:
There were 626,271 CVD events over 16 years of follow up and annual incidence fell gradually from 11.5% in 2003 to 6.6% in 2018. Incidence among all US veterans decreased an estimated 3.1% [95% CI: -3.3%, -2.9%] per year (RR
year
= 0.969). State-level incidences also decreased monotonically over the follow-up period (Figure panel
A
), ranging from an absolute decrease 5.0% (RI) to 1.7% (OR). Estimated incidence for the five states most above (RI, FL, NC, MT, LA) and most below (OR, NV, CT, OK, MI) the national trend are plotted against the US average in Figure panel
B
. These outlying states differed significantly from the overall trend (posterior probabilities ≥ 99.7%), and the magnitude of trend was loosely correlated with initial incidence rate (in 2003). Notably, four states crossed the national average over the study period: CT and OK started below average in 2003 but ended above in 2018, whereas RI and NC started with high incidence and ended with below average incidence in 2018.
Conclusions:
Among US veterans, there was significant geographic variation in the rate of decline of incident CVD from 2003-2018. Future research will investigate the mechanisms underpinning the observed trends, particularly prevalence and control of risk factors.
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11
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Kinlay S, Young MM, Sherrod R, Gagnon DR. Long-Term Outcomes and Duration of Dual Antiplatelet Therapy After Coronary Intervention With Second-Generation Drug-Eluting Stents: The Veterans Affairs Extended DAPT Study. J Am Heart Assoc 2023; 12:e027055. [PMID: 36645075 PMCID: PMC9939065 DOI: 10.1161/jaha.122.027055] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
Background Recent guidelines on dual antiplatelet therapy (DAPT) duration after percutaneous coronary intervention (PCI) balance the subsequent risks of major bleeding with ischemic events. Although generally favoring shorter DAPT duration with second-generation drug-eluting stents, the effects on long-term outcomes in the wider population are uncertain. Methods and Results We tracked all patients having PCI with second-generation drug-eluting stents in the Veterans Affairs Healthcare System between 2006 and 2016 for death, myocardial infarction, stroke, and major bleeding up to 13 years. We compared these outcomes with 4 DAPT durations of 1 to 5, 6 to 9, 10 to 12, and 13 to 18 months after the index PCI using hazard ratios (HRs) and 95% CIs from Cox proportional hazards models adjusted by inverse probability weighting. A total of 40 882 subjects with PCI were followed up for a median of 4.3 (25%-75%: 2.4-6.5) years. DAPT discontinuation was rare early after PCI (5.8% at 1-5 months and 6.3% at 6-9 months) but increased (19% and 44%) >9 months. The risk of cardiovascular and noncardiovascular death was higher (HR, 2.03-3.41) with DAPT discontinuation <9 months, likely reflecting premature cessation from factors related to early death. DAPT discontinuation after 9 months following PCI was associated with lower risks of death (HR, 0.93 [95% CI, 0.88-0.99]), cardiac death (HR, 0.79 [95% CI, 0.70-0.90]), myocardial infarction (HR, 0.75 [95% CI, 0.69-0.82]), and major bleeding (HR, 0.82 [95% CI, 0.74-0.91]). Results were similar with an index PCI for an acute coronary syndrome. Conclusions Stopping DAPT after 9 months is associated with lower long-term risks of adverse ischemic and bleeding events and supports recent guidelines of shorter duration DAPT after PCI with second-generation drug-eluting stents.
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Affiliation(s)
- Scott Kinlay
- Veterans Affairs Boston Healthcare SystemWest RoxburyMA,Harvard Medical SchoolBostonMA,Department of Biostatistics, Massachusetts Veterans Epidemiology Research & Information Center (MAVERIC)VA Boston Healthcare SystemBostonMA,Brigham and Women’s HospitalBostonMA,Boston University Medical SchoolBostonMA
| | - Melissa M. Young
- Veterans Affairs Boston Healthcare SystemWest RoxburyMA,Department of Biostatistics, Massachusetts Veterans Epidemiology Research & Information Center (MAVERIC)VA Boston Healthcare SystemBostonMA
| | | | - David R. Gagnon
- Veterans Affairs Boston Healthcare SystemWest RoxburyMA,Department of Biostatistics, Massachusetts Veterans Epidemiology Research & Information Center (MAVERIC)VA Boston Healthcare SystemBostonMA,Boston University School of Public HealthBostonMA
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12
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Dickerman BA, Gerlovin H, Madenci AL, Figueroa Muñiz MJ, Wise JK, Adhikari N, Ferolito BR, Kurgansky KE, Gagnon DR, Cho K, Casas JP, Hernán MA. Comparative effectiveness of third doses of mRNA-based COVID-19 vaccines in US veterans. Nat Microbiol 2023; 8:55-63. [PMID: 36593297 PMCID: PMC9949349 DOI: 10.1038/s41564-022-01272-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Accepted: 10/17/2022] [Indexed: 01/03/2023]
Abstract
Vaccination against SARS-CoV-2 has been effective in reducing the burden of severe disease and death from COVID-19. Third doses of mRNA-based vaccines have provided a way to address waning immunity and broaden protection against emerging SARS-CoV-2 variants. However, their comparative effectiveness for a range of COVID-19 outcomes across diverse populations is unknown. We emulated a target trial using electronic health records of US veterans who received a third dose of either BNT162b2 or mRNA-1273 vaccines between 20 October 2021 and 8 February 2022, during a period that included Delta- and Omicron-variant waves. Eligible veterans had previously completed an mRNA vaccine primary series. We matched recipients of each vaccine in a 1:1 ratio according to recorded risk factors. Each vaccine group included 65,196 persons. The excess number of events over 16 weeks per 10,000 persons for BNT162b2 compared with mRNA-1273 was 45.4 (95% CI: 19.4, 84.7) for documented infection, 3.7 (2.2, 14.1) for symptomatic COVID-19, 10.6 (5.1, 19.7) for COVID-19 hospitalization, 2.0 (-3.1, 6.3) for COVID-19 intensive care unit admission and 0.2 (-2.2, 4.0) for COVID-19 death. After emulating a second target trial of veterans who received a third dose between 1 January and 1 March 2022, during a period restricted to Omicron-variant predominance, the excess number of events over 9 weeks per 10,000 persons for BNT162b2 compared with mRNA-1273 was 63.2 (95% CI: 15.2, 100.7) for documented infection. The 16-week risks of COVID-19 outcomes were low after a third dose of mRNA-1273 or BNT162b2, although risks were lower with mRNA-1273 than with BNT162b2, particularly for documented infection.
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Affiliation(s)
- Barbra A Dickerman
- CAUSALab, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Hanna Gerlovin
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA.
| | - Arin L Madenci
- CAUSALab, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Surgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Michael J Figueroa Muñiz
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Jessica K Wise
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA
| | - Nimish Adhikari
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Brian R Ferolito
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA
| | - Katherine E Kurgansky
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - David R Gagnon
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Kelly Cho
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA
- Division of Aging, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Juan P Casas
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA
- Division of Aging, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Miguel A Hernán
- CAUSALab, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
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13
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Gaziano L, Sun L, Arnold M, Bell S, Cho K, Kaptoge SK, Song RJ, Burgess S, Posner DC, Mosconi K, Robinson-Cohen C, Mason AM, Bolton TR, Tao R, Allara E, Schubert P, Chen L, Staley JR, Staplin N, Altay S, Amiano P, Arndt V, Ärnlöv J, Barr EL, Björkelund C, Boer JM, Brenner H, Casiglia E, Chiodini P, Cooper JA, Coresh J, Cushman M, Dankner R, Davidson KW, de Jongh RT, Donfrancesco C, Engström G, Freisling H, de la Cámara AG, Gudnason V, Hankey GJ, Hansson PO, Heath AK, Hoorn EJ, Imano H, Jassal SK, Kaaks R, Katzke V, Kauhanen J, Kiechl S, Koenig W, Kronmal RA, Kyrø C, Lawlor DA, Ljungberg B, MacDonald C, Masala G, Meisinger C, Melander O, Moreno Iribas C, Ninomiya T, Nitsch D, Nordestgaard BG, Onland-Moret C, Palmieri L, Petrova D, Garcia JRQ, Rosengren A, Sacerdote C, Sakurai M, Santiuste C, Schulze MB, Sieri S, Sundström J, Tikhonoff V, Tjønneland A, Tong T, Tumino R, Tzoulaki I, van der Schouw YT, Monique Verschuren W, Völzke H, Wallace RB, Wannamethee SG, Weiderpass E, Willeit P, Woodward M, Yamagishi K, Zamora-Ros R, Akwo EA, Pyarajan S, Gagnon DR, Tsao PS, Muralidhar S, Edwards TL, Damrauer SM, Joseph J, Pennells L, Wilson PW, Harrison S, Gaziano TA, Inouye M, Baigent C, Casas JP, Langenberg C, Wareham N, Riboli E, Gaziano J, Danesh J, Hung AM, Butterworth AS, Wood AM, Di Angelantonio E. Mild-to-Moderate Kidney Dysfunction and Cardiovascular Disease: Observational and Mendelian Randomization Analyses. Circulation 2022; 146:1507-1517. [PMID: 36314129 PMCID: PMC9662821 DOI: 10.1161/circulationaha.122.060700] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Accepted: 08/18/2022] [Indexed: 01/28/2023]
Abstract
BACKGROUND End-stage renal disease is associated with a high risk of cardiovascular events. It is unknown, however, whether mild-to-moderate kidney dysfunction is causally related to coronary heart disease (CHD) and stroke. METHODS Observational analyses were conducted using individual-level data from 4 population data sources (Emerging Risk Factors Collaboration, EPIC-CVD [European Prospective Investigation into Cancer and Nutrition-Cardiovascular Disease Study], Million Veteran Program, and UK Biobank), comprising 648 135 participants with no history of cardiovascular disease or diabetes at baseline, yielding 42 858 and 15 693 incident CHD and stroke events, respectively, during 6.8 million person-years of follow-up. Using a genetic risk score of 218 variants for estimated glomerular filtration rate (eGFR), we conducted Mendelian randomization analyses involving 413 718 participants (25 917 CHD and 8622 strokes) in EPIC-CVD, Million Veteran Program, and UK Biobank. RESULTS There were U-shaped observational associations of creatinine-based eGFR with CHD and stroke, with higher risk in participants with eGFR values <60 or >105 mL·min-1·1.73 m-2, compared with those with eGFR between 60 and 105 mL·min-1·1.73 m-2. Mendelian randomization analyses for CHD showed an association among participants with eGFR <60 mL·min-1·1.73 m-2, with a 14% (95% CI, 3%-27%) higher CHD risk per 5 mL·min-1·1.73 m-2 lower genetically predicted eGFR, but not for those with eGFR >105 mL·min-1·1.73 m-2. Results were not materially different after adjustment for factors associated with the eGFR genetic risk score, such as lipoprotein(a), triglycerides, hemoglobin A1c, and blood pressure. Mendelian randomization results for stroke were nonsignificant but broadly similar to those for CHD. CONCLUSIONS In people without manifest cardiovascular disease or diabetes, mild-to-moderate kidney dysfunction is causally related to risk of CHD, highlighting the potential value of preventive approaches that preserve and modulate kidney function.
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Affiliation(s)
- Liam Gaziano
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA (L.G., K.C., R.J.S., D.C.P., P.S., J.J., J.P.C., J.M.G.)
- BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care (L.G., L.S., S. Bell, S.K.K., S. Burgess, K.M., A.M.M., T.R.B., E.A., L.C., J.R.S., P.W., L. Pennells, S.H., M.I., J.D., A.S.B., A.M.W., E.D.A.)
- Heart and Lung Research Institute, University of Cambridge, Cambridge UK (L.G., S. Bell, S.K.K., S. Burgess, K.M., A.M.M., E.A., L. Pennells, M.I., J.D., A.S.B., A.M.W., E.D.A.)
| | - Luanluan Sun
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA (L.G., K.C., R.J.S., D.C.P., P.S., J.J., J.P.C., J.M.G.)
- BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care (L.G., L.S., S. Bell, S.K.K., S. Burgess, K.M., A.M.M., T.R.B., E.A., L.C., J.R.S., P.W., L. Pennells, S.H., M.I., J.D., A.S.B., A.M.W., E.D.A.)
| | | | - Steven Bell
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA (L.G., K.C., R.J.S., D.C.P., P.S., J.J., J.P.C., J.M.G.)
- BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care (L.G., L.S., S. Bell, S.K.K., S. Burgess, K.M., A.M.M., T.R.B., E.A., L.C., J.R.S., P.W., L. Pennells, S.H., M.I., J.D., A.S.B., A.M.W., E.D.A.)
- Heart and Lung Research Institute, University of Cambridge, Cambridge UK (L.G., S. Bell, S.K.K., S. Burgess, K.M., A.M.M., E.A., L. Pennells, M.I., J.D., A.S.B., A.M.W., E.D.A.)
- Stroke Research Group, Department of Clinical Neurosciences (S. Bell), University of Cambridge, UK
- NIHR Blood and Transplant Research Unit in Donor Health and Behaviour (S. Bell, T.R.B., E.A., J.D., A.S.B., A.M.W., E.D.A.), University of Cambridge, UK
| | - Kelly Cho
- Division of Aging (K.C., S.P., J.P.C. J.M.G.), Brigham and Women’s Hospital, Harvard Medical School, Boston, MA
| | - Stephen K. Kaptoge
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA (L.G., K.C., R.J.S., D.C.P., P.S., J.J., J.P.C., J.M.G.)
- BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care (L.G., L.S., S. Bell, S.K.K., S. Burgess, K.M., A.M.M., T.R.B., E.A., L.C., J.R.S., P.W., L. Pennells, S.H., M.I., J.D., A.S.B., A.M.W., E.D.A.)
- Heart and Lung Research Institute, University of Cambridge, Cambridge UK (L.G., S. Bell, S.K.K., S. Burgess, K.M., A.M.M., E.A., L. Pennells, M.I., J.D., A.S.B., A.M.W., E.D.A.)
| | - Rebecca J. Song
- Department of Epidemiology, Boston University School of Public Health, MA (R.J.S.)
| | - Stephen Burgess
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA (L.G., K.C., R.J.S., D.C.P., P.S., J.J., J.P.C., J.M.G.)
- BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care (L.G., L.S., S. Bell, S.K.K., S. Burgess, K.M., A.M.M., T.R.B., E.A., L.C., J.R.S., P.W., L. Pennells, S.H., M.I., J.D., A.S.B., A.M.W., E.D.A.)
- BHF Centre of Research Excellence, School of Clinical Medicine, Addenbrooke’s Hospital (A.M.M., S. Burgess, J.D., A.M.W., A.S.B., E.D.A.)
- Heart and Lung Research Institute, University of Cambridge, Cambridge UK (L.G., S. Bell, S.K.K., S. Burgess, K.M., A.M.M., E.A., L. Pennells, M.I., J.D., A.S.B., A.M.W., E.D.A.)
- Medical Research Council Biostatistics Unit (A.M.M., S. Burgess), University of Cambridge, UK
| | - Daniel C. Posner
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA (L.G., K.C., R.J.S., D.C.P., P.S., J.J., J.P.C., J.M.G.)
| | - Katja Mosconi
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA (L.G., K.C., R.J.S., D.C.P., P.S., J.J., J.P.C., J.M.G.)
- BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care (L.G., L.S., S. Bell, S.K.K., S. Burgess, K.M., A.M.M., T.R.B., E.A., L.C., J.R.S., P.W., L. Pennells, S.H., M.I., J.D., A.S.B., A.M.W., E.D.A.)
- Heart and Lung Research Institute, University of Cambridge, Cambridge UK (L.G., S. Bell, S.K.K., S. Burgess, K.M., A.M.M., E.A., L. Pennells, M.I., J.D., A.S.B., A.M.W., E.D.A.)
| | - Cassianne Robinson-Cohen
- Division of Nephrology, Department of Medicine (C.R.-C., E.A.A.), Vanderbilt University Medical Center, Nashville, TN
| | - Amy M. Mason
- BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care (L.G., L.S., S. Bell, S.K.K., S. Burgess, K.M., A.M.M., T.R.B., E.A., L.C., J.R.S., P.W., L. Pennells, S.H., M.I., J.D., A.S.B., A.M.W., E.D.A.)
- BHF Centre of Research Excellence, School of Clinical Medicine, Addenbrooke’s Hospital (A.M.M., S. Burgess, J.D., A.M.W., A.S.B., E.D.A.)
- Heart and Lung Research Institute, University of Cambridge, Cambridge UK (L.G., S. Bell, S.K.K., S. Burgess, K.M., A.M.M., E.A., L. Pennells, M.I., J.D., A.S.B., A.M.W., E.D.A.)
- Medical Research Council Biostatistics Unit (A.M.M., S. Burgess), University of Cambridge, UK
| | - Thomas R. Bolton
- BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care (L.G., L.S., S. Bell, S.K.K., S. Burgess, K.M., A.M.M., T.R.B., E.A., L.C., J.R.S., P.W., L. Pennells, S.H., M.I., J.D., A.S.B., A.M.W., E.D.A.)
- NIHR Blood and Transplant Research Unit in Donor Health and Behaviour (S. Bell, T.R.B., E.A., J.D., A.S.B., A.M.W., E.D.A.), University of Cambridge, UK
| | - Ran Tao
- Department of Biostatistics (R. Tao), Vanderbilt University Medical Center, Nashville, TN
| | - Elias Allara
- BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care (L.G., L.S., S. Bell, S.K.K., S. Burgess, K.M., A.M.M., T.R.B., E.A., L.C., J.R.S., P.W., L. Pennells, S.H., M.I., J.D., A.S.B., A.M.W., E.D.A.)
- Heart and Lung Research Institute, University of Cambridge, Cambridge UK (L.G., S. Bell, S.K.K., S. Burgess, K.M., A.M.M., E.A., L. Pennells, M.I., J.D., A.S.B., A.M.W., E.D.A.)
- NIHR Blood and Transplant Research Unit in Donor Health and Behaviour (S. Bell, T.R.B., E.A., J.D., A.S.B., A.M.W., E.D.A.), University of Cambridge, UK
| | - Petra Schubert
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA (L.G., K.C., R.J.S., D.C.P., P.S., J.J., J.P.C., J.M.G.)
| | - Lingyan Chen
- BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care (L.G., L.S., S. Bell, S.K.K., S. Burgess, K.M., A.M.M., T.R.B., E.A., L.C., J.R.S., P.W., L. Pennells, S.H., M.I., J.D., A.S.B., A.M.W., E.D.A.)
| | - James R. Staley
- BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care (L.G., L.S., S. Bell, S.K.K., S. Burgess, K.M., A.M.M., T.R.B., E.A., L.C., J.R.S., P.W., L. Pennells, S.H., M.I., J.D., A.S.B., A.M.W., E.D.A.)
| | - Natalie Staplin
- Medical Research Council Population Health Research Unit, Clinical Trial Service Unit and Epidemiological Studies Unit (N.S., C.B.), Nuffield Department of Population Health, University of Oxford, UK
| | - Servet Altay
- Department of Cardiology, Trakya University School of Medicine, Edirne, Turkey (S.A.)
| | - Pilar Amiano
- Ministry of Health of the Basque Government, Sub Directorate for Public Health and Addictions of Gipuzkoa, San Sebastián, Spain (P.A.)
- Biodonostia Health Research Institute, Epidemiology of Chronic and Communicable Diseases Group, San Sebastián, Spain (P.A.)
- Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain (P.A., A.G.d.l.C., D.P., C. Santiuste)
| | - Volker Arndt
- Division of Clinical Epidemiology and Aging Research (V.A.), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Johan Ärnlöv
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA (L.G., K.C., R.J.S., D.C.P., P.S., J.J., J.P.C., J.M.G.)
- BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care (L.G., L.S., S. Bell, S.K.K., S. Burgess, K.M., A.M.M., T.R.B., E.A., L.C., J.R.S., P.W., L. Pennells, S.H., M.I., J.D., A.S.B., A.M.W., E.D.A.)
- BHF Centre of Research Excellence, School of Clinical Medicine, Addenbrooke’s Hospital (A.M.M., S. Burgess, J.D., A.M.W., A.S.B., E.D.A.)
- Heart and Lung Research Institute, University of Cambridge, Cambridge UK (L.G., S. Bell, S.K.K., S. Burgess, K.M., A.M.M., E.A., L. Pennells, M.I., J.D., A.S.B., A.M.W., E.D.A.)
- Medical Research Council Biostatistics Unit (A.M.M., S. Burgess), University of Cambridge, UK
- Stroke Research Group, Department of Clinical Neurosciences (S. Bell), University of Cambridge, UK
- NIHR Blood and Transplant Research Unit in Donor Health and Behaviour (S. Bell, T.R.B., E.A., J.D., A.S.B., A.M.W., E.D.A.), University of Cambridge, UK
- MRC Epidemiology Unit, School of Clinical Medicine (C.L., N.W.), University of Cambridge, UK
- Division of Aging (K.C., S.P., J.P.C. J.M.G.), Brigham and Women’s Hospital, Harvard Medical School, Boston, MA
- Division of Cardiovascular Medicine (J.J., T.A.G.), Brigham and Women’s Hospital, Harvard Medical School, Boston, MA
- Department of Epidemiology, Boston University School of Public Health, MA (R.J.S.)
- Division of Nephrology, Department of Medicine (C.R.-C., E.A.A.), Vanderbilt University Medical Center, Nashville, TN
- Department of Biostatistics (R. Tao), Vanderbilt University Medical Center, Nashville, TN
- Medical Research Council Population Health Research Unit, Clinical Trial Service Unit and Epidemiological Studies Unit (N.S., C.B.), Nuffield Department of Population Health, University of Oxford, UK
- Cancer Epidemiology Unit (T.T.), Nuffield Department of Population Health, University of Oxford, UK
- Department of Cardiology, Trakya University School of Medicine, Edirne, Turkey (S.A.)
- Ministry of Health of the Basque Government, Sub Directorate for Public Health and Addictions of Gipuzkoa, San Sebastián, Spain (P.A.)
- Biodonostia Health Research Institute, Epidemiology of Chronic and Communicable Diseases Group, San Sebastián, Spain (P.A.)
- Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain (P.A., A.G.d.l.C., D.P., C. Santiuste)
- Division of Clinical Epidemiology and Aging Research (V.A.), German Cancer Research Center (DKFZ), Heidelberg, Germany
- Department of Cancer Epidemiology (S.K.J., R.K., V.K.), German Cancer Research Center (DKFZ), Heidelberg, Germany
- Division of Family Medicine and Primary Care, Department of Neurobiology, Care Sciences and Society (NVS), Karolinska Institutet, Stockholm, Sweden (J.A., H.B.)
- School of Health and Social Studies, Dalarna University, Falun, Sweden (J.A.)
- Wellbeing & Preventable Chronic Diseases (WPCD) Division, Menzies School of Health Research, Charles Darwin University, Darwin, NT, Australia (E.L.M.B.)
- Baker Heart and Diabetes Institute, Melbourne, VIC, Australia (E.L.M.B., M.I.)
- Institute of Medicine, School of Public Health and Community Medicine (C.B.), Sahlgrenska Academy, University of Gothenburg, Sweden
- Institute of Medicine, Department of Molecular and Clinical Medicine (P.-O.H., A.R.), Sahlgrenska Academy, University of Gothenburg, Sweden
- National Institute for Public Health and the Environment (RIVM), Bilthoven, the Netherlands (J.M.A.B., W.M.M.V.)
- Network Aging Research (NAR), Heidelberg University, Germany (H.B.)
- Studium Patavinum (E.C.), University of Padua, Italy
- Department of Medicine (V.T.), University of Padua, Italy
- Dipartimento di Salute Mentale e Fisica e Medicina Preventiva, Università degli Studi della Campania ‘Luigi Vanvitelli’, Caserta, Italy (P.C.)
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, UK (J.A.C.)
- Johns Hopkins Bloomberg School of Public Health, Baltimore, MD (J.C.)
- Larner College of Medicine, The University of Vermont, Burlington (M.C.)
- The Gertner Institute for Epidemiology and Health Policy Research, Sheba Medical Center, Tel Hashomer, Israel (R.D.)
- School of Public Health, Department of Epidemiology and Preventive Medicine, Tel Aviv University, Ramat Aviv, Tel Aviv, Israel (R.D.)
- Feinstein Institutes for Medical Research, Northwell Health, Manhasset, New York, NY (R.D., K.W.D.)
- Amsterdam University Medical Centers, VUMC, the Netherlands (R.T.d.J.)
- Department of Cardiovascular, Endocrine-metabolic Diseases and Aging, Istituto Superiore di Sanità, Rome, Italy (C.D., L. Palmer)
- Department of Clinical Sciences, Malmö, Lund University, Sweden (G.E., O.M.)
- International Agency for Research on Cancer (IARC), World Health Organization, Lyon, France (H.F., E.W.)
- 12 Octubre Hospital Research Institute, Madrid, Spain (A.G.d,l,C.)
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland and Icelandic Heart Association, Kopavogur, Iceland (V.G.)
- Medical School Faculty of Health & Medical Sciences, The University of Western Australia, Perth, WA, Australia (G.J.H.)
- Region Västra Götaland, Sahlgrenska University Hospital, Department of Medicine Geriatrics and Emergency Medicine/Östra, Gothenburg, Sweden (P.-O.H., A.R.)
- School of Public Health (A.K.H., I.T., E.R.), Imperial College London, UK
- The George Institute for Global Health (M.W.), Imperial College London, UK
- Department of Internal Medicine, Division of Nephrology and Transplantation, Erasmus MC, University Medical Center Rotterdam, the Netherlands (E.J.H.)
- Public Health, Department of Social Medicine, Osaka University Graduate School of Medicine, Suita, Japan (H.I.)
- University of Eastern Finland (UEF), Kuopio, Finland (J.K.)
- Department of Neurology & Neurosurgery, Medical University of Innsbruck, Innsbruck, Austria (S.K.)
- Clinical Epidemiology Team, Institute of Health Economics, Medical University of Innsbruck, Innsbruck, Austria (S.K., P.W.)
- Institute of Epidemiology and Medical Biometry, University of Ulm, Germany (W.K.)
- Deutsches Herzzentrum München, Technische Universität München, Germany (W.K.)
- German Centre for Cardiovascular Research (DZHK), Partner Site Munich Heart Alliance (W.K.)
- School of Public Health, University of Washington, Seattle (R.A.K.)
- Danish Cancer Society Research Center, Copenhagen, Denmark (C.K., A.T.)
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, UK (D.A.L.)
- Population Health Science, Bristol Medical School, UK (D.A.L.)
- Department of Surgical and Perioperative sciences, Urology and Andrology, Umeå University, Sweden (B.L.)
- University Paris-Saclay, UVSQ, Inserm, Villejuif, France (C. MacDonald)
- Institute for Cancer Research, Prevention and Clinical Network (ISPRO), Florence, Italy (G.M.)
- Helmholtz Zentrum München, Munich, Germany (C. Meisinger)
- Navarra Public Health Institute, IdiSNA, Pamplona, Spain (C.M.I.)
- Red de Investigación en Servicios de Salud en Enfermedades Crónicas (REDISSEC), Pamplona, Spain (C.M.I.)
- Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan (T.N.)
- London School of Hygiene & Tropical Medicine, UK (D.N.)
- Herlev and Gentofte Hospital (B.G.N.), Copenhagen University Hospital, Copenhagen, Denmark
- Frederiksberg Hospital B.G.N.), Copenhagen University Hospital, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences (B.G.N.), University of Copenhagen, Denmark
- Department of Public Health (A.T.), University of Copenhagen, Denmark
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, the Netherlands (C.O.-M., Y.T.v.d.S., W.M.M.V.)
- Escuela Andaluza de Salud Pública (EASP), Granada, Spain (D.P.)
- Instituto de Investigación Biosanitaria ibs.GRANADA, Granada, Spain (D.P.)
- Consejería de Sanidad del Principado de Asturias Oviedo, Asturias, Spain (J.R.Q.G.)
- Unit of Cancer Epidemiology, Città della Salute e della Scienza University-Hospital, Turin, Italy (C. Sacerdote)
- Department of Social and Environmental Medicine, Kanazawa Medical University, Uchinada, Japan (M.S.)
- Department of Epidemiology, Murcia Regional Health Council, IMIB-Arrixaca, Spain (C. Santiuste)
- German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany (M.B.S.)
- German Center for Diabetes Research (DZD), Neuherberg, Germany (M.B.S.)
- Institute of Nutritional Science, University of Potsdam, Germany (M.B.S.)
- Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Milano, Italy (S.S.)
- Department of Medical Sciences, Uppsala University, Sweden (J.S.)
- Hyblean Association for Epidemiological Reserach AIRE - ONLUS, Ragusa, Italy (R.T.)
- Universitätsmedizin Greifswald, Institut für Community Medicine, Abteilung SHIP/ Klinisch-Epidemiologische Forschung, Germany (H.V.)
- College of Public Health, University of Iowa (R.B.W.)
- University College London, UK (S.G.W.)
- The George Institute for Global Health, Camperdown, NSW, Australia (M.W.)
- Department of Public Health Medicine, Faculty of Medicine, and Health Services Research and Development Center, University of Tsukuba, Japan (K.Y.)
- Unit of Nutrition and Cancer, Epidemiology Research Program, Catalan Institute of Oncology, Bellvitge Biomedical Research Institute (IDIBELL), L’Hospitalet de Llobregat (Barcelona), Spain (R.Z.-R.)
- Center for Data and Computational Sciences, VA Boston Healthcare System, Boston, MA (S.P.)
- Department of Biostatistics, Boston University School of Public Health, MA (D.R.G.)
- VA Pal Alto Epidemiology Research and Information Center for Genomics, VA Palo Alto Health Care System, CA (P.S.T.)
- Medicine (Cardiovascular Medicine), Stanford University of School of Medicine, CA (P.S.T.)
- Office of Research and Development, Veterans Health Administration, Washington, DC (S.M.)
- Department of Veterans Affairs, Tennessee Valley Health Care System, Vanderbilt University, Nashville (T.L.E.)
- Medicine/Epidemiology, Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN (T.L.E.)
- Department of Surgery, Corporal Michael Crescenz VA Medical Center and Perelman School of Medicine, University of Pennsylvania, Philadelphia (S.M.D.)
- Internal Medicine, VA Atlanta Healthcare System, Decatur, GA (P.W.F.W.)
- Emory University School of Medicine (Cardiology), Emory University, Atlanta, GA (P.W.F.W.)
- Center for Health Decision Science, Harvard T.H. Chan School of Public Health, Boston, MA (T.A.G.)
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, UK (M.I., J.D., A.S.B., A.M.W., E.D.A.)
- The Alan Turing Institute, London, UK (M.I.)
- Computational Medicine, Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Germany (C.L.)
- Department of Human Genetics, Wellcome Sanger Institute, Hinxton, UK (J.D.)
- Division of Nephrology & Hypertension, Department of Medicine, Tennessee Valley Health Care System and Vanderbilt University Medical Center, Nashville (A.M.H.)
- Cambridge Centre for AI in Medicine, UK (A.M.W.)
- Health Data Science Centre, Human Technopole, Milan, Italy (E.D.A.)
| | - Elizabeth L.M. Barr
- Wellbeing & Preventable Chronic Diseases (WPCD) Division, Menzies School of Health Research, Charles Darwin University, Darwin, NT, Australia (E.L.M.B.)
- Baker Heart and Diabetes Institute, Melbourne, VIC, Australia (E.L.M.B., M.I.)
| | - Cecilia Björkelund
- Medical Research Council Population Health Research Unit, Clinical Trial Service Unit and Epidemiological Studies Unit (N.S., C.B.), Nuffield Department of Population Health, University of Oxford, UK
| | - Jolanda M.A. Boer
- National Institute for Public Health and the Environment (RIVM), Bilthoven, the Netherlands (J.M.A.B., W.M.M.V.)
| | - Hermann Brenner
- Division of Family Medicine and Primary Care, Department of Neurobiology, Care Sciences and Society (NVS), Karolinska Institutet, Stockholm, Sweden (J.A., H.B.)
- Network Aging Research (NAR), Heidelberg University, Germany (H.B.)
| | | | - Paolo Chiodini
- Dipartimento di Salute Mentale e Fisica e Medicina Preventiva, Università degli Studi della Campania ‘Luigi Vanvitelli’, Caserta, Italy (P.C.)
| | - Jackie A. Cooper
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, UK (J.A.C.)
| | - Josef Coresh
- Johns Hopkins Bloomberg School of Public Health, Baltimore, MD (J.C.)
| | - Mary Cushman
- Larner College of Medicine, The University of Vermont, Burlington (M.C.)
| | - Rachel Dankner
- The Gertner Institute for Epidemiology and Health Policy Research, Sheba Medical Center, Tel Hashomer, Israel (R.D.)
- School of Public Health, Department of Epidemiology and Preventive Medicine, Tel Aviv University, Ramat Aviv, Tel Aviv, Israel (R.D.)
- Feinstein Institutes for Medical Research, Northwell Health, Manhasset, New York, NY (R.D., K.W.D.)
| | - Karina W. Davidson
- Feinstein Institutes for Medical Research, Northwell Health, Manhasset, New York, NY (R.D., K.W.D.)
| | | | - Chiara Donfrancesco
- Department of Cardiovascular, Endocrine-metabolic Diseases and Aging, Istituto Superiore di Sanità, Rome, Italy (C.D., L. Palmer)
| | - Gunnar Engström
- Department of Clinical Sciences, Malmö, Lund University, Sweden (G.E., O.M.)
| | - Heinz Freisling
- International Agency for Research on Cancer (IARC), World Health Organization, Lyon, France (H.F., E.W.)
| | - Agustín Gómez de la Cámara
- Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain (P.A., A.G.d.l.C., D.P., C. Santiuste)
- 12 Octubre Hospital Research Institute, Madrid, Spain (A.G.d,l,C.)
| | - Vilmundur Gudnason
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland and Icelandic Heart Association, Kopavogur, Iceland (V.G.)
| | - Graeme J. Hankey
- Medical School Faculty of Health & Medical Sciences, The University of Western Australia, Perth, WA, Australia (G.J.H.)
| | - Per-Olof Hansson
- Institute of Medicine, Department of Molecular and Clinical Medicine (P.-O.H., A.R.), Sahlgrenska Academy, University of Gothenburg, Sweden
- Region Västra Götaland, Sahlgrenska University Hospital, Department of Medicine Geriatrics and Emergency Medicine/Östra, Gothenburg, Sweden (P.-O.H., A.R.)
| | - Alicia K. Heath
- School of Public Health (A.K.H., I.T., E.R.), Imperial College London, UK
| | - Ewout J. Hoorn
- Department of Internal Medicine, Division of Nephrology and Transplantation, Erasmus MC, University Medical Center Rotterdam, the Netherlands (E.J.H.)
| | - Hironori Imano
- Public Health, Department of Social Medicine, Osaka University Graduate School of Medicine, Suita, Japan (H.I.)
| | - Simerjot K. Jassal
- Department of Cancer Epidemiology (S.K.J., R.K., V.K.), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Rudolf Kaaks
- Department of Cancer Epidemiology (S.K.J., R.K., V.K.), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Verena Katzke
- Department of Cancer Epidemiology (S.K.J., R.K., V.K.), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Jussi Kauhanen
- University of Eastern Finland (UEF), Kuopio, Finland (J.K.)
| | - Stefan Kiechl
- Department of Neurology & Neurosurgery, Medical University of Innsbruck, Innsbruck, Austria (S.K.)
- Clinical Epidemiology Team, Institute of Health Economics, Medical University of Innsbruck, Innsbruck, Austria (S.K., P.W.)
| | - Wolfgang Koenig
- Institute of Epidemiology and Medical Biometry, University of Ulm, Germany (W.K.)
- Deutsches Herzzentrum München, Technische Universität München, Germany (W.K.)
- German Centre for Cardiovascular Research (DZHK), Partner Site Munich Heart Alliance (W.K.)
| | | | - Cecilie Kyrø
- Danish Cancer Society Research Center, Copenhagen, Denmark (C.K., A.T.)
| | - Deborah A. Lawlor
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, UK (D.A.L.)
- Population Health Science, Bristol Medical School, UK (D.A.L.)
| | - Börje Ljungberg
- Department of Surgical and Perioperative sciences, Urology and Andrology, Umeå University, Sweden (B.L.)
| | - Conor MacDonald
- University Paris-Saclay, UVSQ, Inserm, Villejuif, France (C. MacDonald)
| | - Giovanna Masala
- Institute for Cancer Research, Prevention and Clinical Network (ISPRO), Florence, Italy (G.M.)
| | | | - Olle Melander
- Department of Clinical Sciences, Malmö, Lund University, Sweden (G.E., O.M.)
| | - Conchi Moreno Iribas
- Navarra Public Health Institute, IdiSNA, Pamplona, Spain (C.M.I.)
- Red de Investigación en Servicios de Salud en Enfermedades Crónicas (REDISSEC), Pamplona, Spain (C.M.I.)
| | - Toshiharu Ninomiya
- Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan (T.N.)
| | | | - Børge G. Nordestgaard
- Herlev and Gentofte Hospital (B.G.N.), Copenhagen University Hospital, Copenhagen, Denmark
- Frederiksberg Hospital B.G.N.), Copenhagen University Hospital, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences (B.G.N.), University of Copenhagen, Denmark
| | - Charlotte Onland-Moret
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, the Netherlands (C.O.-M., Y.T.v.d.S., W.M.M.V.)
| | - Luigi Palmieri
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA (L.G., K.C., R.J.S., D.C.P., P.S., J.J., J.P.C., J.M.G.)
- BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care (L.G., L.S., S. Bell, S.K.K., S. Burgess, K.M., A.M.M., T.R.B., E.A., L.C., J.R.S., P.W., L. Pennells, S.H., M.I., J.D., A.S.B., A.M.W., E.D.A.)
- BHF Centre of Research Excellence, School of Clinical Medicine, Addenbrooke’s Hospital (A.M.M., S. Burgess, J.D., A.M.W., A.S.B., E.D.A.)
- Heart and Lung Research Institute, University of Cambridge, Cambridge UK (L.G., S. Bell, S.K.K., S. Burgess, K.M., A.M.M., E.A., L. Pennells, M.I., J.D., A.S.B., A.M.W., E.D.A.)
- Medical Research Council Biostatistics Unit (A.M.M., S. Burgess), University of Cambridge, UK
- Stroke Research Group, Department of Clinical Neurosciences (S. Bell), University of Cambridge, UK
- NIHR Blood and Transplant Research Unit in Donor Health and Behaviour (S. Bell, T.R.B., E.A., J.D., A.S.B., A.M.W., E.D.A.), University of Cambridge, UK
- MRC Epidemiology Unit, School of Clinical Medicine (C.L., N.W.), University of Cambridge, UK
- Division of Aging (K.C., S.P., J.P.C. J.M.G.), Brigham and Women’s Hospital, Harvard Medical School, Boston, MA
- Division of Cardiovascular Medicine (J.J., T.A.G.), Brigham and Women’s Hospital, Harvard Medical School, Boston, MA
- Department of Epidemiology, Boston University School of Public Health, MA (R.J.S.)
- Division of Nephrology, Department of Medicine (C.R.-C., E.A.A.), Vanderbilt University Medical Center, Nashville, TN
- Department of Biostatistics (R. Tao), Vanderbilt University Medical Center, Nashville, TN
- Medical Research Council Population Health Research Unit, Clinical Trial Service Unit and Epidemiological Studies Unit (N.S., C.B.), Nuffield Department of Population Health, University of Oxford, UK
- Cancer Epidemiology Unit (T.T.), Nuffield Department of Population Health, University of Oxford, UK
- Department of Cardiology, Trakya University School of Medicine, Edirne, Turkey (S.A.)
- Ministry of Health of the Basque Government, Sub Directorate for Public Health and Addictions of Gipuzkoa, San Sebastián, Spain (P.A.)
- Biodonostia Health Research Institute, Epidemiology of Chronic and Communicable Diseases Group, San Sebastián, Spain (P.A.)
- Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain (P.A., A.G.d.l.C., D.P., C. Santiuste)
- Division of Clinical Epidemiology and Aging Research (V.A.), German Cancer Research Center (DKFZ), Heidelberg, Germany
- Department of Cancer Epidemiology (S.K.J., R.K., V.K.), German Cancer Research Center (DKFZ), Heidelberg, Germany
- Division of Family Medicine and Primary Care, Department of Neurobiology, Care Sciences and Society (NVS), Karolinska Institutet, Stockholm, Sweden (J.A., H.B.)
- School of Health and Social Studies, Dalarna University, Falun, Sweden (J.A.)
- Wellbeing & Preventable Chronic Diseases (WPCD) Division, Menzies School of Health Research, Charles Darwin University, Darwin, NT, Australia (E.L.M.B.)
- Baker Heart and Diabetes Institute, Melbourne, VIC, Australia (E.L.M.B., M.I.)
- Institute of Medicine, School of Public Health and Community Medicine (C.B.), Sahlgrenska Academy, University of Gothenburg, Sweden
- Institute of Medicine, Department of Molecular and Clinical Medicine (P.-O.H., A.R.), Sahlgrenska Academy, University of Gothenburg, Sweden
- National Institute for Public Health and the Environment (RIVM), Bilthoven, the Netherlands (J.M.A.B., W.M.M.V.)
- Network Aging Research (NAR), Heidelberg University, Germany (H.B.)
- Studium Patavinum (E.C.), University of Padua, Italy
- Department of Medicine (V.T.), University of Padua, Italy
- Dipartimento di Salute Mentale e Fisica e Medicina Preventiva, Università degli Studi della Campania ‘Luigi Vanvitelli’, Caserta, Italy (P.C.)
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, UK (J.A.C.)
- Johns Hopkins Bloomberg School of Public Health, Baltimore, MD (J.C.)
- Larner College of Medicine, The University of Vermont, Burlington (M.C.)
- The Gertner Institute for Epidemiology and Health Policy Research, Sheba Medical Center, Tel Hashomer, Israel (R.D.)
- School of Public Health, Department of Epidemiology and Preventive Medicine, Tel Aviv University, Ramat Aviv, Tel Aviv, Israel (R.D.)
- Feinstein Institutes for Medical Research, Northwell Health, Manhasset, New York, NY (R.D., K.W.D.)
- Amsterdam University Medical Centers, VUMC, the Netherlands (R.T.d.J.)
- Department of Cardiovascular, Endocrine-metabolic Diseases and Aging, Istituto Superiore di Sanità, Rome, Italy (C.D., L. Palmer)
- Department of Clinical Sciences, Malmö, Lund University, Sweden (G.E., O.M.)
- International Agency for Research on Cancer (IARC), World Health Organization, Lyon, France (H.F., E.W.)
- 12 Octubre Hospital Research Institute, Madrid, Spain (A.G.d,l,C.)
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland and Icelandic Heart Association, Kopavogur, Iceland (V.G.)
- Medical School Faculty of Health & Medical Sciences, The University of Western Australia, Perth, WA, Australia (G.J.H.)
- Region Västra Götaland, Sahlgrenska University Hospital, Department of Medicine Geriatrics and Emergency Medicine/Östra, Gothenburg, Sweden (P.-O.H., A.R.)
- School of Public Health (A.K.H., I.T., E.R.), Imperial College London, UK
- The George Institute for Global Health (M.W.), Imperial College London, UK
- Department of Internal Medicine, Division of Nephrology and Transplantation, Erasmus MC, University Medical Center Rotterdam, the Netherlands (E.J.H.)
- Public Health, Department of Social Medicine, Osaka University Graduate School of Medicine, Suita, Japan (H.I.)
- University of Eastern Finland (UEF), Kuopio, Finland (J.K.)
- Department of Neurology & Neurosurgery, Medical University of Innsbruck, Innsbruck, Austria (S.K.)
- Clinical Epidemiology Team, Institute of Health Economics, Medical University of Innsbruck, Innsbruck, Austria (S.K., P.W.)
- Institute of Epidemiology and Medical Biometry, University of Ulm, Germany (W.K.)
- Deutsches Herzzentrum München, Technische Universität München, Germany (W.K.)
- German Centre for Cardiovascular Research (DZHK), Partner Site Munich Heart Alliance (W.K.)
- School of Public Health, University of Washington, Seattle (R.A.K.)
- Danish Cancer Society Research Center, Copenhagen, Denmark (C.K., A.T.)
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, UK (D.A.L.)
- Population Health Science, Bristol Medical School, UK (D.A.L.)
- Department of Surgical and Perioperative sciences, Urology and Andrology, Umeå University, Sweden (B.L.)
- University Paris-Saclay, UVSQ, Inserm, Villejuif, France (C. MacDonald)
- Institute for Cancer Research, Prevention and Clinical Network (ISPRO), Florence, Italy (G.M.)
- Helmholtz Zentrum München, Munich, Germany (C. Meisinger)
- Navarra Public Health Institute, IdiSNA, Pamplona, Spain (C.M.I.)
- Red de Investigación en Servicios de Salud en Enfermedades Crónicas (REDISSEC), Pamplona, Spain (C.M.I.)
- Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan (T.N.)
- London School of Hygiene & Tropical Medicine, UK (D.N.)
- Herlev and Gentofte Hospital (B.G.N.), Copenhagen University Hospital, Copenhagen, Denmark
- Frederiksberg Hospital B.G.N.), Copenhagen University Hospital, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences (B.G.N.), University of Copenhagen, Denmark
- Department of Public Health (A.T.), University of Copenhagen, Denmark
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, the Netherlands (C.O.-M., Y.T.v.d.S., W.M.M.V.)
- Escuela Andaluza de Salud Pública (EASP), Granada, Spain (D.P.)
- Instituto de Investigación Biosanitaria ibs.GRANADA, Granada, Spain (D.P.)
- Consejería de Sanidad del Principado de Asturias Oviedo, Asturias, Spain (J.R.Q.G.)
- Unit of Cancer Epidemiology, Città della Salute e della Scienza University-Hospital, Turin, Italy (C. Sacerdote)
- Department of Social and Environmental Medicine, Kanazawa Medical University, Uchinada, Japan (M.S.)
- Department of Epidemiology, Murcia Regional Health Council, IMIB-Arrixaca, Spain (C. Santiuste)
- German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany (M.B.S.)
- German Center for Diabetes Research (DZD), Neuherberg, Germany (M.B.S.)
- Institute of Nutritional Science, University of Potsdam, Germany (M.B.S.)
- Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Milano, Italy (S.S.)
- Department of Medical Sciences, Uppsala University, Sweden (J.S.)
- Hyblean Association for Epidemiological Reserach AIRE - ONLUS, Ragusa, Italy (R.T.)
- Universitätsmedizin Greifswald, Institut für Community Medicine, Abteilung SHIP/ Klinisch-Epidemiologische Forschung, Germany (H.V.)
- College of Public Health, University of Iowa (R.B.W.)
- University College London, UK (S.G.W.)
- The George Institute for Global Health, Camperdown, NSW, Australia (M.W.)
- Department of Public Health Medicine, Faculty of Medicine, and Health Services Research and Development Center, University of Tsukuba, Japan (K.Y.)
- Unit of Nutrition and Cancer, Epidemiology Research Program, Catalan Institute of Oncology, Bellvitge Biomedical Research Institute (IDIBELL), L’Hospitalet de Llobregat (Barcelona), Spain (R.Z.-R.)
- Center for Data and Computational Sciences, VA Boston Healthcare System, Boston, MA (S.P.)
- Department of Biostatistics, Boston University School of Public Health, MA (D.R.G.)
- VA Pal Alto Epidemiology Research and Information Center for Genomics, VA Palo Alto Health Care System, CA (P.S.T.)
- Medicine (Cardiovascular Medicine), Stanford University of School of Medicine, CA (P.S.T.)
- Office of Research and Development, Veterans Health Administration, Washington, DC (S.M.)
- Department of Veterans Affairs, Tennessee Valley Health Care System, Vanderbilt University, Nashville (T.L.E.)
- Medicine/Epidemiology, Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN (T.L.E.)
- Department of Surgery, Corporal Michael Crescenz VA Medical Center and Perelman School of Medicine, University of Pennsylvania, Philadelphia (S.M.D.)
- Internal Medicine, VA Atlanta Healthcare System, Decatur, GA (P.W.F.W.)
- Emory University School of Medicine (Cardiology), Emory University, Atlanta, GA (P.W.F.W.)
- Center for Health Decision Science, Harvard T.H. Chan School of Public Health, Boston, MA (T.A.G.)
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, UK (M.I., J.D., A.S.B., A.M.W., E.D.A.)
- The Alan Turing Institute, London, UK (M.I.)
- Computational Medicine, Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Germany (C.L.)
- Department of Human Genetics, Wellcome Sanger Institute, Hinxton, UK (J.D.)
- Division of Nephrology & Hypertension, Department of Medicine, Tennessee Valley Health Care System and Vanderbilt University Medical Center, Nashville (A.M.H.)
- Cambridge Centre for AI in Medicine, UK (A.M.W.)
- Health Data Science Centre, Human Technopole, Milan, Italy (E.D.A.)
| | - Dafina Petrova
- Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain (P.A., A.G.d.l.C., D.P., C. Santiuste)
- Escuela Andaluza de Salud Pública (EASP), Granada, Spain (D.P.)
- Instituto de Investigación Biosanitaria ibs.GRANADA, Granada, Spain (D.P.)
| | | | - Annika Rosengren
- Institute of Medicine, Department of Molecular and Clinical Medicine (P.-O.H., A.R.), Sahlgrenska Academy, University of Gothenburg, Sweden
- Region Västra Götaland, Sahlgrenska University Hospital, Department of Medicine Geriatrics and Emergency Medicine/Östra, Gothenburg, Sweden (P.-O.H., A.R.)
| | - Carlotta Sacerdote
- Unit of Cancer Epidemiology, Città della Salute e della Scienza University-Hospital, Turin, Italy (C. Sacerdote)
| | - Masaru Sakurai
- Department of Social and Environmental Medicine, Kanazawa Medical University, Uchinada, Japan (M.S.)
| | - Carmen Santiuste
- Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain (P.A., A.G.d.l.C., D.P., C. Santiuste)
- Department of Epidemiology, Murcia Regional Health Council, IMIB-Arrixaca, Spain (C. Santiuste)
| | - Matthias B. Schulze
- German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany (M.B.S.)
- German Center for Diabetes Research (DZD), Neuherberg, Germany (M.B.S.)
- Institute of Nutritional Science, University of Potsdam, Germany (M.B.S.)
| | - Sabina Sieri
- Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Milano, Italy (S.S.)
| | - Johan Sundström
- Department of Medical Sciences, Uppsala University, Sweden (J.S.)
| | | | - Anne Tjønneland
- Danish Cancer Society Research Center, Copenhagen, Denmark (C.K., A.T.)
- Department of Public Health (A.T.), University of Copenhagen, Denmark
| | - Tammy Tong
- Cancer Epidemiology Unit (T.T.), Nuffield Department of Population Health, University of Oxford, UK
| | - Rosario Tumino
- Hyblean Association for Epidemiological Reserach AIRE - ONLUS, Ragusa, Italy (R.T.)
| | - Ioanna Tzoulaki
- School of Public Health (A.K.H., I.T., E.R.), Imperial College London, UK
| | - Yvonne T. van der Schouw
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, the Netherlands (C.O.-M., Y.T.v.d.S., W.M.M.V.)
| | - W.M. Monique Verschuren
- National Institute for Public Health and the Environment (RIVM), Bilthoven, the Netherlands (J.M.A.B., W.M.M.V.)
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, the Netherlands (C.O.-M., Y.T.v.d.S., W.M.M.V.)
| | - Henry Völzke
- Universitätsmedizin Greifswald, Institut für Community Medicine, Abteilung SHIP/ Klinisch-Epidemiologische Forschung, Germany (H.V.)
| | | | | | - Elisabete Weiderpass
- International Agency for Research on Cancer (IARC), World Health Organization, Lyon, France (H.F., E.W.)
| | - Peter Willeit
- BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care (L.G., L.S., S. Bell, S.K.K., S. Burgess, K.M., A.M.M., T.R.B., E.A., L.C., J.R.S., P.W., L. Pennells, S.H., M.I., J.D., A.S.B., A.M.W., E.D.A.)
- Clinical Epidemiology Team, Institute of Health Economics, Medical University of Innsbruck, Innsbruck, Austria (S.K., P.W.)
| | - Mark Woodward
- The George Institute for Global Health, Camperdown, NSW, Australia (M.W.)
| | - Kazumasa Yamagishi
- Department of Public Health Medicine, Faculty of Medicine, and Health Services Research and Development Center, University of Tsukuba, Japan (K.Y.)
| | - Raul Zamora-Ros
- Unit of Nutrition and Cancer, Epidemiology Research Program, Catalan Institute of Oncology, Bellvitge Biomedical Research Institute (IDIBELL), L’Hospitalet de Llobregat (Barcelona), Spain (R.Z.-R.)
| | - Elvis A. Akwo
- Division of Nephrology, Department of Medicine (C.R.-C., E.A.A.), Vanderbilt University Medical Center, Nashville, TN
| | - Saiju Pyarajan
- Division of Aging (K.C., S.P., J.P.C. J.M.G.), Brigham and Women’s Hospital, Harvard Medical School, Boston, MA
- Center for Data and Computational Sciences, VA Boston Healthcare System, Boston, MA (S.P.)
| | - David R. Gagnon
- Department of Biostatistics, Boston University School of Public Health, MA (D.R.G.)
| | - Philip S. Tsao
- VA Pal Alto Epidemiology Research and Information Center for Genomics, VA Palo Alto Health Care System, CA (P.S.T.)
- Medicine (Cardiovascular Medicine), Stanford University of School of Medicine, CA (P.S.T.)
| | - Sumitra Muralidhar
- Office of Research and Development, Veterans Health Administration, Washington, DC (S.M.)
| | - Todd L. Edwards
- Department of Veterans Affairs, Tennessee Valley Health Care System, Vanderbilt University, Nashville (T.L.E.)
- Medicine/Epidemiology, Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN (T.L.E.)
| | - Scott M. Damrauer
- Department of Surgery, Corporal Michael Crescenz VA Medical Center and Perelman School of Medicine, University of Pennsylvania, Philadelphia (S.M.D.)
| | - Jacob Joseph
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA (L.G., K.C., R.J.S., D.C.P., P.S., J.J., J.P.C., J.M.G.)
- Division of Cardiovascular Medicine (J.J., T.A.G.), Brigham and Women’s Hospital, Harvard Medical School, Boston, MA
| | - Lisa Pennells
- BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care (L.G., L.S., S. Bell, S.K.K., S. Burgess, K.M., A.M.M., T.R.B., E.A., L.C., J.R.S., P.W., L. Pennells, S.H., M.I., J.D., A.S.B., A.M.W., E.D.A.)
- Heart and Lung Research Institute, University of Cambridge, Cambridge UK (L.G., S. Bell, S.K.K., S. Burgess, K.M., A.M.M., E.A., L. Pennells, M.I., J.D., A.S.B., A.M.W., E.D.A.)
| | - Peter W.F. Wilson
- Internal Medicine, VA Atlanta Healthcare System, Decatur, GA (P.W.F.W.)
- Emory University School of Medicine (Cardiology), Emory University, Atlanta, GA (P.W.F.W.)
| | - Seamus Harrison
- BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care (L.G., L.S., S. Bell, S.K.K., S. Burgess, K.M., A.M.M., T.R.B., E.A., L.C., J.R.S., P.W., L. Pennells, S.H., M.I., J.D., A.S.B., A.M.W., E.D.A.)
| | - Thomas A. Gaziano
- Division of Cardiovascular Medicine (J.J., T.A.G.), Brigham and Women’s Hospital, Harvard Medical School, Boston, MA
- Center for Health Decision Science, Harvard T.H. Chan School of Public Health, Boston, MA (T.A.G.)
| | - Michael Inouye
- BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care (L.G., L.S., S. Bell, S.K.K., S. Burgess, K.M., A.M.M., T.R.B., E.A., L.C., J.R.S., P.W., L. Pennells, S.H., M.I., J.D., A.S.B., A.M.W., E.D.A.)
- Heart and Lung Research Institute, University of Cambridge, Cambridge UK (L.G., S. Bell, S.K.K., S. Burgess, K.M., A.M.M., E.A., L. Pennells, M.I., J.D., A.S.B., A.M.W., E.D.A.)
- Baker Heart and Diabetes Institute, Melbourne, VIC, Australia (E.L.M.B., M.I.)
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, UK (M.I., J.D., A.S.B., A.M.W., E.D.A.)
- The Alan Turing Institute, London, UK (M.I.)
| | - Colin Baigent
- Institute of Medicine, School of Public Health and Community Medicine (C.B.), Sahlgrenska Academy, University of Gothenburg, Sweden
| | - Juan P. Casas
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA (L.G., K.C., R.J.S., D.C.P., P.S., J.J., J.P.C., J.M.G.)
- Division of Aging (K.C., S.P., J.P.C. J.M.G.), Brigham and Women’s Hospital, Harvard Medical School, Boston, MA
| | - Claudia Langenberg
- MRC Epidemiology Unit, School of Clinical Medicine (C.L., N.W.), University of Cambridge, UK
- Computational Medicine, Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Germany (C.L.)
| | - Nick Wareham
- MRC Epidemiology Unit, School of Clinical Medicine (C.L., N.W.), University of Cambridge, UK
| | - Elio Riboli
- The George Institute for Global Health (M.W.), Imperial College London, UK
| | - J.Michael Gaziano
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA (L.G., K.C., R.J.S., D.C.P., P.S., J.J., J.P.C., J.M.G.)
- Division of Aging (K.C., S.P., J.P.C. J.M.G.), Brigham and Women’s Hospital, Harvard Medical School, Boston, MA
| | - John Danesh
- BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care (L.G., L.S., S. Bell, S.K.K., S. Burgess, K.M., A.M.M., T.R.B., E.A., L.C., J.R.S., P.W., L. Pennells, S.H., M.I., J.D., A.S.B., A.M.W., E.D.A.)
- BHF Centre of Research Excellence, School of Clinical Medicine, Addenbrooke’s Hospital (A.M.M., S. Burgess, J.D., A.M.W., A.S.B., E.D.A.)
- Heart and Lung Research Institute, University of Cambridge, Cambridge UK (L.G., S. Bell, S.K.K., S. Burgess, K.M., A.M.M., E.A., L. Pennells, M.I., J.D., A.S.B., A.M.W., E.D.A.)
- NIHR Blood and Transplant Research Unit in Donor Health and Behaviour (S. Bell, T.R.B., E.A., J.D., A.S.B., A.M.W., E.D.A.), University of Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, UK (M.I., J.D., A.S.B., A.M.W., E.D.A.)
- Department of Human Genetics, Wellcome Sanger Institute, Hinxton, UK (J.D.)
| | - Adriana M. Hung
- Division of Nephrology & Hypertension, Department of Medicine, Tennessee Valley Health Care System and Vanderbilt University Medical Center, Nashville (A.M.H.)
| | - Adam S. Butterworth
- BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care (L.G., L.S., S. Bell, S.K.K., S. Burgess, K.M., A.M.M., T.R.B., E.A., L.C., J.R.S., P.W., L. Pennells, S.H., M.I., J.D., A.S.B., A.M.W., E.D.A.)
- BHF Centre of Research Excellence, School of Clinical Medicine, Addenbrooke’s Hospital (A.M.M., S. Burgess, J.D., A.M.W., A.S.B., E.D.A.)
- Heart and Lung Research Institute, University of Cambridge, Cambridge UK (L.G., S. Bell, S.K.K., S. Burgess, K.M., A.M.M., E.A., L. Pennells, M.I., J.D., A.S.B., A.M.W., E.D.A.)
- NIHR Blood and Transplant Research Unit in Donor Health and Behaviour (S. Bell, T.R.B., E.A., J.D., A.S.B., A.M.W., E.D.A.), University of Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, UK (M.I., J.D., A.S.B., A.M.W., E.D.A.)
| | - Angela M. Wood
- BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care (L.G., L.S., S. Bell, S.K.K., S. Burgess, K.M., A.M.M., T.R.B., E.A., L.C., J.R.S., P.W., L. Pennells, S.H., M.I., J.D., A.S.B., A.M.W., E.D.A.)
- BHF Centre of Research Excellence, School of Clinical Medicine, Addenbrooke’s Hospital (A.M.M., S. Burgess, J.D., A.M.W., A.S.B., E.D.A.)
- Heart and Lung Research Institute, University of Cambridge, Cambridge UK (L.G., S. Bell, S.K.K., S. Burgess, K.M., A.M.M., E.A., L. Pennells, M.I., J.D., A.S.B., A.M.W., E.D.A.)
- NIHR Blood and Transplant Research Unit in Donor Health and Behaviour (S. Bell, T.R.B., E.A., J.D., A.S.B., A.M.W., E.D.A.), University of Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, UK (M.I., J.D., A.S.B., A.M.W., E.D.A.)
- Cambridge Centre for AI in Medicine, UK (A.M.W.)
| | - Emanuele Di Angelantonio
- BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care (L.G., L.S., S. Bell, S.K.K., S. Burgess, K.M., A.M.M., T.R.B., E.A., L.C., J.R.S., P.W., L. Pennells, S.H., M.I., J.D., A.S.B., A.M.W., E.D.A.)
- BHF Centre of Research Excellence, School of Clinical Medicine, Addenbrooke’s Hospital (A.M.M., S. Burgess, J.D., A.M.W., A.S.B., E.D.A.)
- Heart and Lung Research Institute, University of Cambridge, Cambridge UK (L.G., S. Bell, S.K.K., S. Burgess, K.M., A.M.M., E.A., L. Pennells, M.I., J.D., A.S.B., A.M.W., E.D.A.)
- NIHR Blood and Transplant Research Unit in Donor Health and Behaviour (S. Bell, T.R.B., E.A., J.D., A.S.B., A.M.W., E.D.A.), University of Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, UK (M.I., J.D., A.S.B., A.M.W., E.D.A.)
- Health Data Science Centre, Human Technopole, Milan, Italy (E.D.A.)
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14
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Ferolito B, do Valle IF, Gerlovin H, Costa L, Casas JP, Gaziano JM, Gagnon DR, Begoli E, Barabási AL, Cho K. Visualizing novel connections and genetic similarities across diseases using a network-medicine based approach. Sci Rep 2022; 12:14914. [PMID: 36050444 PMCID: PMC9436158 DOI: 10.1038/s41598-022-19244-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Accepted: 08/26/2022] [Indexed: 11/08/2022] Open
Abstract
Understanding the genetic relationships between human disorders could lead to better treatment and prevention strategies, especially for individuals with multiple comorbidities. A common resource for studying genetic-disease relationships is the GWAS Catalog, a large and well curated repository of SNP-trait associations from various studies and populations. Some of these populations are contained within mega-biobanks such as the Million Veteran Program (MVP), which has enabled the genetic classification of several diseases in a large well-characterized and heterogeneous population. Here we aim to provide a network of the genetic relationships among diseases and to demonstrate the utility of quantifying the extent to which a given resource such as MVP has contributed to the discovery of such relations. We use a network-based approach to evaluate shared variants among thousands of traits in the GWAS Catalog repository. Our results indicate many more novel disease relationships that did not exist in early studies and demonstrate that the network can reveal clusters of diseases mechanistically related. Finally, we show novel disease connections that emerge when MVP data is included, highlighting methodology that can be used to indicate the contributions of a given biobank.
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Affiliation(s)
- Brian Ferolito
- VA Boston Healthcare System, Massachusetts Veterans Epidemiology and Research Information Center, (MAVERIC), 150 S. Huntington Avenue, Boston, 02130, USA.
| | - Italo Faria do Valle
- VA Boston Healthcare System, Massachusetts Veterans Epidemiology and Research Information Center, (MAVERIC), 150 S. Huntington Avenue, Boston, 02130, USA
- Center for Complex Network Research, Department of Physics, Northeastern University, Boston, 02115, USA
| | - Hanna Gerlovin
- VA Boston Healthcare System, Massachusetts Veterans Epidemiology and Research Information Center, (MAVERIC), 150 S. Huntington Avenue, Boston, 02130, USA
| | - Lauren Costa
- VA Boston Healthcare System, Massachusetts Veterans Epidemiology and Research Information Center, (MAVERIC), 150 S. Huntington Avenue, Boston, 02130, USA
| | - Juan P Casas
- VA Boston Healthcare System, Massachusetts Veterans Epidemiology and Research Information Center, (MAVERIC), 150 S. Huntington Avenue, Boston, 02130, USA
- Brigham and Women's Hospital, Division of Aging, Department of Medicine, Harvard Medical School, Boston, 02115, USA
| | - J Michael Gaziano
- VA Boston Healthcare System, Massachusetts Veterans Epidemiology and Research Information Center, (MAVERIC), 150 S. Huntington Avenue, Boston, 02130, USA
- Brigham and Women's Hospital, Division of Aging, Department of Medicine, Harvard Medical School, Boston, 02115, USA
| | - David R Gagnon
- VA Boston Healthcare System, Massachusetts Veterans Epidemiology and Research Information Center, (MAVERIC), 150 S. Huntington Avenue, Boston, 02130, USA
- School of Public Health, Department of Biostatistics, Boston University, Boston, 02215, USA
| | - Edmon Begoli
- Oak Ridge National Laboratory, Oak Ridge, 37830, USA
| | - Albert-László Barabási
- Center for Complex Network Research, Department of Physics, Northeastern University, Boston, 02115, USA
| | - Kelly Cho
- VA Boston Healthcare System, Massachusetts Veterans Epidemiology and Research Information Center, (MAVERIC), 150 S. Huntington Avenue, Boston, 02130, USA
- Brigham and Women's Hospital, Division of Aging, Department of Medicine, Harvard Medical School, Boston, 02115, USA
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15
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Cai T, He Z, Hong C, Zhang Y, Ho YL, Honerlaw J, Geva A, Ayakulangara Panickan V, King A, Gagnon DR, Gaziano M, Cho K, Liao K, Cai T. Scalable relevance ranking algorithm via semantic similarity assessment improves efficiency of medical chart review. J Biomed Inform 2022; 132:104109. [PMID: 35660521 DOI: 10.1016/j.jbi.2022.104109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2022] [Revised: 04/30/2022] [Accepted: 05/29/2022] [Indexed: 01/19/2023]
Abstract
OBJECTIVE Accurately assigning phenotype information to individual patients via computational phenotyping using Electronic Health Records (EHRs) has been seen as the first step towards enabling EHRs for precision medicine research. Chart review labels annotated by clinical experts, also known as "gold standard" labels, are essential for the development and validation of computational phenotyping algorithms. However, given the complexity of EHR systems, the process of chart review is both labor intensive and time consuming. We propose a fully automated algorithm, referred to as pGUESS, to rank EHR notes according to their relevance to a given phenotype. By identifying the most relevant notes, pGUESS can greatly improve the efficiency and accuracy of chart reviews. METHOD pGUESS uses prior guided semantic similarity to measure the informativeness of a clinical note to a given phenotype. We first select candidate clinical concepts from a pool of comprehensive medical concepts using public knowledge sources and then derive the semantic embedding vector (SEV) for a reference article (SEVref) and each note (SEVnote). The algorithm scores the relevance of a note as the cosine similarity between SEVnote and SEVref. RESULTS The algorithm was validated against four sets of 200 notes that were manually annotated by clinical experts to assess their informativeness to one of three disease phenotypes. pGUESS algorithm substantially outperforms existing unsupervised approaches for classifying the relevance status with respect to both accuracy and scalability across phenotypes. Averaging over the three phenotypes, the rank correlation between the algorithm ranking and gold standard label was 0.64 for pGUESS, but only 0.47 and 0.35 for the next two best performing algorithms. pGUESS is also much more computationally scalable compared to existing algorithms. CONCLUSION pGUESS algorithm can substantially reduce the burden of chart review and holds potential in improving the efficiency and accuracy of human annotation.
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Affiliation(s)
- Tianrun Cai
- Division of Rheumatology, Inflammation, and Immunity, Brigham and Women's Hospital, 60 Fenwood Road, Boston, USA; Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Suite 514, Boston, USA; VA Boston Healthcare System, 150 S Huntington Ave, Boston, USA.
| | - Zeling He
- Division of Rheumatology, Inflammation, and Immunity, Brigham and Women's Hospital, 60 Fenwood Road, Boston, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, 677 Huntington Avenue, Boston, MA, USA
| | - Chuan Hong
- Department of Biostatistics & Bioinformatics, Duke University, Duke University Medical Center 2424 Erwin Road, Suite 1102 Hock Plaza Box 2721, Durham, NC, USA
| | - Yichi Zhang
- Department of Computer Science and Statistics, University of Rhode Island, Tyler Hall, 9 Greenhouse Road, Suite 2, Kingston, RI, USA
| | - Yuk-Lam Ho
- VA Boston Healthcare System, 150 S Huntington Ave, Boston, USA
| | | | - Alon Geva
- Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Suite 514, Boston, USA; Department of Anesthesiology, Boston Children's Hospital, 300 Longwood Avenue, Bader, 6th Floor, Boston, MA, USA
| | | | - Amanda King
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, 677 Huntington Avenue, Boston, MA, USA
| | - David R Gagnon
- VA Boston Healthcare System, 150 S Huntington Ave, Boston, USA; Department of Biostatistics, Boston University, School of Public Health, 801 Massachusetts Ave Crosstown Center, Boston, MA, USA
| | - Michael Gaziano
- Division of Rheumatology, Inflammation, and Immunity, Brigham and Women's Hospital, 60 Fenwood Road, Boston, USA; VA Boston Healthcare System, 150 S Huntington Ave, Boston, USA
| | - Kelly Cho
- Division of Rheumatology, Inflammation, and Immunity, Brigham and Women's Hospital, 60 Fenwood Road, Boston, USA; Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Suite 514, Boston, USA; VA Boston Healthcare System, 150 S Huntington Ave, Boston, USA
| | - Katherine Liao
- Division of Rheumatology, Inflammation, and Immunity, Brigham and Women's Hospital, 60 Fenwood Road, Boston, USA; Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Suite 514, Boston, USA; VA Boston Healthcare System, 150 S Huntington Ave, Boston, USA
| | - Tianxi Cai
- Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Suite 514, Boston, USA; VA Boston Healthcare System, 150 S Huntington Ave, Boston, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, 677 Huntington Avenue, Boston, MA, USA
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16
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Dickerman BA, Madenci AL, Gerlovin H, Kurgansky KE, Wise JK, Figueroa Muñiz MJ, Ferolito BR, Gagnon DR, Gaziano JM, Cho K, Casas JP, Hernán MA. Comparative Safety of BNT162b2 and mRNA-1273 Vaccines in a Nationwide Cohort of US Veterans. JAMA Intern Med 2022; 182:739-746. [PMID: 35696161 PMCID: PMC9194743 DOI: 10.1001/jamainternmed.2022.2109] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Accepted: 04/21/2022] [Indexed: 01/05/2023]
Abstract
Importance The risk of adverse events has been found to be low for participants receiving the BNT162b2 (Pfizer-BioNTech) and mRNA-1273 (Moderna Inc) vaccines in randomized trials. However, a head-to-head comparison of their safety for a broader range of potential adverse events over longer follow-up and in larger and more diverse populations is lacking, to our knowledge. Objective To compare the head-to-head safety in terms of risk of adverse events of the BNT162b2 and mRNA-1273 vaccines in the national health care databases of the US Department of Veterans Affairs, the largest integrated health care system in the US. Design, Setting, and Participants In this cohort study, the electronic health records of US veterans who received a first dose of the BNT162b2 or mRNA-1273 vaccine between January 4 and September 20, 2021, were used. Recipients of each vaccine were matched in a 1:1 ratio according to their risk factors. Exposures Vaccination with either the BNT162b2 vaccine, with a second dose scheduled 21 days later, or the mRNA-1273 vaccine, with a second dose scheduled 28 days later. Main Outcomes and Measures A large panel of potential adverse events was evaluated; the panel included neurologic events, hematologic events, hemorrhagic stroke, ischemic stroke, myocardial infarction, other thromboembolic events, myocarditis or pericarditis, arrhythmia, kidney injury, appendicitis, autoimmune events, herpes zoster or simplex, arthritis or arthropathy, and pneumonia. Risks over 38 weeks were estimated using the Kaplan-Meier estimator. Results Among 433 672 persons included in the matched vaccine groups, the median age was 69 years (IQR, 60-74 years), 93% of individuals were male, and 20% were Black. Estimated 38-week risks of adverse events were generally low after administration of either the BNT162b2 or the mRNA-1273 vaccine. Compared with the mRNA-1273 group, the BNT162b2 group had an excess per 10 000 persons of 10.9 events (95% CI, 1.9-17.4 events) of ischemic stroke, 14.8 events (95% CI, 7.9-21.8 events) of myocardial infarction, 11.3 events (95% CI, 3.4-17.7 events) of other thromboembolic events, and 17.1 events (95% CI, 8.8-30.2 events) of kidney injury. Estimates were largely similar among subgroups defined by age (<40, 40-69, and ≥70 years) and race (Black, White), but there were higher magnitudes of risk differences of ischemic stroke among older persons and White persons, kidney injury among older persons, and other thromboembolic events among Black persons. Small-magnitude differences between the 2 vaccines were seen within 42 days of the first dose, and few differences were seen within 14 days of the first dose. Conclusions and Relevance The findings of this cohort study suggest that there were few differences in risk of adverse events within 14 days of the first dose of either the BNT162b2 or the mRNA-1273 vaccine and small-magnitude differences within 42 days of the first dose. The 38-week risks of adverse events were low in both vaccine groups, although risks were lower for recipients of the mRNA-1273 vaccine than for recipients of the BNT162b2 vaccine. Although the primary analysis was designed to detect safety events unrelated to SARS-CoV-2 infection, the possibility that these differences may partially be explained by a lower effectiveness of the BNT162b2 vaccine in preventing the sequelae of SARS-CoV-2 infection compared with the mRNA-1273 vaccine could not be ruled out. These findings may help inform decision-making in future vaccination campaigns.
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Affiliation(s)
- Barbra A. Dickerman
- CAUSALab, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Arin L. Madenci
- CAUSALab, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
- Department of Surgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
| | - Hanna Gerlovin
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts
| | - Katherine E. Kurgansky
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts
| | - Jessica K. Wise
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts
| | - Michael J. Figueroa Muñiz
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts
| | - Brian R. Ferolito
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts
| | - David R. Gagnon
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts
| | - J. Michael Gaziano
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts
- Division of Aging, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
| | - Kelly Cho
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts
- Division of Aging, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
| | - Juan P. Casas
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts
- Division of Aging, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
| | - Miguel A. Hernán
- CAUSALab, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
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17
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Galloway A, Park Y, Tanukonda V, Ho YL, Nguyen XMT, Maripuri M, Dey AT, Gerlovin H, Posner D, Lynch KE, Cai T, Luoh SW, Whitbourne S, Gagnon DR, Muralidhar S, Tsao PS, Casas JP, Michael Gaziano J, Wilson PWF, Hung AM, Cho K. Impact of Coronavirus Disease 2019 (COVID-19) Severity on Long-term Events in United States Veterans Using the Veterans Affairs Severity Index for COVID-19 (VASIC). J Infect Dis 2022; 226:2113-2117. [PMID: 35512327 PMCID: PMC9129146 DOI: 10.1093/infdis/jiac182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Revised: 04/25/2022] [Accepted: 05/03/2022] [Indexed: 01/04/2023] Open
Abstract
In this retrospective cohort study of 94 595 severe acute respiratory syndrome coronavirus 2-positive cases, we developed and validated an algorithm to assess the association between coronavirus disease 2019 (COVID-19) severity and long-term complications (stroke, myocardial infarction, pulmonary embolism/deep vein thrombosis, heart failure, and mortality). COVID-19 severity was associated with a greater risk of experiencing a long-term complication 31-120 days postinfection. Most incident events occurred 31-60 days postinfection and diminished after day 91, except heart failure for severe patients and death for moderate patients, which peaked on days 91-120. Understanding the differential impact of COVID-19 severity on long-term events provides insight into possible intervention modalities and critical prevention strategies.
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Affiliation(s)
- Ashley Galloway
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, 02111, USA
| | - Yojin Park
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, 02111, USA
| | - Vidisha Tanukonda
- Atlanta VA Healthcare System, Decatur, GA, 30033, USA,Alternate contact: Vidisha Tanukonda, MD Atlanta VA Healthcare System 1670 Clairmont Road Decatur, GA 30033 Tel. (470) 786-5303
| | - Yuk-Lam Ho
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, 02111, USA
| | - Xuan-Mai T. Nguyen
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, 02111, USA
| | - Monika Maripuri
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, 02111, USA
| | - Andrew T. Dey
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, 02111, USA
| | - Hanna Gerlovin
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, 02111, USA
| | - Daniel Posner
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, 02111, USA
| | - Kristine E. Lynch
- VA Salt Lake City Healthcare System, Salt Lake City, UT, 84148, USA,Division of Epidemiology, Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, 84132, USA
| | - Tianxi Cai
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, 02111, USA,Department of Biomedical Informatics, Harvard Medical School, Boston, MA, 02115, USA,Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, 02115, MA, USA
| | - Shiuh-Wen Luoh
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR, 97239, USA,VA Portland Health Care System, Portland, OR, 97239, USA
| | - Stacey Whitbourne
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, 02111, USA,Department of Medicine, Harvard Medical School, Boston, MA, 02115, USA
| | - David R. Gagnon
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, 02111, USA,Department of Biostatistics, Boston University School of Public Health, Boston, MA, 02118, USA
| | - Sumitra Muralidhar
- Office of Research and Development, Veterans Health Administration, Washington, DC, 20571, USA
| | - Phillip S. Tsao
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, 94305, USA,VA Palo Alto Health Care System, Palo Alto, CA,94305, USA
| | - Juan P Casas
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, 02111, USA,Department of Medicine, Harvard Medical School, Boston, MA, 02115, USA,Department of Medicine, Division of Aging, Brigham & Women's Hospital, Boston, MA, 02115, USA
| | - J. Michael Gaziano
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, 02111, USA,Department of Medicine, Harvard Medical School, Boston, MA, 02115, USA,Department of Medicine, Division of Aging, Brigham & Women's Hospital, Boston, MA, 02115, USA
| | - Peter WF Wilson
- Atlanta VA Healthcare System, Decatur, GA, 30033, USA,Division of Cardiology, Emory University School of Medicine, Atlanta, GA, 30322, USA,Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA, 30322, USA
| | - Adriana M Hung
- VA Tennessee Valley Healthcare System, Nashville, TN, 37212, USA,Division of Nephrology and Hypertension, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
| | - Kelly Cho
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, 02111, USA,Department of Medicine, Harvard Medical School, Boston, MA, 02115, USA,Department of Medicine, Division of Aging, Brigham & Women's Hospital, Boston, MA, 02115, USA,Corresponding author: Kelly Cho, PhD VA Boston Healthcare System 2 Avenue De Lafayette Boston, MA 02111 Tel. (781) 400-6465 Fax (857) 364-4424
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18
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Seligman B, Charest B, Ho YL, Gerlovin H, Ward RE, Cho K, Driver JA, Gaziano JM, Gagnon DR, Orkaby AR. 30-day Mortality Following COVID-19 and Influenza Hospitalization Among US Veterans Aged 65 and Older. J Am Geriatr Soc 2022; 70:2542-2551. [PMID: 35474510 PMCID: PMC9115089 DOI: 10.1111/jgs.17828] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Revised: 03/29/2022] [Accepted: 04/09/2022] [Indexed: 11/28/2022]
Abstract
Background COVID‐19 and influenza are important sources of morbidity and mortality among older adults. Understanding how outcomes differ for older adults hospitalized with either infection is important for improving care. We compared outcomes from infection with COVID‐19 and influenza among hospitalized older adults. Methods We conducted a retrospective study of 30‐day mortality among veterans aged 65+ hospitalized with COVID‐19 from March 1, 2020–December 31, 2020 or with influenza A/B from September 1, 2017 to August 31, 2019 in Veterans Affairs Health Care System (VAHCS). COVID‐19 infection was determined by a positive PCR test and influenza by tests used in the VA system. Frailty was defined by the claims‐based Veterans Affairs Frailty Index (VA‐FI). Logistic regressions of mortality on frailty, age, and infection were adjusted for multiple confounders. Results A total of 15,474 veterans were admitted with COVID‐19 and 7867 with influenza. Mean (SD) ages were 76.1 (7.8) and 75.8 (8.3) years, 97.7% and 97.4% were male, and 66.9% and 76.4% were white in the COVID‐19 and influenza cohorts respectively. Crude 30‐day mortality (95% CI) was 18.9% (18.3%–19.5%) for COVID‐19 and 4.3% (3.8%–4.7%) for influenza. Combining cohorts, the odds ratio for 30‐day mortality from COVID‐19 (versus influenza) was 6.61 (5.74–7.65). There was a statistically significant interaction between infection with COVID‐19 and frailty, but there was no significant interaction between COVID‐19 and age. Separating cohorts, greater 30‐day mortality was significantly associated with older age (p: COVID‐19: <0.001, Influenza: <0.001) and for frail compared with robust individuals (p for trend: COVID‐19: <0.001, Influenza: <0.001). Conclusion Mortality from COVID‐19 exceeded that from influenza among hospitalized older adults. However, odds of mortality were higher at every level of frailty among those admitted with influenza compared to COVID‐19. Prevention will remain key to reducing mortality from viral illnesses among older adults.
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Affiliation(s)
- Benjamin Seligman
- New England Geriatrics Research, Education, and Clinical Center, VA Boston Health Care System, Boston, MA.,Division of Gerontology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA.,Geriatrics Research, Education, and Clinical Center, VA Greater Los Angeles Health Care System, Los Angeles, CA.,Division of Geriatric Medicine, Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA
| | - Brian Charest
- Massachusetts Veterans Epidemiology Research and Information Center, VA Boston Health Care System, Boston, MA
| | - Yuk-Lam Ho
- Massachusetts Veterans Epidemiology Research and Information Center, VA Boston Health Care System, Boston, MA
| | - Hanna Gerlovin
- Massachusetts Veterans Epidemiology Research and Information Center, VA Boston Health Care System, Boston, MA
| | - Rachel E Ward
- New England Geriatrics Research, Education, and Clinical Center, VA Boston Health Care System, Boston, MA.,Massachusetts Veterans Epidemiology Research and Information Center, VA Boston Health Care System, Boston, MA.,Department of Physical Medicine and Rehabilitation, Harvard Medical School, Boston, MA
| | - Kelly Cho
- Massachusetts Veterans Epidemiology Research and Information Center, VA Boston Health Care System, Boston, MA.,Division of Aging, Brigham & Women's Hospital, Harvard Medical School, Boston, MA
| | - Jane A Driver
- New England Geriatrics Research, Education, and Clinical Center, VA Boston Health Care System, Boston, MA.,Division of Aging, Brigham & Women's Hospital, Harvard Medical School, Boston, MA
| | - J Michael Gaziano
- Massachusetts Veterans Epidemiology Research and Information Center, VA Boston Health Care System, Boston, MA.,Division of Aging, Brigham & Women's Hospital, Harvard Medical School, Boston, MA
| | - David R Gagnon
- Massachusetts Veterans Epidemiology Research and Information Center, VA Boston Health Care System, Boston, MA.,Department of Biostatistics, Boston University School of Public Health, Boston, MA
| | - Ariela R Orkaby
- New England Geriatrics Research, Education, and Clinical Center, VA Boston Health Care System, Boston, MA.,Division of Aging, Brigham & Women's Hospital, Harvard Medical School, Boston, MA
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19
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Lu B, Posner D, Vassy JL, Ho YL, Galloway A, Raghavan S, Honerlaw J, Tarko L, Russo J, Qazi S, Orkaby AR, Tanukonda V, Djousse L, Gaziano JM, Gagnon DR, Cho K, Wilson PWF. Prediction of Cardiovascular and All-Cause Mortality After Myocardial Infarction in US Veterans. Am J Cardiol 2022; 169:10-17. [PMID: 35063273 DOI: 10.1016/j.amjcard.2021.12.036] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/25/2021] [Revised: 12/17/2021] [Accepted: 12/21/2021] [Indexed: 01/29/2023]
Abstract
Risk prediction models for cardiovascular disease (CVD) death developed from patients without vascular disease may not be suitable for myocardial infarction (MI) survivors. Prediction of mortality risk after MI may help to guide secondary prevention. Using national electronic record data from the Veterans Health Administration 2002 to 2012, we developed risk prediction models for CVD death and all-cause death based on 5-year follow-up data of 100,601 survivors of MI using Cox proportional hazards models. Model performance was evaluated using a cross-validation approach. During follow-up, there were 31,622 deaths and 12,901 CVD deaths. In men, older age, current smoking, atrial fibrillation, heart failure, peripheral artery disease, and lower body mass index were associated with greater risk of death from CVD or all-causes, and statin treatment, hypertension medication, estimated glomerular filtration rate level, and high body mass index were significantly associated with reduced risk of fatal outcomes. Similar associations and slightly different predictors were observed in women. The estimated Harrell's C-statistics of the final model versus the cross-validation estimates were 0.77 versus 0.77 in men and 0.81 versus 0.77 in women for CVD death. Similarly, the C-statistics were 0.75 versus 0.75 in men, 0.78 versus 0.75 in women for all-cause mortality. The predicted risk of death was well calibrated compared with the observed risk. In conclusion, we developed and internally validated risk prediction models of 5-year risk for CVD and all-cause death for outpatient survivors of MI. Traditional risk factors, co-morbidities, and lack of blood pressure or lipid treatment were all associated with greater risk of CVD and all-cause mortality.
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Affiliation(s)
- Bing Lu
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), Veterans Affairs, Boston Healthcare System, Boston, Massachusetts; Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts; Department of Public Health Sciences, University of Connecticut School of Medicine, Farmington, Connecticut.
| | - Daniel Posner
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), Veterans Affairs, Boston Healthcare System, Boston, Massachusetts
| | - Jason L Vassy
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), Veterans Affairs, Boston Healthcare System, Boston, Massachusetts; Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Yuk-Lam Ho
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), Veterans Affairs, Boston Healthcare System, Boston, Massachusetts
| | - Ashley Galloway
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), Veterans Affairs, Boston Healthcare System, Boston, Massachusetts
| | - Sridharan Raghavan
- Veterans Affairs Eastern Colorado Health Care System, Aurora, Colorado; Division of Hospital Medicine, Department of Medicine, University of Colorado School of Medicine, Aurora, Colorado
| | - Jacqueline Honerlaw
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), Veterans Affairs, Boston Healthcare System, Boston, Massachusetts
| | - Laura Tarko
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), Veterans Affairs, Boston Healthcare System, Boston, Massachusetts
| | - John Russo
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), Veterans Affairs, Boston Healthcare System, Boston, Massachusetts
| | - Saadia Qazi
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), Veterans Affairs, Boston Healthcare System, Boston, Massachusetts; Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Ariela R Orkaby
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), Veterans Affairs, Boston Healthcare System, Boston, Massachusetts; Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts; New England Geriatric Research, Education, and Clinical Center (GRECC), Veterans Affairs, Boston Healthcare System, Boston, Massachusetts
| | | | - Luc Djousse
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), Veterans Affairs, Boston Healthcare System, Boston, Massachusetts; Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - J Michael Gaziano
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), Veterans Affairs, Boston Healthcare System, Boston, Massachusetts; Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - David R Gagnon
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), Veterans Affairs, Boston Healthcare System, Boston, Massachusetts; Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts
| | - Kelly Cho
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), Veterans Affairs, Boston Healthcare System, Boston, Massachusetts; Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Peter W F Wilson
- Division of Cardiology, Department of Medicine, Emory University School of Medicine, Atlanta, Georgia; Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, Georgia
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20
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Shrauner W, Lord EM, Nguyen XMT, Song RJ, Galloway A, Gagnon DR, Driver JA, Gaziano JM, Wilson PWF, Djousse L, Cho K, Orkaby AR. Frailty and cardiovascular mortality in more than 3 million US Veterans. Eur Heart J 2022; 43:818-826. [PMID: 34907422 PMCID: PMC9890630 DOI: 10.1093/eurheartj/ehab850] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Revised: 06/17/2021] [Accepted: 11/29/2021] [Indexed: 02/05/2023] Open
Abstract
AIMS Frailty is associated with an increased risk of all-cause mortality and cardiovascular (CV) events. Limited data exist from the modern era of CV prevention on the relationship between frailty and CV mortality. We hypothesized that frailty is associated with an increased risk of CV mortality. METHODS AND RESULTS All US Veterans aged ≥65 years who were regular users of Veteran Affairs care from 2002 to 2017 were included. Frailty was defined using a 31-item previously validated frailty index, ranging from 0 to 1. The primary outcome was CV mortality with secondary analyses examining the relationship between frailty and CV events (myocardial infarction, stroke, revascularization). Survival analysis models were adjusted for age, sex, ethnicity, geographic region, smoking, hyperlipidaemia, statin use, and blood pressure medication use. There were 3 068 439 US Veterans included in the analysis. Mean age was 74.1 ± 5.8 years in 2002, 76.0 ± 8.3 years in 2014, 98% male, and 87.5% White. In 2002, the median (interquartile range) frailty score was 0.16 (0.10-0.23). This increased and stabilized to 0.19 (0.10-0.32) for 2006-14. The presence of frailty was associated with an increased risk of CV mortality at every stage of frailty. Frailty was associated with an increased risk of myocardial infarction and stroke, but not revascularization. CONCLUSION In this population, both the presence and severity of frailty are tightly correlated with CV death, independent of underlying CV disease. This study is the largest and most contemporary evaluation of the relationship between frailty and CV mortality to date. Further work is needed to understand how this risk can be diminished. KEY QUESTION Can an electronic frailty index identify adults aged 65 and older who are at risk of CV mortality and major CV events? KEY FINDING Among 3 068 439 US Veterans aged 65 and older, frailty was associated with an increased risk of CV mortality at every level of frailty. Frailty was also associated with an increased risk of myocardial infarction and stroke, but not revascularization. TAKE HOME MESSAGE Both the presence and severity of frailty are associated with CV mortality and major CV events, independent of underlying CV disease.
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Affiliation(s)
- William Shrauner
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, 150 South Huntington Ave, Boston, MA 02130, USA
- Division of Aging, Department of Medicine, Brigham & Women's Hospital, Harvard Medical School, 1620 Tremont St Boston, MA 02120, USA
- Division of Cardiology, Department of Medicine, Boston Medical Center, One Boston Medical Center Pl, Boston, MA 02118, USA
| | - Emily M Lord
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, 150 South Huntington Ave, Boston, MA 02130, USA
| | - Xuan-Mai T Nguyen
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, 150 South Huntington Ave, Boston, MA 02130, USA
| | - Rebecca J Song
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, 150 South Huntington Ave, Boston, MA 02130, USA
| | - Ashley Galloway
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, 150 South Huntington Ave, Boston, MA 02130, USA
| | - David R Gagnon
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, 150 South Huntington Ave, Boston, MA 02130, USA
- New England GRECC (Geriatric Research, Education, and Clinical Center) VA Boston Healthcare System, 150 South Huntington Ave Boston, MA 02130, USA
| | - Jane A Driver
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, 150 South Huntington Ave, Boston, MA 02130, USA
- Division of Aging, Department of Medicine, Brigham & Women's Hospital, Harvard Medical School, 1620 Tremont St Boston, MA 02120, USA
- New England GRECC (Geriatric Research, Education, and Clinical Center) VA Boston Healthcare System, 150 South Huntington Ave Boston, MA 02130, USA
| | - J Michael Gaziano
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, 150 South Huntington Ave, Boston, MA 02130, USA
- Division of Aging, Department of Medicine, Brigham & Women's Hospital, Harvard Medical School, 1620 Tremont St Boston, MA 02120, USA
| | - Peter W F Wilson
- Atlanta VA Medical Center, 1670 Clairmont Rd, Decatur, GA 30033, USA
- Division of Cardiology, Department of Medicine, Emory University School of Medicine, 1525 Clifton Rd, Atlanta, GA 30322, USA
- Department of Epidemiology, Rollins School of Public Health, Emory University, 1518 Clifton Rd, Atlanta, GA 30322, USA
| | - Luc Djousse
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, 150 South Huntington Ave, Boston, MA 02130, USA
- Division of Aging, Department of Medicine, Brigham & Women's Hospital, Harvard Medical School, 1620 Tremont St Boston, MA 02120, USA
| | - Kelly Cho
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, 150 South Huntington Ave, Boston, MA 02130, USA
- Division of Aging, Department of Medicine, Brigham & Women's Hospital, Harvard Medical School, 1620 Tremont St Boston, MA 02120, USA
| | - Ariela R Orkaby
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, 150 South Huntington Ave, Boston, MA 02130, USA
- Division of Aging, Department of Medicine, Brigham & Women's Hospital, Harvard Medical School, 1620 Tremont St Boston, MA 02120, USA
- New England GRECC (Geriatric Research, Education, and Clinical Center) VA Boston Healthcare System, 150 South Huntington Ave Boston, MA 02130, USA
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21
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Quach LT, Cho K, Driver JA, Ward R, Spiro A, Dugan E, Gaziano MJ, Djousse L, Rudolph JL, Gagnon DR. Social Characteristics, Health, and Mortality Among Male Centenarians Using Veterans Affairs (VA) Health Care. Res Aging 2022; 44:136-143. [PMID: 33779393 PMCID: PMC10756333 DOI: 10.1177/01640275211000724] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
We studied male centenarian Veterans using VA health care to understand the impact of social characteristics on their annual mortality rate, adjusting for prevalent health conditions. This longitudinal study used VA Electronic Health Record data from 1997 to 2012 (n = 1,858). Covariates included age, race, marital status, and periods of military service. The mean age was 100.4 ± 1.4 years, 76% were white, and 49% were married. The average annual mortality rate was 32 per 100 person-years. The annual mortality rate was stable and not affected by race but did vary by marital status. Divorced or separated centenarians had a 21% higher rate of death than married centenarians. A diagnosis of dementia or of congestive heart failure each increased the mortality risk by 37%. Providers should consider prevalent health conditions, as well as marital status, in managing care of centenarian Veterans.
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Affiliation(s)
- Lien T. Quach
- VA Boston Healthcare System, Massachusetts Veterans Epidemiology Research and Information Center, MA, USA
- Department of Gerontology, The University of Massachusetts Boston, MA, USA
- Providence VA Medical Center, Providence, RI, USA
| | - Kelly Cho
- VA Boston Healthcare System, Massachusetts Veterans Epidemiology Research and Information Center, MA, USA
- Division of Aging, Brigham and Women’s Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Jane A. Driver
- VA Boston Healthcare System, Massachusetts Veterans Epidemiology Research and Information Center, MA, USA
- Providence VA Medical Center, Providence, RI, USA
- Division of Aging, Brigham and Women’s Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Rachel Ward
- VA Boston Healthcare System, Massachusetts Veterans Epidemiology Research and Information Center, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Avron Spiro
- VA Boston Healthcare System, Massachusetts Veterans Epidemiology Research and Information Center, MA, USA
- Brown University, Providence, RI, USA
| | - Elizabeth Dugan
- Department of Gerontology, The University of Massachusetts Boston, MA, USA
| | - Michael J. Gaziano
- VA Boston Healthcare System, Massachusetts Veterans Epidemiology Research and Information Center, MA, USA
- Division of Aging, Brigham and Women’s Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Luc Djousse
- VA Boston Healthcare System, Massachusetts Veterans Epidemiology Research and Information Center, MA, USA
- Division of Aging, Brigham and Women’s Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - James L. Rudolph
- Providence VA Medical Center, Providence, RI, USA
- Brown University, Providence, RI, USA
| | - David R. Gagnon
- VA Boston Healthcare System, Massachusetts Veterans Epidemiology Research and Information Center, MA, USA
- Brown University, Providence, RI, USA
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22
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Dickerman BA, Gerlovin H, Madenci AL, Kurgansky KE, Ferolito BR, Figueroa Muñiz MJ, Gagnon DR, Gaziano JM, Cho K, Casas JP, Hernán MA. Comparative Effectiveness of BNT162b2 and mRNA-1273 Vaccines in U.S. Veterans. N Engl J Med 2022; 386:105-115. [PMID: 34942066 PMCID: PMC8693691 DOI: 10.1056/nejmoa2115463] [Citation(s) in RCA: 151] [Impact Index Per Article: 75.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
BACKGROUND The messenger RNA (mRNA)-based vaccines BNT162b2 and mRNA-1273 are more than 90% effective against coronavirus disease 2019 (Covid-19). However, their comparative effectiveness for a range of outcomes across diverse populations is unknown. METHODS We emulated a target trial using the electronic health records of U.S. veterans who received a first dose of the BNT162b2 or mRNA-1273 vaccine between January 4 and May 14, 2021, during a period marked by predominance of the SARS-CoV-2 B.1.1.7 (alpha) variant. We matched recipients of each vaccine in a 1:1 ratio according to their risk factors. Outcomes included documented severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, symptomatic Covid-19, hospitalization for Covid-19, admission to an intensive care unit (ICU) for Covid-19, and death from Covid-19. We estimated risks using the Kaplan-Meier estimator. To assess the influence of the B.1.617.2 (delta) variant, we emulated a second target trial that involved veterans vaccinated between July 1 and September 20, 2021. RESULTS Each vaccine group included 219,842 persons. Over 24 weeks of follow-up in a period marked by alpha-variant predominance, the estimated risk of documented infection was 5.75 events per 1000 persons (95% confidence interval [CI], 5.39 to 6.23) in the BNT162b2 group and 4.52 events per 1000 persons (95% CI, 4.17 to 4.84) in the mRNA-1273 group. The excess number of events per 1000 persons for BNT162b2 as compared with mRNA-1273 was 1.23 (95% CI, 0.72 to 1.81) for documented infection, 0.44 (95% CI, 0.25 to 0.70) for symptomatic Covid-19, 0.55 (95% CI, 0.36 to 0.83) for hospitalization for Covid-19, 0.10 (95% CI, 0.00 to 0.26) for ICU admission for Covid-19, and 0.02 (95% CI, -0.06 to 0.12) for death from Covid-19. The corresponding excess risk (BNT162b2 vs. mRNA-1273) of documented infection over 12 weeks of follow-up in a period marked by delta-variant predominance was 6.54 events per 1000 persons (95% CI, -2.58 to 11.82). CONCLUSIONS The 24-week risk of Covid-19 outcomes was low after vaccination with mRNA-1273 or BNT162b2, although risks were lower with mRNA-1273 than with BNT162b2. This pattern was consistent across periods marked by alpha- and delta-variant predominance. (Funded by the Department of Veterans Affairs and others.).
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Affiliation(s)
- Barbra A Dickerman
- From CAUSALab (B.A.D., A.L.M., M.A.H.) and the Departments of Epidemiology (B.A.D., A.L.M., M.A.H.) and Biostatistics (M.A.H.), Harvard T.H. Chan School of Public Health, Massachusetts Veterans Epidemiology Research and Information Center, Veterans Affairs Boston Healthcare System (H.G., K.E.K., B.R.F., M.J.F.M., D.R.G., J.M.G., K.C., J.P.C.), the Department of Surgery (A.L.M.) and Division of Aging (J.M.G., K.C., J.P.C.), Brigham and Women's Hospital, Harvard Medical School, and the Department of Biostatistics (M.J.F.M., D.R.G.), Boston University School of Public Health - all in Boston
| | - Hanna Gerlovin
- From CAUSALab (B.A.D., A.L.M., M.A.H.) and the Departments of Epidemiology (B.A.D., A.L.M., M.A.H.) and Biostatistics (M.A.H.), Harvard T.H. Chan School of Public Health, Massachusetts Veterans Epidemiology Research and Information Center, Veterans Affairs Boston Healthcare System (H.G., K.E.K., B.R.F., M.J.F.M., D.R.G., J.M.G., K.C., J.P.C.), the Department of Surgery (A.L.M.) and Division of Aging (J.M.G., K.C., J.P.C.), Brigham and Women's Hospital, Harvard Medical School, and the Department of Biostatistics (M.J.F.M., D.R.G.), Boston University School of Public Health - all in Boston
| | - Arin L Madenci
- From CAUSALab (B.A.D., A.L.M., M.A.H.) and the Departments of Epidemiology (B.A.D., A.L.M., M.A.H.) and Biostatistics (M.A.H.), Harvard T.H. Chan School of Public Health, Massachusetts Veterans Epidemiology Research and Information Center, Veterans Affairs Boston Healthcare System (H.G., K.E.K., B.R.F., M.J.F.M., D.R.G., J.M.G., K.C., J.P.C.), the Department of Surgery (A.L.M.) and Division of Aging (J.M.G., K.C., J.P.C.), Brigham and Women's Hospital, Harvard Medical School, and the Department of Biostatistics (M.J.F.M., D.R.G.), Boston University School of Public Health - all in Boston
| | - Katherine E Kurgansky
- From CAUSALab (B.A.D., A.L.M., M.A.H.) and the Departments of Epidemiology (B.A.D., A.L.M., M.A.H.) and Biostatistics (M.A.H.), Harvard T.H. Chan School of Public Health, Massachusetts Veterans Epidemiology Research and Information Center, Veterans Affairs Boston Healthcare System (H.G., K.E.K., B.R.F., M.J.F.M., D.R.G., J.M.G., K.C., J.P.C.), the Department of Surgery (A.L.M.) and Division of Aging (J.M.G., K.C., J.P.C.), Brigham and Women's Hospital, Harvard Medical School, and the Department of Biostatistics (M.J.F.M., D.R.G.), Boston University School of Public Health - all in Boston
| | - Brian R Ferolito
- From CAUSALab (B.A.D., A.L.M., M.A.H.) and the Departments of Epidemiology (B.A.D., A.L.M., M.A.H.) and Biostatistics (M.A.H.), Harvard T.H. Chan School of Public Health, Massachusetts Veterans Epidemiology Research and Information Center, Veterans Affairs Boston Healthcare System (H.G., K.E.K., B.R.F., M.J.F.M., D.R.G., J.M.G., K.C., J.P.C.), the Department of Surgery (A.L.M.) and Division of Aging (J.M.G., K.C., J.P.C.), Brigham and Women's Hospital, Harvard Medical School, and the Department of Biostatistics (M.J.F.M., D.R.G.), Boston University School of Public Health - all in Boston
| | - Michael J Figueroa Muñiz
- From CAUSALab (B.A.D., A.L.M., M.A.H.) and the Departments of Epidemiology (B.A.D., A.L.M., M.A.H.) and Biostatistics (M.A.H.), Harvard T.H. Chan School of Public Health, Massachusetts Veterans Epidemiology Research and Information Center, Veterans Affairs Boston Healthcare System (H.G., K.E.K., B.R.F., M.J.F.M., D.R.G., J.M.G., K.C., J.P.C.), the Department of Surgery (A.L.M.) and Division of Aging (J.M.G., K.C., J.P.C.), Brigham and Women's Hospital, Harvard Medical School, and the Department of Biostatistics (M.J.F.M., D.R.G.), Boston University School of Public Health - all in Boston
| | - David R Gagnon
- From CAUSALab (B.A.D., A.L.M., M.A.H.) and the Departments of Epidemiology (B.A.D., A.L.M., M.A.H.) and Biostatistics (M.A.H.), Harvard T.H. Chan School of Public Health, Massachusetts Veterans Epidemiology Research and Information Center, Veterans Affairs Boston Healthcare System (H.G., K.E.K., B.R.F., M.J.F.M., D.R.G., J.M.G., K.C., J.P.C.), the Department of Surgery (A.L.M.) and Division of Aging (J.M.G., K.C., J.P.C.), Brigham and Women's Hospital, Harvard Medical School, and the Department of Biostatistics (M.J.F.M., D.R.G.), Boston University School of Public Health - all in Boston
| | - J Michael Gaziano
- From CAUSALab (B.A.D., A.L.M., M.A.H.) and the Departments of Epidemiology (B.A.D., A.L.M., M.A.H.) and Biostatistics (M.A.H.), Harvard T.H. Chan School of Public Health, Massachusetts Veterans Epidemiology Research and Information Center, Veterans Affairs Boston Healthcare System (H.G., K.E.K., B.R.F., M.J.F.M., D.R.G., J.M.G., K.C., J.P.C.), the Department of Surgery (A.L.M.) and Division of Aging (J.M.G., K.C., J.P.C.), Brigham and Women's Hospital, Harvard Medical School, and the Department of Biostatistics (M.J.F.M., D.R.G.), Boston University School of Public Health - all in Boston
| | - Kelly Cho
- From CAUSALab (B.A.D., A.L.M., M.A.H.) and the Departments of Epidemiology (B.A.D., A.L.M., M.A.H.) and Biostatistics (M.A.H.), Harvard T.H. Chan School of Public Health, Massachusetts Veterans Epidemiology Research and Information Center, Veterans Affairs Boston Healthcare System (H.G., K.E.K., B.R.F., M.J.F.M., D.R.G., J.M.G., K.C., J.P.C.), the Department of Surgery (A.L.M.) and Division of Aging (J.M.G., K.C., J.P.C.), Brigham and Women's Hospital, Harvard Medical School, and the Department of Biostatistics (M.J.F.M., D.R.G.), Boston University School of Public Health - all in Boston
| | - Juan P Casas
- From CAUSALab (B.A.D., A.L.M., M.A.H.) and the Departments of Epidemiology (B.A.D., A.L.M., M.A.H.) and Biostatistics (M.A.H.), Harvard T.H. Chan School of Public Health, Massachusetts Veterans Epidemiology Research and Information Center, Veterans Affairs Boston Healthcare System (H.G., K.E.K., B.R.F., M.J.F.M., D.R.G., J.M.G., K.C., J.P.C.), the Department of Surgery (A.L.M.) and Division of Aging (J.M.G., K.C., J.P.C.), Brigham and Women's Hospital, Harvard Medical School, and the Department of Biostatistics (M.J.F.M., D.R.G.), Boston University School of Public Health - all in Boston
| | - Miguel A Hernán
- From CAUSALab (B.A.D., A.L.M., M.A.H.) and the Departments of Epidemiology (B.A.D., A.L.M., M.A.H.) and Biostatistics (M.A.H.), Harvard T.H. Chan School of Public Health, Massachusetts Veterans Epidemiology Research and Information Center, Veterans Affairs Boston Healthcare System (H.G., K.E.K., B.R.F., M.J.F.M., D.R.G., J.M.G., K.C., J.P.C.), the Department of Surgery (A.L.M.) and Division of Aging (J.M.G., K.C., J.P.C.), Brigham and Women's Hospital, Harvard Medical School, and the Department of Biostatistics (M.J.F.M., D.R.G.), Boston University School of Public Health - all in Boston
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Gerlovin H, Posner DC, Ho YL, Rentsch CT, Tate JP, King JT, Kurgansky KE, Danciu I, Costa L, Linares FA, Goethert ID, Jacobson DA, Freiberg MS, Begoli E, Muralidhar S, Ramoni RB, Tourassi G, Gaziano JM, Justice AC, Gagnon DR, Cho K. Pharmacoepidemiology, Machine Learning, and COVID-19: An Intent-to-Treat Analysis of Hydroxychloroquine, With or Without Azithromycin, and COVID-19 Outcomes Among Hospitalized US Veterans. Am J Epidemiol 2021; 190:2405-2419. [PMID: 34165150 PMCID: PMC8384407 DOI: 10.1093/aje/kwab183] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Revised: 06/03/2021] [Accepted: 06/17/2021] [Indexed: 12/11/2022] Open
Abstract
Hydroxychloroquine (HCQ) was proposed as an early therapy for coronavirus disease
2019 (COVID-19) after in vitro studies indicated possible
benefit. Previous in vivo observational studies have presented
conflicting results, though recent randomized clinical trials have reported no
benefit from HCQ amongst hospitalized COVID-19 patients. We examined the effects
of HCQ alone, and in combination with azithromycin, in a hospitalized COVID-19
positive, United States (US) Veteran population using a propensity score
adjusted survival analysis with imputation of missing data. From March 1, 2020
through April 30, 2020, 64,055 US Veterans were tested for COVID-19 based on
Veteran Affairs Healthcare Administration electronic health record data. Of the
7,193 positive cases, 2,809 were hospitalized, and 657 individuals were
prescribed HCQ within the first 48-hours of hospitalization for the treatment of
COVID-19. There was no apparent benefit associated with HCQ receipt, alone or in
combination with azithromycin, and an increased risk of intubation when used in
combination with azithromycin [Hazard Ratio (95% Confidence Interval):
1.55 (1.07, 2.24)]. In conclusion, we assessed the effectiveness of HCQ with or
without azithromycin in treating patients hospitalized with COVID-19 using a
national sample of the US Veteran population. Using rigorous study design and
analytic methods to reduce confounding and bias, we found no evidence of a
survival benefit from the administration of HCQ.
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24
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Ward RE, Orkaby AR, Dumontier C, Charest B, Hawley CE, Yaksic E, Quach L, Kim DH, Gagnon DR, Gaziano JM, Cho K, Djousse L, Driver JA. Trajectories of Frailty in the 5 Years Prior to Death Among U.S. Veterans Born 1927-1934. J Gerontol A Biol Sci Med Sci 2021; 76:e347-e353. [PMID: 34244759 PMCID: PMC8825219 DOI: 10.1093/gerona/glab196] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Indexed: 11/14/2022] Open
Abstract
BACKGROUND Electronic frailty indices (eFIs) are increasingly used to identify patients at risk for morbidity and mortality. Whether eFIs capture the spectrum of frailty change, including decline, stability, and improvement, is unknown. METHODS In a nationwide retrospective birth cohort of U.S. Veterans, a validated eFI, including 31 health deficits, was calculated annually using medical record and insurance claims data (2002-2012). K-means clustering was used to assign patients into frailty trajectories measured 5 years prior to death. RESULTS There were 214 250 veterans born between 1927 and 1934 (mean [SD] age at death = 79.4 [2.8] years, 99.2% male, 90.3% White) with an annual eFI in the 5 years before death. Nine frailty trajectories were identified. Those starting at nonfrail or prefrail had 2 stable trajectories (nonfrail to prefrail, n = 29 786 and stable prefrail, n = 28 499) and 2 rapidly increasing trajectories (prefrail to moderately frail, n = 28 244 and prefrail to severely frail, n = 22 596). Those who were mildly frail at baseline included 1 gradually increasing trajectory (mildly to moderately frail, n = 33 806) and 1 rapidly increasing trajectory (mildly to severely frail, n = 15 253). Trajectories that started at moderately or severely frail included 2 gradually increasing trajectories (moderately to severely frail, n = 27 662 and progressing severely frail, n = 14 478) and 1 recovering trajectory (moderately frail to mildly frail, n = 13 926). CONCLUSIONS Nine frailty trajectories, including 1 recovering trajectory, were identified in this cohort of older U.S. Veterans. Future work is needed to understand whether prevention and treatment strategies can improve frailty trajectories and contribute to compression of morbidity toward the end of life.
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Affiliation(s)
- Rachel E Ward
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston HealthCare System, USA
- New England Geriatric Research, Education, and Clinical Center (GRECC), VA Boston HealthCare System, Massachusetts, USA
- Department of Physical Medicine and Rehabilitation, Harvard Medical School, Boston, Massachusetts, USA
| | - Ariela R Orkaby
- New England Geriatric Research, Education, and Clinical Center (GRECC), VA Boston HealthCare System, Massachusetts, USA
| | - Clark Dumontier
- New England Geriatric Research, Education, and Clinical Center (GRECC), VA Boston HealthCare System, Massachusetts, USA
- Division of Aging, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Hinda and Arthur Marcus Institute for Aging Research, Hebrew SeniorLife, Boston, Massachusetts, USA
| | - Brian Charest
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston HealthCare System, USA
| | - Chelsea E Hawley
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston HealthCare System, USA
| | - Enzo Yaksic
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston HealthCare System, USA
| | - Lien Quach
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston HealthCare System, USA
- Department of Gerontology, University of Massachusetts Boston, USA
| | - Dae H Kim
- Hinda and Arthur Marcus Institute for Aging Research, Hebrew SeniorLife, Boston, Massachusetts, USA
| | - David R Gagnon
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston HealthCare System, USA
- Boston University School of Public Health Department of Biostatistics, Massachusetts, USA
| | - J Michael Gaziano
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston HealthCare System, USA
- Division of Aging, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Kelly Cho
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston HealthCare System, USA
- Division of Aging, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Luc Djousse
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston HealthCare System, USA
- Division of Aging, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Jane A Driver
- New England Geriatric Research, Education, and Clinical Center (GRECC), VA Boston HealthCare System, Massachusetts, USA
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston HealthCare System, USA
- Division of Aging, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
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25
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Cho K, Keithly SC, Kurgansky KE, Madenci AL, Gerlovin H, Marucci-Wellman H, Doubleday A, Thomas ER, Park Y, Ho YL, Sugimoto JD, Moore KP, Peterson AC, Hoag C, Gupta K, Jeans K, Klote M, Ramoni R, Huang GD, Casas JP, Gagnon DR, Hernán MA, Smith NL, Gaziano JM. Early Convalescent Plasma Therapy and Mortality Among US Veterans Hospitalized With Nonsevere COVID-19: An Observational Analysis Emulating a Target Trial. J Infect Dis 2021; 224:967-975. [PMID: 34153099 PMCID: PMC8411382 DOI: 10.1093/infdis/jiab330] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2021] [Accepted: 06/18/2021] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND Early convalescent plasma transfusion may reduce mortality in patients with nonsevere coronavirus disease 2019 (COVID-19). METHODS This study emulates a (hypothetical) target trial using observational data from a cohort of US veterans admitted to a Department of Veterans Affairs (VA) facility between 1 May and 17 November 2020 with nonsevere COVID-19. The intervention was convalescent plasma initiated within 2 days of eligibility. Thirty-day mortality was compared using cumulative incidence curves, risk differences, and hazard ratios estimated from pooled logistic models with inverse probability weighting to adjust for confounding. RESULTS Of 11 269 eligible person-trials contributed by 4755 patients, 402 trials were assigned to the convalescent plasma group. Forty and 671 deaths occurred within the plasma and nonplasma groups, respectively. The estimated 30-day mortality risk was 6.5% (95% confidence interval [CI], 4.0%-9.7%) in the plasma group and 6.2% (95% CI, 5.6%-7.0%) in the nonplasma group. The associated risk difference was 0.30% (95% CI, -2.30% to 3.60%) and the hazard ratio was 1.04 (95% CI, .64-1.62). CONCLUSIONS Our target trial emulation estimated no meaningful differences in 30-day mortality between nonsevere COVID-19 patients treated and untreated with convalescent plasma. Clinical Trials Registration. NCT04545047.
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Affiliation(s)
- Kelly Cho
- Massachusetts Veterans Epidemiology Research and
Information Center, Department of Veterans Affairs Office of Research and
Development, Boston, Massachusetts,
USA
- Department of Medicine, Brigham and Women’s
Hospital, Harvard Medical School, Boston,
Massachusetts, USA
- Correspondence: Kelly Cho, PhD, VA Boston Healthcare System, 150 S.
Huntington Avenue, Boston, MA 02130 ()
| | - Sarah C Keithly
- Seattle Epidemiologic Research and Information Center,
Department of Veterans Affairs Office of Research and Development,
Seattle, Washington, USA
| | - Katherine E Kurgansky
- Massachusetts Veterans Epidemiology Research and
Information Center, Department of Veterans Affairs Office of Research and
Development, Boston, Massachusetts,
USA
| | - Arin L Madenci
- Departments of Epidemiology and Biostatistics, Harvard T.
H. Chan School of Public Health, Boston,
Massachusetts, USA
| | - Hanna Gerlovin
- Massachusetts Veterans Epidemiology Research and
Information Center, Department of Veterans Affairs Office of Research and
Development, Boston, Massachusetts,
USA
| | - Helen Marucci-Wellman
- Massachusetts Veterans Epidemiology Research and
Information Center, Department of Veterans Affairs Office of Research and
Development, Boston, Massachusetts,
USA
| | - Annie Doubleday
- Seattle Epidemiologic Research and Information Center,
Department of Veterans Affairs Office of Research and Development,
Seattle, Washington, USA
| | - Eva R Thomas
- Seattle Epidemiologic Research and Information Center,
Department of Veterans Affairs Office of Research and Development,
Seattle, Washington, USA
| | - Yojin Park
- Massachusetts Veterans Epidemiology Research and
Information Center, Department of Veterans Affairs Office of Research and
Development, Boston, Massachusetts,
USA
| | - Yuk-Lam Ho
- Massachusetts Veterans Epidemiology Research and
Information Center, Department of Veterans Affairs Office of Research and
Development, Boston, Massachusetts,
USA
| | - Jonathan D Sugimoto
- Seattle Epidemiologic Research and Information Center,
Department of Veterans Affairs Office of Research and Development,
Seattle, Washington, USA
- Department of Epidemiology, School of Public Health,
University of Washington, Seattle,
Washington, USA
- Vaccine and Infectious Disease Division, Fred Hutchinson
Cancer Research Center, Seattle,
Washington, USA
| | - Kathryn P Moore
- Seattle Epidemiologic Research and Information Center,
Department of Veterans Affairs Office of Research and Development,
Seattle, Washington, USA
| | - Alexander C Peterson
- Seattle Epidemiologic Research and Information Center,
Department of Veterans Affairs Office of Research and Development,
Seattle, Washington, USA
| | - Constance Hoag
- Massachusetts Veterans Epidemiology Research and
Information Center, Department of Veterans Affairs Office of Research and
Development, Boston, Massachusetts,
USA
| | - Kalpana Gupta
- Boston Healthcare System,
Department of Veterans Affairs, Boston, Massachusetts,
USA
- Department of Medicine, Boston University School of
Medicine, Boston, Massachusetts, USA
| | - Karen Jeans
- Department of Veterans Affairs Office of Research and
Development, Washington, District of
Columbia, USA
| | - Molly Klote
- Department of Veterans Affairs Office of Research and
Development, Washington, District of
Columbia, USA
| | - Rachel Ramoni
- Department of Veterans Affairs Office of Research and
Development, Washington, District of
Columbia, USA
| | - Grant D Huang
- Department of Veterans Affairs Office of Research and
Development, Washington, District of
Columbia, USA
| | - Juan P Casas
- Massachusetts Veterans Epidemiology Research and
Information Center, Department of Veterans Affairs Office of Research and
Development, Boston, Massachusetts,
USA
- Department of Medicine, Brigham and Women’s
Hospital, Harvard Medical School, Boston,
Massachusetts, USA
| | - David R Gagnon
- Massachusetts Veterans Epidemiology Research and
Information Center, Department of Veterans Affairs Office of Research and
Development, Boston, Massachusetts,
USA
- Department of Biostatistics, Boston University School of
Public Health, Boston, Massachusetts,
USA
| | - Miguel A Hernán
- Departments of Epidemiology and Biostatistics, Harvard T.
H. Chan School of Public Health, Boston,
Massachusetts, USA
| | - Nicholas L Smith
- Seattle Epidemiologic Research and Information Center,
Department of Veterans Affairs Office of Research and Development,
Seattle, Washington, USA
- Department of Epidemiology, School of Public Health,
University of Washington, Seattle,
Washington, USA
| | - J Michael Gaziano
- Massachusetts Veterans Epidemiology Research and
Information Center, Department of Veterans Affairs Office of Research and
Development, Boston, Massachusetts,
USA
- Department of Medicine, Brigham and Women’s
Hospital, Harvard Medical School, Boston,
Massachusetts, USA
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26
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Gaziano L, Cho K, Djousse L, Schubert P, Galloway A, Ho YL, Kurgansky K, Gagnon DR, Russo JP, Di Angelantonio E, Wood AM, Danesh J, Gaziano JM, Butterworth AS, Wilson PWF, Joseph J. Risk factors and prediction models for incident heart failure with reduced and preserved ejection fraction. ESC Heart Fail 2021; 8:4893-4903. [PMID: 34528757 PMCID: PMC8712836 DOI: 10.1002/ehf2.13429] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Revised: 04/28/2021] [Accepted: 05/02/2021] [Indexed: 01/09/2023] Open
Abstract
Aims This study aims to develop the first race‐specific and sex‐specific risk prediction models for heart failure with preserved (HFpEF) and reduced ejection fraction (HFrEF). Methods and results We created a cohort of 1.8 million individuals who had an outpatient clinic visit between 2002 and 2007 within the Veterans Affairs (VA) Healthcare System and obtained information on HFpEF, HFrEF, and several risk factors from electronic health records (EHR). Variables were selected for the risk prediction models in a ‘derivation cohort’ that consisted of individuals with baseline date in 2002, 2003, or 2004 using a forward stepwise selection based on a change in C‐index threshold. Discrimination and calibration were assessed in the remaining participants (internal ‘validation cohort’). A total of 66 831 individuals developed HFpEF, and 92 233 developed HFrEF (52 679 and 71 463 in the derivation cohort) over a median of 11.1 years of follow‐up. The HFpEF risk prediction model included age, diabetes, BMI, COPD, previous MI, antihypertensive treatment, SBP, smoking status, atrial fibrillation, and estimated glomerular filtration rate (eGFR), while the HFrEF model additionally included previous CAD. For the HFpEF model, C‐indices were 0.74 (SE = 0.002) for white men, 0.76 (0.005) for black men, 0.79 (0.015) for white women, and 0.77 (0.026) for black women, compared with 0.72 (0.002), 0.72 (0.004), 0.77 (0.017), and 0.75 (0.028), respectively, for the HFrEF model. These risk prediction models were generally well calibrated in each race‐specific and sex‐specific stratum of the validation cohort. Conclusions Our race‐specific and sex‐specific risk prediction models, which used easily obtainable clinical variables, can be a useful tool to implement preventive strategies or subtype‐specific prevention trials in the nine million users of the VA healthcare system and the general population after external validation.
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Affiliation(s)
- Liam Gaziano
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), Cardiology Section, VA Boston Healthcare System, 1400 VFW Parkway, West Roxbury, Boston, MA, 02132, USA.,BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Kelly Cho
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), Cardiology Section, VA Boston Healthcare System, 1400 VFW Parkway, West Roxbury, Boston, MA, 02132, USA.,Department of Medicine, Division of Aging, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Luc Djousse
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), Cardiology Section, VA Boston Healthcare System, 1400 VFW Parkway, West Roxbury, Boston, MA, 02132, USA.,Department of Medicine, Division of Aging, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Petra Schubert
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), Cardiology Section, VA Boston Healthcare System, 1400 VFW Parkway, West Roxbury, Boston, MA, 02132, USA
| | - Ashley Galloway
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), Cardiology Section, VA Boston Healthcare System, 1400 VFW Parkway, West Roxbury, Boston, MA, 02132, USA
| | - Yuk-Lam Ho
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), Cardiology Section, VA Boston Healthcare System, 1400 VFW Parkway, West Roxbury, Boston, MA, 02132, USA
| | - Katherine Kurgansky
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), Cardiology Section, VA Boston Healthcare System, 1400 VFW Parkway, West Roxbury, Boston, MA, 02132, USA
| | - David R Gagnon
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), Cardiology Section, VA Boston Healthcare System, 1400 VFW Parkway, West Roxbury, Boston, MA, 02132, USA.,Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - John P Russo
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), Cardiology Section, VA Boston Healthcare System, 1400 VFW Parkway, West Roxbury, Boston, MA, 02132, USA.,Landmark College, Putney, VT, USA
| | - Emanuele Di Angelantonio
- BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Angela M Wood
- BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - John Danesh
- BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - John Michael Gaziano
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), Cardiology Section, VA Boston Healthcare System, 1400 VFW Parkway, West Roxbury, Boston, MA, 02132, USA.,Department of Medicine, Division of Aging, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Adam S Butterworth
- BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Peter W F Wilson
- Atlanta VA Medical Center, Decatur, GA, USA.,Department of Medicine, Division of Cardiovascular Disease, Emory University School of Medicine, Atlanta, GA, USA
| | - Jacob Joseph
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), Cardiology Section, VA Boston Healthcare System, 1400 VFW Parkway, West Roxbury, Boston, MA, 02132, USA.,Department of Medicine, Division of Cardiovascular Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
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27
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Robinson SA, Cooper JA, Goldstein RL, Polak M, Cruz Rivera PN, Gagnon DR, Samuelson A, Moore S, Kadri R, Richardson CR, Moy ML. A randomised trial of a web-based physical activity self-management intervention in COPD. ERJ Open Res 2021; 7:00158-2021. [PMID: 34476247 PMCID: PMC8405869 DOI: 10.1183/23120541.00158-2021] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Accepted: 06/15/2021] [Indexed: 11/21/2022] Open
Abstract
Improving exercise capacity is a primary objective in COPD. Declines in exercise capacity result in reduced physical activity and health-related quality of life (HRQoL). Self-management interventions can teach patients skills and behaviours to manage their disease. Technology-mediated interventions have the potential to provide easily accessible support for disease self-management. We evaluated the effectiveness of a web-based self-management intervention, focused on physical activity promotion, on exercise capacity in COPD. This 6-month randomised controlled trial (NCT02099799) enrolled 153 persons with COPD at two US sites (VABoston, n=108; VABirmingham, n=45). Participants were allocated (1:1) to the web-based self-management intervention (physical activity promotion through personalised, progressive step-count goals, feedback, online COPD-related education and social support via an online community) or usual care. The primary outcome was exercise capacity (6-min walk distance (6 MWD)). Secondary outcomes included physical activity (daily steps per day), HRQoL (St. George's Respiratory Questionnaire Total Score), dyspnoea, COPD-related knowledge and social support. Change in step-count goals reflected intervention engagement. Participants' mean age was 69 (sd=7), and mean forced expiratory volume in 1 s % predicted was 61% (sd=21%). Change in 6MWD did not differ between groups. Intervention participants improved their mean daily step counts by 1312 more than those in the usual care group (p<0.001). Groups did not differ on other secondary outcomes. VABirmingham participants were significantly more engaged with the intervention, although site did not modify the effect of the intervention on 6MWD or secondary outcomes. The intervention did not improve exercise capacity but improved physical activity at 6 months. Additional intervention modifications are needed to optimise its COPD self-management capabilities. A web-based self-management intervention improved physical activity but not exercise capacity. There is a need to develop and study accessible self-management interventions for COPD.https://bit.ly/3iT1yvU
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Affiliation(s)
- Stephanie A Robinson
- Center for Healthcare Organization and Implementation Research (CHOIR), Edith Nourse Rogers Memorial Veterans Hospital, Bedford, MA, USA.,Pulmonary Division, Boston University School of Medicine, Boston, MA, USA
| | - J Allen Cooper
- Birmingham VA Medical Center, Birmingham, AL, USA.,Pulmonary, Allergy and Critical Care Medicine, Dept of Medicine, University of Alabama at Birmingham, AL, USA
| | - Rebekah L Goldstein
- Pulmonary and Critical Care Medicine Section, VA Boston Healthcare System, Boston, MA, USA
| | - Madeline Polak
- Pulmonary and Critical Care Medicine Section, VA Boston Healthcare System, Boston, MA, USA
| | - Paola N Cruz Rivera
- Pulmonary and Critical Care Medicine Section, VA Boston Healthcare System, Boston, MA, USA
| | - David R Gagnon
- Dept of Biostatistics, Boston University School of Public Health, Boston, MA, USA.,Massachusetts Veterans Epidemiology Research and Information Center, Veterans Affairs Boston Healthcare System, Boston, MA, USA
| | | | - Sheila Moore
- Birmingham VA Medical Center, Birmingham, AL, USA
| | - Reema Kadri
- Dept of Family Medicine, University of Michigan, Ann Arbor, MI, USA
| | | | - Marilyn L Moy
- Pulmonary and Critical Care Medicine Section, VA Boston Healthcare System, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA
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28
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Song RJ, Ho YL, Schubert P, Park Y, Posner D, Lord EM, Costa L, Gerlovin H, Kurgansky KE, Anglin-Foote T, DuVall S, Huffman JE, Pyarajan S, Beckham JC, Chang KM, Liao KP, Djousse L, Gagnon DR, Whitbourne SB, Ramoni R, Muralidhar S, Tsao PS, O’Donnell CJ, Gaziano JM, Casas JP, Cho K. Phenome-wide association of 1809 phenotypes and COVID-19 disease progression in the Veterans Health Administration Million Veteran Program. PLoS One 2021; 16:e0251651. [PMID: 33984066 PMCID: PMC8118298 DOI: 10.1371/journal.pone.0251651] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Accepted: 04/30/2021] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND The risk factors associated with the stages of Coronavirus Disease-2019 (COVID-19) disease progression are not well known. We aim to identify risk factors specific to each state of COVID-19 progression from SARS-CoV-2 infection through death. METHODS AND RESULTS We included 648,202 participants from the Veteran Affairs Million Veteran Program (2011-). We identified characteristics and 1,809 ICD code-based phenotypes from the electronic health record. We used logistic regression to examine the association of age, sex, body mass index (BMI), race, and prevalent phenotypes to the stages of COVID-19 disease progression: infection, hospitalization, intensive care unit (ICU) admission, and 30-day mortality (separate models for each). Models were adjusted for age, sex, race, ethnicity, number of visit months and ICD codes, state infection rate and controlled for multiple testing using false discovery rate (≤0.1). As of August 10, 2020, 5,929 individuals were SARS-CoV-2 positive and among those, 1,463 (25%) were hospitalized, 579 (10%) were in ICU, and 398 (7%) died. We observed a lower risk in women vs. men for ICU and mortality (Odds Ratio (95% CI): 0.48 (0.30-0.76) and 0.59 (0.31-1.15), respectively) and a higher risk in Black vs. Other race patients for hospitalization and ICU (OR (95%CI): 1.53 (1.32-1.77) and 1.63 (1.32-2.02), respectively). We observed an increased risk of all COVID-19 disease states with older age and BMI ≥35 vs. 20-24 kg/m2. Renal failure, respiratory failure, morbid obesity, acid-base balance disorder, white blood cell diseases, hydronephrosis and bacterial infections were associated with an increased risk of ICU admissions; sepsis, chronic skin ulcers, acid-base balance disorder and acidosis were associated with mortality. CONCLUSIONS Older age, higher BMI, males and patients with a history of respiratory, kidney, bacterial or metabolic comorbidities experienced greater COVID-19 severity. Future studies to investigate the underlying mechanisms associated with these phenotype clusters and COVID-19 are warranted.
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Affiliation(s)
- Rebecca J. Song
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts, United States of America
- Department of Epidemiology, Boston University School of Public Health, Boston, Massachusetts, United States of America
| | - Yuk-Lam Ho
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts, United States of America
| | - Petra Schubert
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts, United States of America
| | - Yojin Park
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts, United States of America
| | - Daniel Posner
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts, United States of America
| | - Emily M. Lord
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts, United States of America
| | - Lauren Costa
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts, United States of America
| | - Hanna Gerlovin
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts, United States of America
| | - Katherine E. Kurgansky
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts, United States of America
| | - Tori Anglin-Foote
- VA Salt Lake City Health Care System, Salt Lake City, Utah, United States of America
| | - Scott DuVall
- VA Salt Lake City Health Care System, Salt Lake City, Utah, United States of America
- Office of Research and Development, Veterans Health Administration, Washington, DC, United States of America
- Department of Medicine, University of Utah School of Medicine, Salt Lake City, Utah, United States of America
| | - Jennifer E. Huffman
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts, United States of America
| | - Saiju Pyarajan
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts, United States of America
- Department of Medicine, Division of Aging, Brigham & Women’s Hospital, Boston, Massachusetts, United States of America
- Department of Medicine, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Jean C. Beckham
- Durham VA Medical Center, Durham, North Carolina, United States of America
- Department of Psychiatry and Behavioral Sciences, University Medical Center, Durham, North Carolina, United States of America
- VA Mid-Atlantic Mental Illness Research Education and Clinical Center, Durham, North Carolina, United States of America
| | - Kyong-Mi Chang
- Corporal Michael Crescenz VA Medical Center, Philadelphia, Pennsylvania, United States of America
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Katherine P. Liao
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts, United States of America
- Department of Medicine, Harvard Medical School, Boston, Massachusetts, United States of America
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Luc Djousse
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts, United States of America
- Department of Medicine, Division of Aging, Brigham & Women’s Hospital, Boston, Massachusetts, United States of America
- Department of Medicine, Harvard Medical School, Boston, Massachusetts, United States of America
| | - David R. Gagnon
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts, United States of America
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, United States of America
| | - Stacey B. Whitbourne
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts, United States of America
- Department of Medicine, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Rachel Ramoni
- Office of Research and Development, Veterans Health Administration, Washington, DC, United States of America
| | - Sumitra Muralidhar
- Office of Research and Development, Veterans Health Administration, Washington, DC, United States of America
| | - Philip S. Tsao
- Department of Medicine, Stanford University School of Medicine, Stanford, California, United States of America
- VA Palo Alto Health Care System, Palo Alto, California, United States of America
| | - Christopher J. O’Donnell
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts, United States of America
- Department of Medicine, Harvard Medical School, Boston, Massachusetts, United States of America
| | - John Michael Gaziano
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts, United States of America
- Department of Medicine, Division of Aging, Brigham & Women’s Hospital, Boston, Massachusetts, United States of America
- Department of Medicine, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Juan P. Casas
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts, United States of America
- Department of Medicine, Division of Aging, Brigham & Women’s Hospital, Boston, Massachusetts, United States of America
- Department of Medicine, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Kelly Cho
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts, United States of America
- Department of Medicine, Division of Aging, Brigham & Women’s Hospital, Boston, Massachusetts, United States of America
- Department of Medicine, Harvard Medical School, Boston, Massachusetts, United States of America
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Djoussé L, Zhou G, McClelland RL, Ma N, Zhou X, Kabagambe EK, Talegawkar SA, Judd SE, Biggs ML, Fitzpatrick AL, Clark CR, Gagnon DR, Steffen LM, Gaziano JM, Lee IM, Buring JE, Manson JE. Egg consumption, overall diet quality, and risk of type 2 diabetes and coronary heart disease: A pooling project of US prospective cohorts. Clin Nutr 2021; 40:2475-2482. [PMID: 33932789 PMCID: PMC8564713 DOI: 10.1016/j.clnu.2021.03.003] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2020] [Revised: 01/25/2021] [Accepted: 03/02/2021] [Indexed: 12/25/2022]
Abstract
BACKGROUND AND AIMS Data on the relation of egg consumption with risk of type 2 diabetes (T2D) and coronary heart disease (CHD) are limited and inconsistent. Few studies have controlled for overall dietary patterns in egg-T2D or egg-CHD analyses, and it is unclear whether any observed elevated risks of T2D and CHD with frequent egg consumption is real or due to confounding by dietary habits. We tested the hypothesis that frequent egg consumption is associated with a higher risk of T2D and CHD risk after adjustment for overall dietary patterns among adults. DESIGN We used prospective cohort design to complete time-to-event analyses. METHODS We pooled de novo, harmonized, individual-level analyses from nine US cohorts (n = 103,811). Cox regression was used to estimate hazard ratios separately in each cohort adjusting for age, ethnicity, body mass index (BMI), exercise, smoking, alcohol intake, and dietary patterns. We pooled cohort-specific results using an inverse-variance weighted method to estimate summary relative risks. RESULTS Median age ranged from 25 to 72 years. Median egg consumption was 1 egg per week in most of the cohorts. While egg consumption up to one per week was not associated with T2D risk, consumption of ≥2 eggs per week was associated with elevated risk [27% elevated risk of T2D comparing 7+ eggs/week with none (95% CI: 16%-37%)]. There was little evidence for heterogeneity across cohorts and we observed similar conclusions when stratified by BMI. Overall, egg consumption was not associated with the risk of CHD. However, in a sensitivity analysis, there was a 30% higher risk of CHD (95% CI: 3%-56%) restricted to older adults consuming 5-6 eggs/week. CONCLUSIONS Our data showed an elevated risk of T2D with egg consumption of ≥2 eggs per week but not with <2 eggs/week. While there was no overall association of egg consumption with CHD risk, the elevated CHD observed with consumption of 5-6 eggs/week in older cohorts merits further investigation.
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Affiliation(s)
- Luc Djoussé
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA; Boston VA Healthcare System, Boston, MA, USA.
| | - Guohai Zhou
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | | | - Nanxun Ma
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Xia Zhou
- University of Minnesota School of Public Health Division of Epidemiology and Community, Health, Minneapolis, MN, USA
| | | | - Sameera A Talegawkar
- Department of Exercise and Nutrition Sciences, Milken Institute School of Public Health at the George Washington University, Washington, DC, USA
| | | | - Mary L Biggs
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | | | - Cheryl R Clark
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA
| | - David R Gagnon
- Boston VA Healthcare System, Boston, MA, USA; Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Lyn M Steffen
- University of Minnesota School of Public Health Division of Epidemiology and Community, Health, Minneapolis, MN, USA
| | - J Michael Gaziano
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA; Boston VA Healthcare System, Boston, MA, USA
| | - I-Min Lee
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Julie E Buring
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA
| | - JoAnn E Manson
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA
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30
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Gaziano L, Giambartolomei C, Pereira AC, Gaulton A, Posner DC, Swanson SA, Ho YL, Iyengar SK, Kosik NM, Vujkovic M, Gagnon DR, Bento AP, Barrio-Hernandez I, Rönnblom L, Hagberg N, Lundtoft C, Langenberg C, Pietzner M, Valentine D, Gustincich S, Tartaglia GG, Allara E, Surendran P, Burgess S, Zhao JH, Peters JE, Prins BP, Angelantonio ED, Devineni P, Shi Y, Lynch KE, DuVall SL, Garcon H, Thomann LO, Zhou JJ, Gorman BR, Huffman JE, O'Donnell CJ, Tsao PS, Beckham JC, Pyarajan S, Muralidhar S, Huang GD, Ramoni R, Beltrao P, Danesh J, Hung AM, Chang KM, Sun YV, Joseph J, Leach AR, Edwards TL, Cho K, Gaziano JM, Butterworth AS, Casas JP. Actionable druggable genome-wide Mendelian randomization identifies repurposing opportunities for COVID-19. Nat Med 2021; 27:668-676. [PMID: 33837377 PMCID: PMC7612986 DOI: 10.1038/s41591-021-01310-z] [Citation(s) in RCA: 86] [Impact Index Per Article: 28.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2020] [Accepted: 03/05/2021] [Indexed: 12/31/2022]
Abstract
Drug repurposing provides a rapid approach to meet the urgent need for therapeutics to address COVID-19. To identify therapeutic targets relevant to COVID-19, we conducted Mendelian randomization analyses, deriving genetic instruments based on transcriptomic and proteomic data for 1,263 actionable proteins that are targeted by approved drugs or in clinical phase of drug development. Using summary statistics from the Host Genetics Initiative and the Million Veteran Program, we studied 7,554 patients hospitalized with COVID-19 and >1 million controls. We found significant Mendelian randomization results for three proteins (ACE2, P = 1.6 × 10-6; IFNAR2, P = 9.8 × 10-11 and IL-10RB, P = 2.3 × 10-14) using cis-expression quantitative trait loci genetic instruments that also had strong evidence for colocalization with COVID-19 hospitalization. To disentangle the shared expression quantitative trait loci signal for IL10RB and IFNAR2, we conducted phenome-wide association scans and pathway enrichment analysis, which suggested that IFNAR2 is more likely to play a role in COVID-19 hospitalization. Our findings prioritize trials of drugs targeting IFNAR2 and ACE2 for early management of COVID-19.
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Affiliation(s)
- Liam Gaziano
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA
- BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Claudia Giambartolomei
- Central RNA Lab, Istituto Italiano di Tecnologia, Genoa, Italy
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Alexandre C Pereira
- Laboratory of Genetics and Molecular Cardiology, Heart Institute, University of São Paulo, São Paulo, Brazil
- Genetics Department, Harvard Medical School, Harvard University, Boston, MA, USA
| | - Anna Gaulton
- Chemical Biology, European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK
| | - Daniel C Posner
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA
| | - Sonja A Swanson
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Yuk-Lam Ho
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA
| | - Sudha K Iyengar
- Louis Stokes Cleveland VA Medical Center, Cleveland, OH, USA
- Department of Population and Quantitative Health Sciences, Case Western Reserve University and Louis Stoke, Cleveland VA, Cleveland, OH, USA
| | - Nicole M Kosik
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA
| | - Marijana Vujkovic
- The Corporal Michael J. Crescenz VA Medical Center, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
- Department of Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - David R Gagnon
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA
- Biostatistics, School of Public Health, Boston University, Boston, MA, USA
| | - A Patrícia Bento
- Chemical Biology, European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK
| | | | - Lars Rönnblom
- Department of Medical Sciences, Uppsala University, Uppsala, Sweden
| | - Niklas Hagberg
- Department of Medical Sciences, Uppsala University, Uppsala, Sweden
| | | | - Claudia Langenberg
- Berlin Institute of Health, Charité University Medicine Berlin, Berlin, Germany
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Maik Pietzner
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Dennis Valentine
- Institute of Health Informatics, University College London, London, UK
- Health Data Research, University College London, London, UK
| | | | | | - Elias Allara
- BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Praveen Surendran
- BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Stephen Burgess
- BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
| | - Jing Hua Zhao
- BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - James E Peters
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
- Centre for Inflammatory Disease, Dept of Immunology and Inflammation, Imperial College, London, UK
| | - Bram P Prins
- BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
| | - Emanuele Di Angelantonio
- BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
- National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, UK
- National Institute for Health Research Cambridge Biomedical Research Centre, University of Cambridge and Cambridge University Hospitals, Cambridge, UK
| | - Poornima Devineni
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA
| | - Yunling Shi
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA
| | - Kristine E Lynch
- VA Informatics and Computing Infrastructure, VA Salt Lake City Health Care System, Salt Lake City, UT, USA
- Department of Internal Medicine, Epidemiology, University of Utah, Salt Lake City, UT, USA
| | - Scott L DuVall
- VA Informatics and Computing Infrastructure, VA Salt Lake City Health Care System, Salt Lake City, UT, USA
- Department of Internal Medicine, Epidemiology, University of Utah, Salt Lake City, UT, USA
| | - Helene Garcon
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA
| | - Lauren O Thomann
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA
| | - Jin J Zhou
- Department of Epidemiology and Biostatistics, University of Arizona, Tucson, AZ, USA
- Phoenix VA Health Care System, Phoenix, AZ, USA
| | - Bryan R Gorman
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA
| | - Jennifer E Huffman
- Center for Population Genomics, Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA
| | - Christopher J O'Donnell
- Cardiology, VA Boston Healthcare System, Boston, MA, USA
- Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Philip S Tsao
- Epidemiology Research and Information Center (ERIC), VA Palo Alto Health Care System, Palo Alto, CA, USA
- Department of Medicine, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Jean C Beckham
- MIRECC, Durham VA Medical Center, Durham, NC, USA
- Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA
| | - Saiju Pyarajan
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA
| | - Sumitra Muralidhar
- Office of Research and Development, Department of Veterans Affairs, Washington, DC, USA
| | - Grant D Huang
- Office of Research and Development, Department of Veterans Affairs, Washington, DC, USA
| | - Rachel Ramoni
- Office of Research and Development, Department of Veterans Affairs, Washington, DC, USA
| | - Pedro Beltrao
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK
| | - John Danesh
- BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
- National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, UK
- National Institute for Health Research Cambridge Biomedical Research Centre, University of Cambridge and Cambridge University Hospitals, Cambridge, UK
| | - Adriana M Hung
- VA Tennessee Valley Healthcare System, Nashville, TN, USA
- Nephrology & Hypertension, Vanderbilt University, Nashville, TN, USA
| | - Kyong-Mi Chang
- Department of Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
- The Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA
| | - Yan V Sun
- Atlanta VA Health Care System, Decatur, GA, USA
- Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, GA, USA
| | - Jacob Joseph
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA
- Medicine, Cardiovascular, VA Boston Healthcare System and Brigham & Women's Hospital, Boston, MA, USA
| | - Andrew R Leach
- Chemical Biology, European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK
| | - Todd L Edwards
- Department of Veterans Affairs, Tennessee Valley Healthcare System, Vanderbilt University, Nashville, TN, USA
- Medicine, Epidemiology, Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Kelly Cho
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA
- Division of Aging, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - J Michael Gaziano
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA
- Division of Aging, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Adam S Butterworth
- BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK.
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK.
- National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, UK.
- National Institute for Health Research Cambridge Biomedical Research Centre, University of Cambridge and Cambridge University Hospitals, Cambridge, UK.
| | - Juan P Casas
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA.
- Division of Aging, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
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Seligman B, Charest B, Gagnon DR, Orkaby AR. Trends in 30-day mortality from COVID-19 among older adults in the Veterans Affairs system. J Am Geriatr Soc 2021; 69:1448-1450. [PMID: 33769557 PMCID: PMC8251413 DOI: 10.1111/jgs.17127] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Revised: 03/02/2021] [Accepted: 03/03/2021] [Indexed: 12/15/2022]
Affiliation(s)
- Benjamin Seligman
- New England Geriatrics Research, Education, and Clinical Center, Boston VA Health Care System, Boston, Massachusetts, USA
| | - Brian Charest
- Massachusetts Veterans Epidemiology Research and Information Center, Boston VA Health Care System, Boston, Massachusetts, USA
| | - David R Gagnon
- Massachusetts Veterans Epidemiology Research and Information Center, Boston VA Health Care System, Boston, Massachusetts, USA.,Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, USA
| | - Ariela R Orkaby
- New England Geriatrics Research, Education, and Clinical Center, Boston VA Health Care System, Boston, Massachusetts, USA.,Division of Aging, Brigham & Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
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Ho YL, Nguyen XMT, Yan JQ, Vassy JL, Gagnon DR, Gaziano JM, Wilson PWF, Cho K, Djoussé L. Chocolate consumption and risk of coronary artery disease: the Million Veteran Program. Am J Clin Nutr 2021; 113:1137-1144. [PMID: 34483344 PMCID: PMC8412179 DOI: 10.1093/ajcn/nqaa427] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Abstract
Background Although previous studies have suggested cocoa products may promote cardiovascular health in the general population, no public data are available from patients receiving care in a national integrated health care system. Objectives We tested the hypothesis that regular chocolate consumption is associated with a lower risk of coronary artery disease (CAD) events among participants of the Million Veteran Program (MVP). Secondary analysis examined if the main hypothesis was observed among participants with type 2 diabetes. Methods We analyzed data from MVP participants who completed the food frequency section of the MVP Lifestyle Survey and were free of CAD at the time of survey completion. CAD events during follow-up (International Statistical Classification of Diseases Ninth Revision codes 410-411 and 413-414, and Tenth Revision codes I20-I25 except I25.2) were assessed using electronic health records. We fitted a Cox proportional hazard model to estimate the RR of CAD. Results Of 188,447 MVP enrollees with survey data, mean ± SD age was 64 ± 12.0 y and 90% were men. For regular chocolate (28.3 g/serving) consumption of <1 serving/mo, 1-3 servings/mo, 1 serving/wk, 2-4 servings/wk, and ≥5 servings/wk, crude incidence rates (per 1000 person-years) for fatal and nonfatal CAD events or coronary procedures were 20.2, 17.5, 16.7, 17.1, and 16.9, respectively, during a mean follow-up of 3.2 y. After adjusting for age, sex, race, and lifestyle factors, the corresponding HRs (95% CIs) were 1.00 (ref), 0.92 (0.87, 0.96), 0.88 (0.83, 0.93), 0.89 (0.84, 0.95), and 0.89 (0.84, 0.96), respectively (P for linear trend < 0.0001). In a secondary analysis of 47,265 diabetics, we did not observe a decreasing trend in CAD mortality among those who consumed ≥1 serving of chocolate a month compared with those who consumed <1 serving/mo. Conclusions Regular chocolate consumption was associated with a lower risk of CAD among veterans, but was not associated with cardiovascular disease risk in veterans with type 2 diabetes.
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Affiliation(s)
- Yuk-Lam Ho
- Address correspondence to Y-LH (e-mail: )
| | - Xuan-Mai T Nguyen
- VA Boston Healthcare System, Boston, MA, USA,Division of Aging, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA,Harvard Medical School, Boston, MA, USA
| | | | - Jason L Vassy
- VA Boston Healthcare System, Boston, MA, USA,Harvard Medical School, Boston, MA, USA,Division of General Internal Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - David R Gagnon
- VA Boston Healthcare System, Boston, MA, USA,Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - J Michael Gaziano
- VA Boston Healthcare System, Boston, MA, USA,Division of Aging, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA,Harvard Medical School, Boston, MA, USA,Division of General Internal Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Peter W F Wilson
- Atlanta VA Medical Center, Atlanta, GA, USA,School of Medicine, Emory University, Atlanta, GA, USA,School of Public Health, Emory University, Atlanta, GA, USA
| | - Kelly Cho
- VA Boston Healthcare System, Boston, MA, USA,Division of Aging, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA,Harvard Medical School, Boston, MA, USA
| | - Luc Djoussé
- VA Boston Healthcare System, Boston, MA, USA,Division of Aging, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA,Harvard Medical School, Boston, MA, USA
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Ngwa JS, Cabral HJ, Cheng DM, Gagnon DR, LaValley MP, Cupples LA. Revisiting methods for modeling longitudinal and survival data: Framingham Heart Study. BMC Med Res Methodol 2021; 21:29. [PMID: 33568059 PMCID: PMC7876802 DOI: 10.1186/s12874-021-01207-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Accepted: 01/13/2021] [Indexed: 11/27/2022] Open
Abstract
Background Statistical methods for modeling longitudinal and time-to-event data has received much attention in medical research and is becoming increasingly useful. In clinical studies, such as cancer and AIDS, longitudinal biomarkers are used to monitor disease progression and to predict survival. These longitudinal measures are often missing at failure times and may be prone to measurement errors. More importantly, time-dependent survival models that include the raw longitudinal measurements may lead to biased results. In previous studies these two types of data are frequently analyzed separately where a mixed effects model is used for the longitudinal data and a survival model is applied to the event outcome. Methods In this paper we compare joint maximum likelihood methods, a two-step approach and a time dependent covariate method that link longitudinal data to survival data with emphasis on using longitudinal measures to predict survival. We apply a Bayesian semi-parametric joint method and maximum likelihood joint method that maximizes the joint likelihood of the time-to-event and longitudinal measures. We also implement the Two-Step approach, which estimates random effects separately, and a classic Time Dependent Covariate Model. We use simulation studies to assess bias, accuracy, and coverage probabilities for the estimates of the link parameter that connects the longitudinal measures to survival times. Results Simulation results demonstrate that the Two-Step approach performed best at estimating the link parameter when variability in the longitudinal measure is low but is somewhat biased downwards when the variability is high. Bayesian semi-parametric and maximum likelihood joint methods yield higher link parameter estimates with low and high variability in the longitudinal measure. The Time Dependent Covariate method resulted in consistent underestimation of the link parameter. We illustrate these methods using data from the Framingham Heart Study in which lipid measurements and Myocardial Infarction data were collected over a period of 26 years. Conclusions Traditional methods for modeling longitudinal and survival data, such as the time dependent covariate method, that use the observed longitudinal data, tend to provide downwardly biased estimates. The two-step approach and joint models provide better estimates, although a comparison of these methods may depend on the underlying residual variance. Supplementary Information The online version contains supplementary material available at 10.1186/s12874-021-01207-y.
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Affiliation(s)
- Julius S Ngwa
- Department of Biostatistics, School of Public Health, Boston University, 801 Massachusetts Ave, CT 3rd Floor, Boston, MA, 02118, USA. .,Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, 615 North Wolfe St E3009, Baltimore, MD, 21205, USA.
| | - Howard J Cabral
- Department of Biostatistics, School of Public Health, Boston University, 801 Massachusetts Ave, CT 3rd Floor, Boston, MA, 02118, USA
| | - Debbie M Cheng
- Department of Biostatistics, School of Public Health, Boston University, 801 Massachusetts Ave, CT 3rd Floor, Boston, MA, 02118, USA
| | - David R Gagnon
- Department of Biostatistics, School of Public Health, Boston University, 801 Massachusetts Ave, CT 3rd Floor, Boston, MA, 02118, USA
| | - Michael P LaValley
- Department of Biostatistics, School of Public Health, Boston University, 801 Massachusetts Ave, CT 3rd Floor, Boston, MA, 02118, USA
| | - L Adrienne Cupples
- Department of Biostatistics, School of Public Health, Boston University, 801 Massachusetts Ave, CT 3rd Floor, Boston, MA, 02118, USA. .,National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, MA, 01702, USA.
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King JT, Yoon JS, Rentsch CT, Tate JP, Park LS, Kidwai-Khan F, Skanderson M, Hauser RG, Jacobson DA, Erdos J, Cho K, Ramoni R, Gagnon DR, Justice AC. Development and validation of a 30-day mortality index based on pre-existing medical administrative data from 13,323 COVID-19 patients: The Veterans Health Administration COVID-19 (VACO) Index. PLoS One 2020; 15:e0241825. [PMID: 33175863 PMCID: PMC7657526 DOI: 10.1371/journal.pone.0241825] [Citation(s) in RCA: 63] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Accepted: 10/21/2020] [Indexed: 01/08/2023] Open
Abstract
BACKGROUND Available COVID-19 mortality indices are limited to acute inpatient data. Using nationwide medical administrative data available prior to SARS-CoV-2 infection from the US Veterans Health Administration (VA), we developed the VA COVID-19 (VACO) 30-day mortality index and validated the index in two independent, prospective samples. METHODS AND FINDINGS We reviewed SARS-CoV-2 testing results within the VA between February 8 and August 18, 2020. The sample was split into a development cohort (test positive between March 2 and April 15, 2020), an early validation cohort (test positive between April 16 and May 18, 2020), and a late validation cohort (test positive between May 19 and July 19, 2020). Our logistic regression model in the development cohort considered demographics (age, sex, race/ethnicity), and pre-existing medical conditions and the Charlson Comorbidity Index (CCI) derived from ICD-10 diagnosis codes. Weights were fixed to create the VACO Index that was then validated by comparing area under receiver operating characteristic curves (AUC) in the early and late validation cohorts and among important validation cohort subgroups defined by sex, race/ethnicity, and geographic region. We also evaluated calibration curves and the range of predictions generated within age categories. 13,323 individuals tested positive for SARS-CoV-2 (median age: 63 years; 91% male; 42% non-Hispanic Black). We observed 480/3,681 (13%) deaths in development, 253/2,151 (12%) deaths in the early validation cohort, and 403/7,491 (5%) deaths in the late validation cohort. Age, multimorbidity described with CCI, and a history of myocardial infarction or peripheral vascular disease were independently associated with mortality-no other individual comorbid diagnosis provided additional information. The VACO Index discriminated mortality in development (AUC = 0.79, 95% CI: 0.77-0.81), and in early (AUC = 0.81 95% CI: 0.78-0.83) and late (AUC = 0.84, 95% CI: 0.78-0.86) validation. The VACO Index allows personalized estimates of 30-day mortality after COVID-19 infection. For example, among those aged 60-64 years, overall mortality was estimated at 9% (95% CI: 6-11%). The Index further discriminated risk in this age stratum from 4% (95% CI: 3-7%) to 21% (95% CI: 12-31%), depending on sex and comorbid disease. CONCLUSION Prior to infection, demographics and comorbid conditions can discriminate COVID-19 mortality risk overall and within age strata. The VACO Index reproducibly identified individuals at substantial risk of COVID-19 mortality who might consider continuing social distancing, despite relaxed state and local guidelines.
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Affiliation(s)
- Joseph T. King
- VA Connecticut Healthcare System, U.S. Department of Veterans Affairs, West Haven, Connecticut, United States of America
- Department of Neurosurgery, Yale School of Medicine, New Haven, Connecticut, United States of America
| | - James S. Yoon
- VA Connecticut Healthcare System, U.S. Department of Veterans Affairs, West Haven, Connecticut, United States of America
- Department of Neurosurgery, Yale School of Medicine, New Haven, Connecticut, United States of America
- Yale School of Medicine, New Haven, Connecticut, United States of America
| | - Christopher T. Rentsch
- VA Connecticut Healthcare System, U.S. Department of Veterans Affairs, West Haven, Connecticut, United States of America
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Janet P. Tate
- VA Connecticut Healthcare System, U.S. Department of Veterans Affairs, West Haven, Connecticut, United States of America
- Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut, United States of America
| | - Lesley S. Park
- Stanford Center for Population Health Sciences, Stanford University School of Medicine, Stanford, California, United States of America
| | - Farah Kidwai-Khan
- VA Connecticut Healthcare System, U.S. Department of Veterans Affairs, West Haven, Connecticut, United States of America
- Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut, United States of America
| | - Melissa Skanderson
- VA Connecticut Healthcare System, U.S. Department of Veterans Affairs, West Haven, Connecticut, United States of America
| | - Ronald G. Hauser
- VA Connecticut Healthcare System, U.S. Department of Veterans Affairs, West Haven, Connecticut, United States of America
- Department of Laboratory Medicine, Yale School of Medicine, New Haven, Connecticut, United States of America
| | - Daniel A. Jacobson
- Oak Ridge National Laboratory, Biosciences Division, Oak Ridge, Tennessee, United States of America
- The Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee Knoxville, Knoxville, Tennessee, United States of America
- Department of Psychology, University of Tennessee Knoxville, Knoxville, Tennesee, United States of America
| | - Joseph Erdos
- VA Connecticut Healthcare System, U.S. Department of Veterans Affairs, West Haven, Connecticut, United States of America
- Department of Psychiatry, Yale School of Medicine, New Haven, Connecticut, United States of America
| | - Kelly Cho
- VA Boston Healthcare System, U.S. Department of Veterans Affairs, Boston, Massachusetts, United States of America
- Department of Medicine, Harvard Medical School, Boston, Massachusetts, United States of America
- Division of Aging, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
| | - Rachel Ramoni
- Office of Research and Development, Veterans Health Administration, United States Department of Veterans Affairs, Washington, DC, United States of America
| | - David R. Gagnon
- Department of Neurosurgery, Yale School of Medicine, New Haven, Connecticut, United States of America
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, United States of America
| | - Amy C. Justice
- VA Connecticut Healthcare System, U.S. Department of Veterans Affairs, West Haven, Connecticut, United States of America
- Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut, United States of America
- Yale School of Public Health, New Haven, Connecticut, United States of America
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Affiliation(s)
- Ariela R Orkaby
- New England GRECC (Geriatric Research, Education, and Clinical Center), VA Boston Healthcare System, Boston, Massachusetts
| | - David R Gagnon
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston
| | - Luc Djousse
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston
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Djoussé L, Ho YL, Nguyen XMT, Quaden RM, Gagnon DR, Gaziano JM, Cho K. Egg consumption and risk of coronary artery disease in the Million Veteran Program. Clin Nutr 2020; 39:2842-2847. [PMID: 31902601 PMCID: PMC7311223 DOI: 10.1016/j.clnu.2019.12.017] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2019] [Revised: 12/05/2019] [Accepted: 12/16/2019] [Indexed: 12/13/2022]
Abstract
BACKGROUND & AIMS Limited and inconsistent data are available on the relation between egg consumption and risk of myocardial infarction (MI) and it is unclear if adiposity or type 2 diabetes modifies egg-MI relation. We tested the primary hypothesis that egg consumption is positively associated with incidence of MI among veterans. In secondary analyses, we examined potential effect modification of egg-MI relation by adiposity and type 2 diabetes. METHODS We analyzed data collected on 188,267 US veterans who were enrolled in the Million Veteran Program (MVP) from 2011 to 2018. Information on egg consumption was obtained via self-administered food frequency questionnaire and we used electronic health records to identify incident MI. RESULTS The mean age was 64.4 (SD = 12.0) years and 9.9% of the population were female. We ascertained 10,260 new cases of non-fatal MI during an average follow up of 3.24 years (range: 0.002 to 7.49 y). Hazard ratio (95% CI) for non-fatal MI were 1.00 (ref), 0.93 (0.85-0.1.02), 0.96 (0.87-1.05), 0.98 (0.89-1.07), 1.08 (0.98-1.19), 1.11 (1.00-1.24), and 1.13 (1.00-1.28) for egg consumption of <1/month, 1-3/month, 1/week, 2-4/week, 5-6/week, 1/d, and 2+/d, respectively, controlling for age, sex, race, body mass index, smoking, exercise, alcohol intake, and overall dietary pattern (p non-linear trend 0.019). In secondary analyses, we observed similar results with a composite endpoint including fatal MI, coronary angioplasty and revascularization. CONCLUSIONS Our data showed no association of infrequent consumption of eggs with non-fatal MI but a slightly elevated risk with intake of 1 or more eggs per day among US veterans.
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Affiliation(s)
- Luc Djoussé
- Massachusetts Veterans Epidemiology Research and Information Center, VA Boston Healthcare System, Boston, MA, USA; Harvard Medical School, Boston, MA, USA; Department of Medicine, Division of Aging, Brigham and Women's Hospital, Boston, MA, USA.
| | - Yuk-Lam Ho
- Massachusetts Veterans Epidemiology Research and Information Center, VA Boston Healthcare System, Boston, MA, USA
| | - Xuan-Mai T Nguyen
- Massachusetts Veterans Epidemiology Research and Information Center, VA Boston Healthcare System, Boston, MA, USA; Harvard Medical School, Boston, MA, USA; Department of Medicine, Division of Aging, Brigham and Women's Hospital, Boston, MA, USA
| | - Rachel M Quaden
- Massachusetts Veterans Epidemiology Research and Information Center, VA Boston Healthcare System, Boston, MA, USA
| | - David R Gagnon
- Massachusetts Veterans Epidemiology Research and Information Center, VA Boston Healthcare System, Boston, MA, USA; Boston University School of Public Health, Boston, MA, USA
| | - J Michael Gaziano
- Massachusetts Veterans Epidemiology Research and Information Center, VA Boston Healthcare System, Boston, MA, USA; Harvard Medical School, Boston, MA, USA; Department of Medicine, Division of Aging, Brigham and Women's Hospital, Boston, MA, USA
| | - Kelly Cho
- Massachusetts Veterans Epidemiology Research and Information Center, VA Boston Healthcare System, Boston, MA, USA; Harvard Medical School, Boston, MA, USA; Department of Medicine, Division of Aging, Brigham and Women's Hospital, Boston, MA, USA
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Raghavan S, Ho YL, Vassy JL, Posner D, Honerlaw J, Costa L, Phillips LS, Gagnon DR, Wilson PWF, Cho K. Optimizing Atherosclerotic Cardiovascular Disease Risk Estimation for Veterans With Diabetes Mellitus. Circ Cardiovasc Qual Outcomes 2020; 13:e006528. [PMID: 32862698 PMCID: PMC7914289 DOI: 10.1161/circoutcomes.120.006528] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
BACKGROUND Estimated 10-year atherosclerotic cardiovascular disease (ASCVD) risk in diabetes mellitus patients is used to guide primary prevention, but the performance of risk estimators (2013 Pooled Cohort Equations [PCE] and Risk Equations for Complications of Diabetes [RECODe]) varies across populations. Data from electronic health records could be used to improve risk estimation for a health system's patients. We aimed to evaluate risk equations for initial ASCVD events in US veterans with diabetes mellitus and improve model performance in this population. METHODS AND RESULTS We studied 183 096 adults with diabetes mellitus and without prior ASCVD who received care in the Veterans Affairs Healthcare System (VA) from 2002 to 2016 with mean follow-up of 4.6 years. We evaluated model discrimination, using Harrell's C statistic, and calibration, using the reclassification χ2 test, of the PCE and RECODe equations to predict fatal or nonfatal myocardial infarction or stroke and cardiovascular mortality. We then tested whether model performance was affected by deriving VA-specific β-coefficients. Discrimination of ASCVD events by the PCE was improved by deriving VA-specific β-coefficients (C statistic increased from 0.560 to 0.597) and improved further by including measures of glycemia, renal function, and diabetes mellitus treatment (C statistic, 0.632). Discrimination by the RECODe equations was improved by substituting VA-specific coefficients (C statistic increased from 0.604 to 0.621). Absolute risk estimation by PCE and RECODe equations also improved with VA-specific coefficients; the calibration P increased from <0.001 to 0.08 for PCE and from <0.001 to 0.005 for RECODe, where higher P indicates better calibration. Approximately two-thirds of veterans would meet a guideline indication for high-intensity statin therapy based on the PCE versus only 10% to 15% using VA-fitted models. CONCLUSIONS Existing ASCVD risk equations overestimate risk in veterans with diabetes mellitus, potentially impacting guideline-indicated statin therapy. Prediction model performance can be improved for a health system's patients using readily available electronic health record data.
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Affiliation(s)
- Sridharan Raghavan
- Veterans Affairs Eastern Colorado Healthcare System, Aurora, CO
- Division of Hospital Medicine, University of Colorado School of Medicine, Aurora, CO
- Colorado Cardiovascular Outcomes Research Consortium, Aurora, CO
| | - Yuk-Lam Ho
- Veterans Affairs Boston Healthcare System, Boston, MA
| | - Jason L. Vassy
- Veterans Affairs Boston Healthcare System, Boston, MA
- Department of Medicine, Harvard Medical School, Boston, MA
- Division of General Internal Medicine and Primary Care, Brigham and Women’s Hospital, Boston, MA
| | - Daniel Posner
- Veterans Affairs Boston Healthcare System, Boston, MA
| | | | - Lauren Costa
- Veterans Affairs Boston Healthcare System, Boston, MA
| | - Lawrence S. Phillips
- Atlanta Veterans Affairs Medical Center, Decatur, GA
- Division of Endocrinology, Emory University School of Medicine, Atlanta, GA
| | - David R. Gagnon
- Veterans Affairs Boston Healthcare System, Boston, MA
- Department of Biostatistics, Boston University School of Public Health, Boston, MA
| | - Peter W. F. Wilson
- Atlanta Veterans Affairs Medical Center, Decatur, GA
- Division of Cardiology, Emory University School of Medicine, Atlanta, GA
| | - Kelly Cho
- Veterans Affairs Boston Healthcare System, Boston, MA
- Department of Medicine, Harvard Medical School, Boston, MA
- Division of Aging, Brigham and Women’s Hospital, Boston, MA
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Orkaby AR, Driver JA, Ho YL, Lu B, Costa L, Honerlaw J, Galloway A, Vassy JL, Forman DE, Gaziano JM, Gagnon DR, Wilson PWF, Cho K, Djousse L. Association of Statin Use With All-Cause and Cardiovascular Mortality in US Veterans 75 Years and Older. JAMA 2020; 324:68-78. [PMID: 32633800 PMCID: PMC7341181 DOI: 10.1001/jama.2020.7848] [Citation(s) in RCA: 114] [Impact Index Per Article: 28.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
IMPORTANCE Data are limited regarding statin therapy for primary prevention of atherosclerotic cardiovascular disease (ASCVD) in adults 75 years and older. OBJECTIVE To evaluate the role of statin use for mortality and primary prevention of ASCVD in veterans 75 years and older. DESIGN, SETTING, AND PARTICIPANTS Retrospective cohort study that used Veterans Health Administration (VHA) data on adults 75 years and older, free of ASCVD, and with a clinical visit in 2002-2012. Follow-up continued through December 31, 2016. All data were linked to Medicare and Medicaid claims and pharmaceutical data. A new-user design was used, excluding those with any prior statin use. Cox proportional hazards models were fit to evaluate the association of statin use with outcomes. Analyses were conducted using propensity score overlap weighting to balance baseline characteristics. EXPOSURES Any new statin prescription. MAIN OUTCOMES AND MEASURES The primary outcomes were all-cause and cardiovascular mortality. Secondary outcomes included a composite of ASCVD events (myocardial infarction, ischemic stroke, and revascularization with coronary artery bypass graft surgery or percutaneous coronary intervention). RESULTS Of 326 981 eligible veterans (mean [SD] age, 81.1 [4.1] years; 97% men; 91% white), 57 178 (17.5%) newly initiated statins during the study period. During a mean follow-up of 6.8 (SD, 3.9) years, a total 206 902 deaths occurred including 53 296 cardiovascular deaths, with 78.7 and 98.2 total deaths/1000 person-years among statin users and nonusers, respectively (weighted incidence rate difference [IRD]/1000 person-years, -19.5 [95% CI, -20.4 to -18.5]). There were 22.6 and 25.7 cardiovascular deaths per 1000 person-years among statin users and nonusers, respectively (weighted IRD/1000 person-years, -3.1 [95 CI, -3.6 to -2.6]). For the composite ASCVD outcome there were 123 379 events, with 66.3 and 70.4 events/1000 person-years among statin users and nonusers, respectively (weighted IRD/1000 person-years, -4.1 [95% CI, -5.1 to -3.0]). After propensity score overlap weighting was applied, the hazard ratio was 0.75 (95% CI, 0.74-0.76) for all-cause mortality, 0.80 (95% CI, 0.78-0.81) for cardiovascular mortality, and 0.92 (95% CI, 0.91-0.94) for a composite of ASCVD events when comparing statin users with nonusers. CONCLUSIONS AND RELEVANCE Among US veterans 75 years and older and free of ASCVD at baseline, new statin use was significantly associated with a lower risk of all-cause and cardiovascular mortality. Further research, including from randomized clinical trials, is needed to more definitively determine the role of statin therapy in older adults for primary prevention of ASCVD.
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Affiliation(s)
- Ariela R. Orkaby
- New England Geriatric Research, Education, and Clinical Center (GRECC), VA Boston Healthcare System, Boston, Massachusetts
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts
- Division of Aging, Brigham & Women’s Hospital, Harvard Medical School, Boston, Massachusetts
| | - Jane A. Driver
- New England Geriatric Research, Education, and Clinical Center (GRECC), VA Boston Healthcare System, Boston, Massachusetts
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts
- Division of Aging, Brigham & Women’s Hospital, Harvard Medical School, Boston, Massachusetts
| | - Yuk-Lam Ho
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts
| | - Bing Lu
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts
- Department of Medicine, Brigham & Women’s Hospital, Harvard Medical School, Boston, Massachusetts
| | - Lauren Costa
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts
| | - Jacqueline Honerlaw
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts
| | - Ashley Galloway
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts
| | - Jason L. Vassy
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts
- Department of Medicine, Brigham & Women’s Hospital, Harvard Medical School, Boston, Massachusetts
| | - Daniel E. Forman
- Section of Geriatric Cardiology, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania
- Geriatric Research, Education, and Clinical Center, VA Pittsburgh Healthcare System, Pittsburgh, Pennsylvania
| | - J. Michael Gaziano
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts
- Division of Aging, Brigham & Women’s Hospital, Harvard Medical School, Boston, Massachusetts
| | - David R. Gagnon
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts
| | - Peter W. F. Wilson
- Atlanta VA Medical Center, Decatur, Georgia
- Division of Cardiology, Emory University School of Medicine, Atlanta, Georgia
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, Georgia
| | - Kelly Cho
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts
- Division of Aging, Brigham & Women’s Hospital, Harvard Medical School, Boston, Massachusetts
| | - Luc Djousse
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts
- Division of Aging, Brigham & Women’s Hospital, Harvard Medical School, Boston, Massachusetts
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Vassy JL, Lu B, Ho YL, Galloway A, Raghavan S, Honerlaw J, Tarko L, Russo J, Qazi S, Orkaby AR, Tanukonda V, Djousse L, Gaziano JM, Gagnon DR, Cho K, Wilson PWF. Estimation of Atherosclerotic Cardiovascular Disease Risk Among Patients in the Veterans Affairs Health Care System. JAMA Netw Open 2020; 3:e208236. [PMID: 32662843 PMCID: PMC7361654 DOI: 10.1001/jamanetworkopen.2020.8236] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Abstract
IMPORTANCE Current guidelines recommend statin therapy for millions of US residents for the primary prevention of atherosclerotic cardiovascular disease (ASCVD). It is unclear whether traditional prediction models that do not account for current widespread statin use are sufficient for risk assessment. OBJECTIVES To examine the performance of the Pooled Cohort Equations (PCE) for 5-year ASCVD risk estimation in a contemporary cohort and to test the hypothesis that inclusion of statin therapy improves model performance. DESIGN, SETTING, AND PARTICIPANTS This cohort study included adult patients in the Veterans Affairs health care system without baseline ASCVD. Using national electronic health record data, 3 Cox proportional hazards models were developed to estimate 5-year ASCVD risk, as follows: the variables and published β coefficients from the PCE (model 1), the PCE variables with cohort-derived β coefficients (model 2), and model 2 plus baseline statin use (model 3). Data were collected from January 2002 to December 2012 and analyzed from June 2016 to March 2020. EXPOSURES Traditional ASCVD risk factors from the PCE plus baseline statin use. MAIN OUTCOMES AND MEASURES Incident ASCVD and ASCVD mortality. RESULTS Of 1 672 336 patients in the cohort (mean [SD] baseline age 58.0 [13.8] years, 1 575 163 [94.2%] men, 1 383 993 [82.8%] white), 312 155 (18.7%) were receiving statin therapy at baseline. During 5 years of follow-up, 66 605 (4.0%) experienced an ASCVD event, and 31 878 (1.9%) experienced ASCVD death. Compared with the original PCE, the cohort-derived model did not improve model discrimination in any of the 4 age-sex strata but did improve model calibration. The PCE overestimated ASCVD risk compared with the cohort-derived model; 211 237 of 1 136 161 white men (18.6%), 29 634 of 218 463 black men (13.6%), 1741 of 44 399 white women (3.9%), and 836 of 16 034 black women (5.2%) would be potentially eligible for statin therapy under the PCE but not the cohort-derived model. When added to the cohort-derived model, baseline statin therapy was associated with a 7% (95% CI, 5%-9%) lower relative risk of ASCVD and a 25% (95% CI, 23%-28%) lower relative risk for ASCVD death. CONCLUSIONS AND RELEVANCE In this study, lower than expected rates of incident ASCVD events in a contemporary national cohort were observed. The PCE overestimated ASCVD risk, and more than 15% of patients would be potentially eligible for statin therapy based on the PCE but not on a cohort-derived model. In the statin era, health care professionals and systems should base ASCVD risk assessment on models calibrated to their patient populations.
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Affiliation(s)
- Jason L. Vassy
- Veterans Affairs Boston Healthcare System, Boston, Massachusetts
- Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
| | - Bing Lu
- Veterans Affairs Boston Healthcare System, Boston, Massachusetts
- Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
| | - Yuk-Lam Ho
- Veterans Affairs Boston Healthcare System, Boston, Massachusetts
| | - Ashley Galloway
- Veterans Affairs Boston Healthcare System, Boston, Massachusetts
| | - Sridharan Raghavan
- Veterans Affairs Eastern Colorado Healthcare System, Aurora
- Division of Hospital Medicine, University of Colorado School of Medicine, Aurora
- Colorado Cardiovascular Outcomes Research Consortium, Aurora
| | | | - Laura Tarko
- Veterans Affairs Boston Healthcare System, Boston, Massachusetts
| | - John Russo
- Veterans Affairs Boston Healthcare System, Boston, Massachusetts
- Landmark College, Putney, Vermont
| | - Saadia Qazi
- Veterans Affairs Boston Healthcare System, Boston, Massachusetts
- Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
| | - Ariela R. Orkaby
- Veterans Affairs Boston Healthcare System, Boston, Massachusetts
- Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
| | - Vidisha Tanukonda
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts
| | - Luc Djousse
- Veterans Affairs Boston Healthcare System, Boston, Massachusetts
- Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
| | - J. Michael Gaziano
- Veterans Affairs Boston Healthcare System, Boston, Massachusetts
- Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
| | - David R. Gagnon
- Veterans Affairs Boston Healthcare System, Boston, Massachusetts
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts
| | - Kelly Cho
- Veterans Affairs Boston Healthcare System, Boston, Massachusetts
- Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
| | - Peter W. F. Wilson
- Atlanta Veterans Affairs Medical Center, Decatur, Georgia
- Division of Cardiology, Emory University School of Medicine, Atlanta, Georgia
- Rollins School of Public Health, Department of Epidemiology, Emory University, Atlanta, Georgia
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Knight KE, Honerlaw J, Danciu I, Linares F, Ho YL, Gagnon DR, Rush E, Gaziano JM, Begoli E, Cho K. Standardized Architecture for a Mega-Biobank Phenomic Library: The Million Veteran Program (MVP). AMIA Jt Summits Transl Sci Proc 2020; 2020:326-334. [PMID: 32477652 PMCID: PMC7233040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Electronic health records (EHRs) provide a wealth of data for phenotype development in population health studies, and researchers invest considerable time to curate data elements and validate disease definitions. The ability to reproduce well-defined phenotypes increases data quality, comparability of results and expedites research. In this paper, we present a standardized approach to organize and capture phenotype definitions, resulting in the creation of an open, online repository of phenotypes. This resource captures phenotype development, provenance and process from the Million Veteran Program, a national mega-biobank embedded in the Veterans Health Administration (VHA). To ensure that the repository is searchable, extendable, and sustainable, it is necessary to develop both a proper digital catalog architecture and underlying metadata infrastructure to enable effective management of the data fields required to define each phenotype. Our methods provide a resource for VHA investigators and a roadmap for researchers interested in standardizing their phenotype definitions to increase portability.
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Affiliation(s)
| | - Jacqueline Honerlaw
- Division of Population Health and Data Science, Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA
| | | | | | - Yuk-Lam Ho
- Division of Population Health and Data Science, Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA
| | - David R Gagnon
- Division of Population Health and Data Science, Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA
- Department of Biostatistics, Boston University School of Public Health, Boston, MA
| | | | - J Michael Gaziano
- Division of Population Health and Data Science, Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | | | - Kelly Cho
- Division of Population Health and Data Science, Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA
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Clark K, Goldstein RL, Hart JE, Teylan M, Lazzari AA, Gagnon DR, Tun CG, Garshick E. Authors' response to letter to the editor by Zhiqiang Wu, Jiazhang Wu, and Zhibin Lan. Spinal Cord 2020; 58:840. [PMID: 32210355 DOI: 10.1038/s41393-020-0453-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2020] [Revised: 03/11/2020] [Accepted: 03/11/2020] [Indexed: 11/09/2022]
Affiliation(s)
- Kristopher Clark
- Department of Medicine, Boston University/Boston Medical Center, Boston, MA, USA
| | - Rebekah L Goldstein
- Research and Development Service, VA Boston Healthcare System, West Roxbury, MA, USA
| | - Jaime E Hart
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Merilee Teylan
- Research and Development Service, VA Boston Healthcare System, West Roxbury, MA, USA
| | - Antonio A Lazzari
- Department of Medicine, Boston University/Boston Medical Center, Boston, MA, USA.,Division of Primary Care and Rheumatology Section, VA Boston Healthcare System, West Roxbury, MA, USA
| | - David R Gagnon
- VA Cooperative Studies Program, VA Boston Healthcare System, Boston, MA, USA
| | - Carlos G Tun
- Department of Physical Medicine and Rehabilitation, VA Boston Healthcare System, Boston, MA, USA
| | - Eric Garshick
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA. .,Pulmonary, Allergy, Sleep and Critical Care Medicine Section, VA Boston Healthcare System and Harvard Medical School, West Roxbury, MA, USA.
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Raghavan S, Vassy JL, Ho YL, Song RJ, Gagnon DR, Cho K, Wilson PWF, Phillips LS. Diabetes Mellitus-Related All-Cause and Cardiovascular Mortality in a National Cohort of Adults. J Am Heart Assoc 2020; 8:e011295. [PMID: 30776949 PMCID: PMC6405678 DOI: 10.1161/jaha.118.011295] [Citation(s) in RCA: 231] [Impact Index Per Article: 57.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Background Diabetes mellitus is a risk factor for cardiovascular disease ( CVD ) and has been associated with 2- to 4-fold higher mortality. Diabetes mellitus-related mortality has not been reassessed in individuals receiving routine care in the United States in the contemporary era of CVD risk reduction. Methods and Results We retrospectively studied 963 648 adults receiving care in the US Veterans Affairs Healthcare System from 2002 to 2014; mean follow-up was 8 years. We estimated associations of diabetes mellitus status and hemoglobin A1c (HbA1c) with all-cause and CVD mortality using covariate-adjusted incidence rates and multivariable Cox proportional hazards regression. Of participants, 34% had diabetes mellitus. Compared with nondiabetic individuals, patients with diabetes mellitus had 7.0 (95% CI , 6.7-7.4) and 3.5 (95% CI, 3.3-3.7) deaths/1000-person-years higher all-cause and CVD mortality, respectively. The age-, sex-, race-, and ethnicity-adjusted hazard ratio for diabetes mellitus-related mortality was 1.29 (95% CI, 1.28-1.31), and declined with adjustment for CVD risk factors (hazard ratio, 1.18 [95% CI, 1.16-1.19]) and glycemia (hazard ratio, 1.03 [95% CI, 1.02-1.05]). Among individuals with diabetes mellitus, CVD mortality increased as HbA1c exceeded 7% (hazard ratios, 1.11 [95% CI, 1.08-1.14], 1.25 [95% CI, 1.22-1.29], and 1.52 [95% CI, 1.48-1.56] for HbA1c 7%-7.9%, 8%-8.9%, and ≥9%, respectively, relative to HbA1c 6%-6.9%). HbA1c 6% to 6.9% was associated with the lowest mortality risk irrespective of CVD history or age. Conclusions Diabetes mellitus remains significantly associated with all-cause and CVD mortality, although diabetes mellitus-related excess mortality is lower in the contemporary era than previously. We observed a gradient of mortality risk with increasing HbA1c >6% to 6.9%, suggesting HbA1c remains an informative predictor of outcomes even if causality cannot be inferred.
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Affiliation(s)
- Sridharan Raghavan
- 1 Department of Veterans Affairs Eastern Colorado Healthcare System Aurora CO.,2 Division of Hospital Medicine University of Colorado School of Medicine Aurora CO.,3 Colorado Cardiovascular Outcomes Research Consortium Aurora CO
| | - Jason L Vassy
- 4 Department of Veterans Affairs Boston Healthcare System Boston MA.,5 Department of Medicine Harvard Medical School Boston MA
| | - Yuk-Lam Ho
- 4 Department of Veterans Affairs Boston Healthcare System Boston MA
| | - Rebecca J Song
- 4 Department of Veterans Affairs Boston Healthcare System Boston MA
| | - David R Gagnon
- 4 Department of Veterans Affairs Boston Healthcare System Boston MA.,6 Department of Biostatistics Boston University School of Public Health Boston MA
| | - Kelly Cho
- 4 Department of Veterans Affairs Boston Healthcare System Boston MA.,5 Department of Medicine Harvard Medical School Boston MA
| | - Peter W F Wilson
- 7 Department of Veterans Affairs Atlanta Medical Center Atlanta GA.,8 Division of Cardiology Emory University School of Medicine Atlanta GA
| | - Lawrence S Phillips
- 7 Department of Veterans Affairs Atlanta Medical Center Atlanta GA.,9 Division of Endocrinology Emory University School of Medicine Atlanta GA
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Kurgansky KE, Schubert P, Parker R, Djousse L, Riebman JB, Gagnon DR, Joseph J. Association of pulse rate with outcomes in heart failure with reduced ejection fraction: a retrospective cohort study. BMC Cardiovasc Disord 2020; 20:92. [PMID: 32101141 PMCID: PMC7045436 DOI: 10.1186/s12872-020-01384-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2019] [Accepted: 02/10/2020] [Indexed: 01/10/2023] Open
Abstract
BACKGROUND In a real-world setting, the effect of pulse rate measured at the time of diagnosis and serially during follow-up and management, on outcomes in heart failure with reduced ejection fraction (HFrEF), has not been well-studied. Furthermore, how beta-blockade use in a real-world situation modifies this relation between pulse rate and outcomes in HFrEF is not well-known. Hence, we identified a large, national, real-world cohort of HFrEF to examine the association of pulse rate and outcomes. METHODS Using Veterans Affairs (VA) national electronic health records we identified incident HFrEF cases between 2006 and 2012. We examined the associations of both baseline and serially measured pulse rates, with mortality and days hospitalized per year for heart failure and for any cause, using crude and multivariable Cox proportional hazards and Poisson or negative binomial models, respectively. The exposure was examined as continuous, dichotomous, and categorical. Post-hoc analyses addressed the interaction of pulse rate and beta-blocker target dose. RESULTS We identified 51,194 incident HFrEF cases (67 ± 12 years, 98% male, 77% white. A significant positive, near linear relationship was observed for both baseline and serially measured pulse rates with all-cause mortality, all-cause hospitalization and heart failure hospitalization after adjusting for covariates including beta-blocker use. Patients who had a pulse rate ≥ 70 bpm in the past 6 months had 36% (95% CI: 31-42%), 25% (95% CI: 19-32%), and 51% (95% CI: 33-72%) increased rates of mortality, all-cause hospitalization, and heart failure hospitalization, respectively, compared to patients with pulse rates < 70 bpm. A minority of subjects (15%) were treated with guideline directed beta blockade ≥50% of recommended target dose, among whom better outcomes were seen compared to those who did not achieve target dose in patients with pulse rates both above and below 70 beats per minute. CONCLUSIONS High pulse rate, both at the time of diagnosis and during follow-up, is strongly associated with increased risk of adverse outcomes in HFrEF patients, independent of the use of beta-blockers. In a real-world setting, the majority of HFrEF patients do not achieve target dose of beta-blockade; greater use of strategies to reduce heart rate may improve outcomes in HFrEF.
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Affiliation(s)
- Katherine E Kurgansky
- Massachusetts Veterans Epidemiology and Research Information Center (MAVERIC), Veterans Affairs Boston Healthcare System, Boston, MA, USA
| | - Petra Schubert
- Massachusetts Veterans Epidemiology and Research Information Center (MAVERIC), Veterans Affairs Boston Healthcare System, Boston, MA, USA
| | - Rachel Parker
- Massachusetts Veterans Epidemiology and Research Information Center (MAVERIC), Veterans Affairs Boston Healthcare System, Boston, MA, USA
| | - Luc Djousse
- Massachusetts Veterans Epidemiology and Research Information Center (MAVERIC), Veterans Affairs Boston Healthcare System, Boston, MA, USA.,Department of Medicine, Division of Aging, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | | | - David R Gagnon
- Massachusetts Veterans Epidemiology and Research Information Center (MAVERIC), Veterans Affairs Boston Healthcare System, Boston, MA, USA.,Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Jacob Joseph
- Massachusetts Veterans Epidemiology and Research Information Center (MAVERIC), Veterans Affairs Boston Healthcare System, Boston, MA, USA. .,Department of Medicine, Division of Cardiovascular Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA. .,Cardiology Section, VA Boston Healthcare System, 1400 VFW Parkway, West Roxbury, MA, 02132, USA.
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Clark K, Goldstein RL, Hart JE, Teylan M, Lazzari AA, Gagnon DR, Tun CG, Garshick E. Plasma vitamin D, past chest illness, and risk of future chest illness in chronic spinal cord injury (SCI): a longitudinal observational study. Spinal Cord 2020; 58:504-512. [PMID: 31949283 DOI: 10.1038/s41393-019-0409-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2019] [Revised: 12/21/2019] [Accepted: 12/23/2019] [Indexed: 01/08/2023]
Abstract
STUDY DESIGN Observational study. OBJECTIVE Assess associations between vitamin D levels and other risk factors on future chest illness in a chronic spinal cord injury (SCI) cohort. SETTING Veterans Affairs Boston and the Boston, MA community. METHODS Between August 2009 and August 2017, 253 participants with chronic SCI were followed over a median of 3.2 years (up to 7.4 years) with two to four visits a median of 1.7 years apart. At each visit, plasma 25-hydroxyvitamin D level was obtained, spirometry performed, and a respiratory questionnaire assessing chest illnesses since last visit was completed. Repeated measures negative binomial regression was used to assess chest illness risk longitudinally. RESULTS At entry, 25% had deficient vitamin D levels (<20 nanograms/milliliter (ng/ml)), 52% were insufficient (20 to <30 ng/ml), and 23% were sufficient (≥30 ng/ml). Over 545 study visits, chest illnesses (n = 106) were reported by 60 participants. In multivariable models (including previous chest illness history), deficient vitamin D levels (compared with those with sufficient levels) were associated with future chest illness though with wide confidence limits (relative risk (RR) = 1.36, 95% confidence intervals (CI) = 0.74, 2.47). The strongest association with chest illness during the follow-up period was in persons who reported pneumonia/bronchitis after injury and a chest illness in the three years before study entry (RR = 7.62; 95% CI = 3.70, 15.71). CONCLUSION Assessed prospectively in chronic SCI, there was a suggestive association between deficient vitamin D levels and future chest illness. Past chest illness history was also strongly associated with future chest illness.
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Affiliation(s)
- Kristopher Clark
- Department of Medicine, Boston University/Boston Medical Center, Boston, MA, USA
| | - Rebekah L Goldstein
- Research and Development Service, VA Boston Healthcare System, West Roxbury, MA, USA
| | - Jaime E Hart
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Merilee Teylan
- Research and Development Service, VA Boston Healthcare System, West Roxbury, MA, USA
| | - Antonio A Lazzari
- Department of Medicine, Boston University/Boston Medical Center, Boston, MA, USA.,Division of Primary Care and Rheumatology Section, VA Boston Healthcare System, West Roxbury, MA, USA
| | - David R Gagnon
- VA Cooperative Studies Program, VA Boston Healthcare System, Boston, MA, USA
| | - Carlos G Tun
- Department of Physical Medicine and Rehabilitation, VA Boston Healthcare System, Boston, MA, USA
| | - Eric Garshick
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA. .,Pulmonary, Allergy, Sleep and Critical Care Medicine Section, VA Boston Healthcare System and Harvard Medical School, West Roxbury, MA, USA.
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Wan ES, Kantorowski A, Polak M, Kadri R, Richardson CR, Gagnon DR, Garshick E, Moy ML. Long-term effects of web-based pedometer-mediated intervention on COPD exacerbations. Respir Med 2020; 162:105878. [PMID: 32056676 DOI: 10.1016/j.rmed.2020.105878] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/23/2019] [Revised: 01/08/2020] [Accepted: 01/09/2020] [Indexed: 10/25/2022]
Abstract
BACKGROUND Technology-based physical activity (PA) interventions have been shown to improve daily step counts and health-related quality of life, but their effect on long-term clinical outcomes like acute exacerbations (AEs) is unknown in persons with COPD. METHODS U.S. Veterans with stable COPD were randomized (1:1) to either pedometer alone (control) or pedometer plus a website with feedback, goal-setting, disease education, and a community forum (intervention) for 3 months. AEs were assessed every 3 months over a follow-up period of approximately 15 months. Pedometer-assessed daily step counts, health-related quality-of-life (HRQL), and self-efficacy were assessed at baseline, end-of-intervention at 3 months, and during follow-up approximately 6 and 12 months after enrollment. Zero-inflated Poisson models assessed the effect of the intervention on risk for AEs, compared to controls. Generalized linear mixed-effects models for repeated measures examined between-group and within-group changes in daily step count, HRQL, and self-efficacy. RESULTS There were no significant differences in age, FEV1% predicted, baseline daily step count, AEs the year prior to enrollment, or duration of follow-up between the intervention (n = 57) and control (n = 52) groups. The intervention group had a significantly reduced risk of AEs (rate ratio = 0.51, [95%CI 0.31-0.85]), compared to the control group. There were no significant between-group differences in change in average daily step count, HRQL, or self-efficacy at 6 and 12 months after enrollment. CONCLUSIONS A 3-month internet-mediated, pedometer-based PA intervention was associated with reduced risk for AEs of COPD over 12-15 months of follow-up. ClinicalTrials.gov identifier: NCT01772082.
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Affiliation(s)
- Emily S Wan
- Pulmonary and Critical Care Medicine Section, VA Boston Healthcare System, Boston, MA, USA; Channing Division of Network Medicine, Brigham & Women's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Ana Kantorowski
- Pulmonary and Critical Care Medicine Section, VA Boston Healthcare System, Boston, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Madeline Polak
- Pulmonary and Critical Care Medicine Section, VA Boston Healthcare System, Boston, MA, USA
| | - Reema Kadri
- Department of Family Medicine, University of Michigan, Ann Arbor, MI, USA
| | | | - David R Gagnon
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA; Massachusetts Veterans Epidemiology Research and Information Center, Veterans Affairs Boston Healthcare System, Boston, MA, USA
| | - Eric Garshick
- Pulmonary and Critical Care Medicine Section, VA Boston Healthcare System, Boston, MA, USA; Channing Division of Network Medicine, Brigham & Women's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Marilyn L Moy
- Pulmonary and Critical Care Medicine Section, VA Boston Healthcare System, Boston, MA, USA; Harvard Medical School, Boston, MA, USA.
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Raghavan S, Ho YL, Kini V, Rhee MK, Vassy JL, Gagnon DR, Cho K, Wilson PWF, Phillips LS. Association Between Early Hypertension Control and Cardiovascular Disease Incidence in Veterans With Diabetes. Diabetes Care 2019; 42:1995-2003. [PMID: 31515207 PMCID: PMC6754236 DOI: 10.2337/dc19-0686] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/04/2019] [Accepted: 07/26/2019] [Indexed: 02/03/2023]
Abstract
OBJECTIVE Guidelines for hypertension treatment in patients with diabetes diverge regarding the systolic blood pressure (SBP) threshold at which treatment should be initiated and treatment goal. We examined associations of early SBP treatment with atherosclerotic cardiovascular disease (ASCVD) events in U.S. adults with diabetes. RESEARCH DESIGN AND METHODS We studied 43,986 patients with diabetes who newly initiated antihypertensive therapy between 2002 and 2007. Patients were classified into categories based on SBP at treatment initiation (130-139 or ≥140 mmHg) and after 2 years of treatment (100-119, 120-129, 130-139, 140-159, and ≥160 mmHg). The primary outcome was composite ASCVD events (fatal and nonfatal myocardial infarction and stroke), estimated using inverse probability of treatment-weighted Poisson regression and multivariable Cox proportional hazards regression. RESULTS Relative to individuals who initiated treatment when SBP was 130-139 mmHg, those with pretreatment SBP ≥140 mmHg had higher ASCVD risk (hazard ratio 1.10 [95% CI 1.02, 1.19]). Relative to those with pretreatment SBP of 130-139 mmHg and on-treatment SBP of 120-129 mmHg (reference group), ASCVD incidence was higher in those with pretreatment SBP ≥140 mmHg and on-treatment SBP 120-129 mmHg (adjusted incidence rate difference [IRD] 1.0 [-0.2 to 2.1] events/1,000 person-years) and in those who achieved on-treatment SBP 130-139 mmHg (IRD 1.9 [0.6, 3.2] and 1.1 [0.04, 2.2] events/1,000 person-years for those with pretreatment SBP 130-139 mmHg and ≥140 mmHg, respectively). CONCLUSIONS In this observational study, patients with diabetes initiating antihypertensive therapy when SBP was 130-139 mmHg and those achieving on-treatment SBP <130 mmHg had better outcomes than those with higher SBP levels when initiating or after 2 years on treatment.
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Affiliation(s)
- Sridharan Raghavan
- Veterans Affairs Eastern Colorado Healthcare System, Aurora, CO
- Division of Hospital Medicine, University of Colorado School of Medicine, Aurora, CO
- Colorado Cardiovascular Outcomes Research Consortium, Aurora, CO
| | - Yuk-Lam Ho
- Veterans Affairs Boston Healthcare System, Boston, MA
| | - Vinay Kini
- Colorado Cardiovascular Outcomes Research Consortium, Aurora, CO
- Division of Cardiology, University of Colorado School of Medicine, Aurora, CO
| | - Mary K Rhee
- Atlanta Veterans Affairs Medical Center, Decatur, GA
- Division of Endocrinology, Metabolism, and Lipids, Emory University School of Medicine, Atlanta, GA
| | - Jason L Vassy
- Veterans Affairs Boston Healthcare System, Boston, MA
- Department of Medicine, Harvard Medical School, Boston, MA
- Division of General Internal Medicine and Primary Care, Brigham and Women's Hospital, Boston, MA
| | - David R Gagnon
- Veterans Affairs Boston Healthcare System, Boston, MA
- Department of Biostatistics, Boston University School of Public Health, Boston, MA
| | - Kelly Cho
- Veterans Affairs Boston Healthcare System, Boston, MA
- Department of Medicine, Harvard Medical School, Boston, MA
| | - Peter W F Wilson
- Atlanta Veterans Affairs Medical Center, Decatur, GA
- Division of Cardiology, Emory University School of Medicine, Atlanta, GA
| | - Lawrence S Phillips
- Atlanta Veterans Affairs Medical Center, Decatur, GA
- Division of Endocrinology, Metabolism, and Lipids, Emory University School of Medicine, Atlanta, GA
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Ngwa JS, Cabral HJ, Cheng DM, Gagnon DR, LaValley MP, Cupples LA. GENERATING SURVIVAL TIMES WITH TIME-VARYING COVARIATES USING THE LAMBERT W FUNCTION. COMMUN STAT-SIMUL C 2019; 2019:10.1080/03610918.2019.1648822. [PMID: 33311841 PMCID: PMC7731987 DOI: 10.1080/03610918.2019.1648822] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2018] [Revised: 07/01/2019] [Accepted: 07/21/2019] [Indexed: 10/26/2022]
Abstract
Simulation studies provide an important statistical tool in evaluating survival methods, requiring an appropriate data-generating process to simulate data for an underlying statistical model. Many studies with time-to-event outcomes use the Cox proportional hazard model. While methods for simulating such data with time-invariant predictors have been described, methods for simulating data with time-varying covariates are sorely needed. Here, we describe an approach for generating data for the Cox proportional hazard model with time-varying covariates when event times follow an Exponential or Weibull distribution. For each distribution, we derive a closed-form expression to generate survival times and link the time-varying covariates with the hazard function. We consider a continuous time-varying covariate measured at regular intervals over time, as well as time-invariant covariates, in generating time-to-event data under a number of scenarios. Our results suggest this method can lead to simulation studies with reliable and robust estimation of the association parameter in Cox-Weibull and Cox-Exponential models.
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Affiliation(s)
- Julius S. Ngwa
- Department of Biostatistics, Boston University, School of Public Health, 801 Massachusetts Ave, CT 3 Floor, Boston, MA 02118, U.S.A
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, 615 North Wolfe St, Baltimore, MD 21205, U.S.A
| | - Howard J. Cabral
- Department of Biostatistics, Boston University, School of Public Health, 801 Massachusetts Ave, CT 3 Floor, Boston, MA 02118, U.S.A
| | - Debbie M. Cheng
- Department of Biostatistics, Boston University, School of Public Health, 801 Massachusetts Ave, CT 3 Floor, Boston, MA 02118, U.S.A
| | - David R. Gagnon
- Department of Biostatistics, Boston University, School of Public Health, 801 Massachusetts Ave, CT 3 Floor, Boston, MA 02118, U.S.A
| | - Michael P. LaValley
- Department of Biostatistics, Boston University, School of Public Health, 801 Massachusetts Ave, CT 3 Floor, Boston, MA 02118, U.S.A
| | - L. Adrienne Cupples
- Department of Biostatistics, Boston University, School of Public Health, 801 Massachusetts Ave, CT 3 Floor, Boston, MA 02118, U.S.A
- National Heart, Lung, and Blood Institute’s Framingham Heart Study, Framingham, MA 01702, U.S.A
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Harrington KM, Nguyen XMT, Song RJ, Hannagan K, Quaden R, Gagnon DR, Cho K, Deen JE, Muralidhar S, O'Leary TJ, Gaziano JM, Whitbourne SB. Gender Differences in Demographic and Health Characteristics of the Million Veteran Program Cohort. Womens Health Issues 2019; 29 Suppl 1:S56-S66. [PMID: 31253243 PMCID: PMC7061933 DOI: 10.1016/j.whi.2019.04.012] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2018] [Revised: 04/13/2019] [Accepted: 04/19/2019] [Indexed: 01/16/2023]
Abstract
BACKGROUND The Department of Veterans Affairs Million Veteran Program (MVP) is the largest ongoing cohort program of its kind, with 654,903 enrollees as of June 2018. The objectives of this study were to examine gender differences in the MVP cohort with respect to response and enrollment rates; demographic, health, and health care characteristics; and prevalence of self-reported health conditions. METHODS The MVP Baseline Survey was completed by 415,694 veterans (8% women), providing self-report measures of demographic characteristics, health status, and medical history. RESULTS Relative to men, women demonstrated a higher positive responder rate (23.0% vs. 16.0%), slightly higher enrollment rate (13.5% vs. 12.9%), and, among enrollees, a lower survey completion rate (59.7% vs. 63.8%). Women were younger, more racially diverse, had higher educational attainment, and were less likely to be married or cohabitating with a partner than men. Women were more likely to report good to excellent health status but poorer physical fitness, and less likely to report lifetime smoking and drinking than men. Compared with men, women veterans showed an increased prevalence of musculoskeletal conditions, thyroid problems, gastrointestinal conditions, migraine headaches, and mental health disorders, as well as a decreased prevalence of gout, cardiovascular diseases, high cholesterol, diabetes, and hearing problems. CONCLUSIONS These results revealed some substantial gender differences in the research participation rates, demographic profile, health characteristics, and prevalence of health conditions for veterans in the MVP cohort. Findings highlight the need for tailoring recruitment efforts to ensure representation of the increasing women veteran population receiving care through the Veterans Health Administration.
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Affiliation(s)
- Kelly M Harrington
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts; Department of Psychiatry, Boston University School of Medicine, Boston, Massachusetts.
| | - Xuan-Mai T Nguyen
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts; Department of Medicine, Division of Aging, Brigham and Women's Hospital, Boston, Massachusetts; Department of Medicine, Harvard Medical School, Boston, Massachusetts
| | - Rebecca J Song
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts; Department of Epidemiology, Boston University School of Public Health, Boston, Massachusetts
| | - Keri Hannagan
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts
| | - Rachel Quaden
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts
| | - David R Gagnon
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts; Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts
| | - Kelly Cho
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts; Department of Medicine, Division of Aging, Brigham and Women's Hospital, Boston, Massachusetts; Department of Medicine, Harvard Medical School, Boston, Massachusetts
| | - Jennifer E Deen
- Office of Research and Development, Veterans Health Administration, Washington, District of Columbia
| | - Sumitra Muralidhar
- Office of Research and Development, Veterans Health Administration, Washington, District of Columbia
| | - Timothy J O'Leary
- Office of Research and Development, Veterans Health Administration, Washington, District of Columbia; Department of Pathology, University of Maryland School of Medicine, Baltimore, Maryland
| | - John Michael Gaziano
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts; Department of Medicine, Division of Aging, Brigham and Women's Hospital, Boston, Massachusetts; Department of Medicine, Harvard Medical School, Boston, Massachusetts
| | - Stacey B Whitbourne
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts; Department of Medicine, Division of Aging, Brigham and Women's Hospital, Boston, Massachusetts; Department of Medicine, Harvard Medical School, Boston, Massachusetts
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Honerlaw JP, Ho YL, Nguyen XMT, Cho K, Vassy JL, Gagnon DR, O'Donnell CJ, Gaziano JM, Wilson PWF, Djousse L. Fried food consumption and risk of coronary artery disease: The Million Veteran Program. Clin Nutr 2019; 39:1203-1208. [PMID: 31279615 DOI: 10.1016/j.clnu.2019.05.008] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2018] [Revised: 05/03/2019] [Accepted: 05/06/2019] [Indexed: 01/28/2023]
Abstract
INTRODUCTION Previous studies of the relationship between fried food consumption and coronary artery disease (CAD) have yielded conflicting results. We tested the hypothesis that frequent fried food consumption is associated with a higher risk of incident CAD events in Million Veteran Program (MVP) participants. METHODS Veterans Health Administration electronic health record data were linked to questionnaires completed at MVP enrollment. Self-reported fried food consumption at baseline was categorized: (<1, 1-3, 4-6 times per week or daily). The outcome of interest was non-fatal myocardial infarction or CAD events. We fitted a Cox regression model adjusting for age, sex, race, education, exercise, smoking and alcohol consumption. RESULTS Of 154,663 MVP enrollees with survey data, mean age was 64 years and 90% were men. During a mean follow-up of approximately 3 years, there were 6,725 CAD events. There was a positive linear relationship between frequency of fried food consumption and risk of CAD (p for trend 0.0015). Multivariable adjusted hazard ratios (95% CI) were 1.0 (ref), 1.07 (1.01-1.13), 1.08 (1.01-1.16), and 1.14 (1.03-1.27) across consecutive increasing categories of fried food intake. CONCLUSIONS In a large national cohort of U.S. Veterans, fried food consumption has a positive, dose-dependent association with CAD.
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Affiliation(s)
- Jacqueline P Honerlaw
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA.
| | - Yuk-Lam Ho
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA
| | - Xuan-Mai T Nguyen
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA; Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Kelly Cho
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA; Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Jason L Vassy
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA; Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA
| | - David R Gagnon
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA; Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Christopher J O'Donnell
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA; Harvard Medical School, Boston, MA, USA
| | - J Michael Gaziano
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA; Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Peter W F Wilson
- Atlanta VA Medical Center, Decatur, GA, USA; Emory University Schools of Medicine and Public Health, Atlanta, GA, USA
| | - Luc Djousse
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA; Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA
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Harrington KM, Quaden R, Stein MB, Honerlaw JP, Cissell S, Pietrzak RH, Zhao H, Radhakrishnan K, Aslan M, Gaziano JM, Concato J, Gagnon DR, Gelernter J, Cho K. Validation of an Electronic Medical Record-Based Algorithm for Identifying Posttraumatic Stress Disorder in U.S. Veterans. J Trauma Stress 2019; 32:226-237. [PMID: 31009556 PMCID: PMC6699164 DOI: 10.1002/jts.22399] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/08/2018] [Revised: 11/21/2018] [Accepted: 11/27/2018] [Indexed: 12/28/2022]
Abstract
We developed an algorithm for identifying U.S. veterans with a history of posttraumatic stress disorder (PTSD), using the Department of Veterans Affairs (VA) electronic medical record (EMR) system. This work was motivated by the need to create a valid EMR-based phenotype to identify thousands of cases and controls for a genome-wide association study of PTSD in veterans. We used manual chart review (n = 500) as the gold standard. For both the algorithm and chart review, three classifications were possible: likely PTSD, possible PTSD, and likely not PTSD. We used Lasso regression with cross-validation to select statistically significant predictors of PTSD from the EMR and then generate a predicted probability score of being a PTSD case for every participant in the study population (range: 0-1.00). Comparing the performance of our probabilistic approach (Lasso algorithm) to a rule-based approach (International Classification of Diseases [ICD] algorithm), the Lasso algorithm showed modestly higher overall percent agreement with chart review than the ICD algorithm (80% vs. 75%), higher sensitivity (0.95 vs. 0.84), and higher accuracy (AUC = 0.95 vs. 0.90). We applied a 0.7 probability cut-point to the Lasso results to determine final PTSD case-control status for the VA population. The final algorithm had a 0.99 sensitivity, 0.99 specificity, 0.95 positive predictive value, and 1.00 negative predictive value for PTSD classification (grouping possible PTSD and likely not PTSD) as determined by chart review. This algorithm may be useful for other research and quality improvement endeavors within the VA.
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Affiliation(s)
- Kelly M. Harrington
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts, USA
- Department of Psychiatry, Boston University School of Medicine, Boston, Massachusetts, USA
| | - Rachel Quaden
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts, USA
| | - Murray B. Stein
- Psychiatry Service, VA San Diego Healthcare System, San Diego, California, USA
- Departments of Psychiatry and Family Medicine & Public Health, University of California San Diego, La Jolla, California, USA
| | - Jacqueline P. Honerlaw
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts, USA
| | - Shadha Cissell
- Psychiatry Service, VA San Diego Healthcare System, San Diego, California, USA
| | - Robert H. Pietrzak
- Psychiatry Service, VA Connecticut Healthcare System, West Haven, Connecticut, USA
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, USA
| | - Hongyu Zhao
- VA Clinical Epidemiology Research Center (CERC), VA Connecticut Healthcare System, West Haven, Connecticut, USA
- Department of Biostatistics, Yale University School of Public Health, New Haven, Connecticut, USA
| | - Krishnan Radhakrishnan
- VA Clinical Epidemiology Research Center (CERC), VA Connecticut Healthcare System, West Haven, Connecticut, USA
- Department of Internal Medicine, University of Kentucky College of Medicine, Lexington, Kentucky, USA
| | - Mihaela Aslan
- VA Clinical Epidemiology Research Center (CERC), VA Connecticut Healthcare System, West Haven, Connecticut, USA
- Department of Medicine, Yale University School of Medicine, New Haven, Connecticut, USA
| | - John Michael Gaziano
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts, USA
- Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - John Concato
- VA Clinical Epidemiology Research Center (CERC), VA Connecticut Healthcare System, West Haven, Connecticut, USA
- Department of Medicine, Yale University School of Medicine, New Haven, Connecticut, USA
| | - David R. Gagnon
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, USA
| | - Joel Gelernter
- Psychiatry Service, VA Connecticut Healthcare System, West Haven, Connecticut, USA
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, USA
- Departments of Genetics and Neuroscience, Yale University School of Medicine, New Haven, Connecticut, USA
| | - Kelly Cho
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts, USA
- Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
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