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Cai CX, Nishimura A, Bowring MG, Westlund E, Tran D, Ng JH, Nagy P, Cook M, McLeggon JA, DuVall SL, Matheny ME, Golozar A, Ostropolets A, Minty E, Desai P, Bu F, Toy B, Hribar M, Falconer T, Zhang L, Lawrence-Archer L, Boland MV, Goetz K, Hall N, Shoaibi A, Reps J, Sena AG, Blacketer C, Swerdel J, Jhaveri KD, Lee E, Gilbert Z, Zeger SL, Crews DC, Suchard MA, Hripcsak G, Ryan PB. Similar Risk of Kidney Failure among Patients with Blinding Diseases Who Receive Ranibizumab, Aflibercept, and Bevacizumab: An Observational Health Data Sciences and Informatics Network Study. Ophthalmol Retina 2024:S2468-6530(24)00118-0. [PMID: 38519026 DOI: 10.1016/j.oret.2024.03.014] [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] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Revised: 03/08/2024] [Accepted: 03/12/2024] [Indexed: 03/24/2024]
Abstract
PURPOSE To characterize the incidence of kidney failure associated with intravitreal anti-VEGF exposure; and compare the risk of kidney failure in patients treated with ranibizumab, aflibercept, or bevacizumab. DESIGN Retrospective cohort study across 12 databases in the Observational Health Data Sciences and Informatics (OHDSI) network. SUBJECTS Subjects aged ≥ 18 years with ≥ 3 monthly intravitreal anti-VEGF medications for a blinding disease (diabetic retinopathy, diabetic macular edema, exudative age-related macular degeneration, or retinal vein occlusion). METHODS The standardized incidence proportions and rates of kidney failure while on treatment with anti-VEGF were calculated. For each comparison (e.g., aflibercept versus ranibizumab), patients from each group were matched 1:1 using propensity scores. Cox proportional hazards models were used to estimate the risk of kidney failure while on treatment. A random effects meta-analysis was performed to combine each database's hazard ratio (HR) estimate into a single network-wide estimate. MAIN OUTCOME MEASURES Incidence of kidney failure while on anti-VEGF treatment, and time from cohort entry to kidney failure. RESULTS Of the 6.1 million patients with blinding diseases, 37 189 who received ranibizumab, 39 447 aflibercept, and 163 611 bevacizumab were included; the total treatment exposure time was 161 724 person-years. The average standardized incidence proportion of kidney failure was 678 per 100 000 persons (range, 0-2389), and incidence rate 742 per 100 000 person-years (range, 0-2661). The meta-analysis HR of kidney failure comparing aflibercept with ranibizumab was 1.01 (95% confidence interval [CI], 0.70-1.47; P = 0.45), ranibizumab with bevacizumab 0.95 (95% CI, 0.68-1.32; P = 0.62), and aflibercept with bevacizumab 0.95 (95% CI, 0.65-1.39; P = 0.60). CONCLUSIONS There was no substantially different relative risk of kidney failure between those who received ranibizumab, bevacizumab, or aflibercept. Practicing ophthalmologists and nephrologists should be aware of the risk of kidney failure among patients receiving intravitreal anti-VEGF medications and that there is little empirical evidence to preferentially choose among the specific intravitreal anti-VEGF agents. FINANCIAL DISCLOSURES Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
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Affiliation(s)
- Cindy X Cai
- Wilmer Eye Institute, Johns Hopkins School of Medicine, Baltimore, Maryland.
| | - Akihiko Nishimura
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Mary G Bowring
- Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, Maryland
| | - Erik Westlund
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Diep Tran
- Wilmer Eye Institute, Johns Hopkins School of Medicine, Baltimore, Maryland
| | - Jia H Ng
- Division of Kidney Diseases and Hypertension, Donald and Barbara School of Medicine at Hofstra/Northwell, New York
| | - Paul Nagy
- Department of Biomedical Informatics and Data Science, Johns Hopkins School of Medicine, Johns Hopkins University, Baltimore, Maryland
| | | | - Jody-Ann McLeggon
- Department of Biomedical Informatics, Columbia University, New York, New York
| | - Scott L DuVall
- VA Informatics and Computing Infrastructure, US Department of Veterans Affairs, Salt Lake City, Utah; Department of Internal Medicine Division of Epidemiology, University of Utah School of Medicine, Salt Lake City, Utah
| | - Michael E Matheny
- VA Informatics and Computing Infrastructure, Tennessee Valley Healthcare System, Nashville, Tennessee; Department of Biomedical Informatics, Vanderbilt University, Nashville, Tennessee
| | - Asieh Golozar
- Odysseus Data Services, Inc., Cambridge, Massachusetts; OHDSI Center at the Roux Institute, Northeastern University, Boston, Massachusetts
| | | | - Evan Minty
- O'Brien Center for Public Health, Department of Medicine, University of Calgary, Canada
| | - Priya Desai
- Technology / Digital Solutions, Stanford Health Care and Stanford University School of Medicine, Palo Alto, California
| | - Fan Bu
- Department of Biostatistics, University of California - Los Angeles, Los Angeles, California
| | - Brian Toy
- Roski Eye Institute, Keck School of Medicine, University of Southern California; Los Angeles, California
| | - Michelle Hribar
- National Eye Institute, National Institutes of Health, Bethesda, Maryland; Casey Eye Institute, Oregon Health & Science University, Portland, Oregon
| | - Thomas Falconer
- Department of Biomedical Informatics, Columbia University, New York, New York
| | - Linying Zhang
- Department of Biomedical Informatics, Columbia University, New York, New York
| | - Laurence Lawrence-Archer
- Odysseus Data Services, Inc., Cambridge, Massachusetts; OHDSI Center at the Roux Institute, Northeastern University, Boston, Massachusetts
| | - Michael V Boland
- Mass Eye and Ear, and Harvard Medical School, Boston, Massachusetts
| | - Kerry Goetz
- National Eye Institute, National Institutes of Health, Bethesda, Maryland
| | - Nathan Hall
- Janssen Research and Development, Titusville, New Jersey
| | - Azza Shoaibi
- Janssen Research and Development, Titusville, New Jersey
| | - Jenna Reps
- Janssen Research and Development, Titusville, New Jersey
| | - Anthony G Sena
- Janssen Research and Development, Titusville, New Jersey; Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, the Netherlands
| | | | - Joel Swerdel
- Janssen Research and Development, Titusville, New Jersey
| | - Kenar D Jhaveri
- Glomerular Center at Northwell Health, Division of Kidney Diseases and Hypertension, Donald and Barbara School of Medicine at Hofstra/Northwell, New York
| | - Edward Lee
- Roski Eye Institute, Keck School of Medicine, University of Southern California; Los Angeles, California
| | - Zachary Gilbert
- Roski Eye Institute, Keck School of Medicine, University of Southern California; Los Angeles, California
| | - Scott L Zeger
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Deidra C Crews
- Division of Nephrology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Marc A Suchard
- VA Informatics and Computing Infrastructure, US Department of Veterans Affairs, Salt Lake City, Utah; Department of Biostatistics, University of California - Los Angeles, Los Angeles, California
| | - George Hripcsak
- Department of Biomedical Informatics, Columbia University, New York, New York
| | - Patrick B Ryan
- Janssen Research and Development, Titusville, New Jersey
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Khera R, Aminorroaya A, Dhingra LS, Thangaraj PM, Camargos AP, Bu F, Ding X, Nishimura A, Anand TV, Arshad F, Blacketer C, Chai Y, Chattopadhyay S, Cook M, Dorr DA, Duarte-Salles T, DuVall SL, Falconer T, French TE, Hanchrow EE, Kaur G, Lau WC, Li J, Li K, Liu Y, Lu Y, Man KK, Matheny ME, Mathioudakis N, McLeggon JA, McLemore MF, Minty E, Morales DR, Nagy P, Ostropolets A, Pistillo A, Phan TP, Pratt N, Reyes C, Richter L, Ross J, Ruan E, Seager SL, Simon KR, Viernes B, Yang J, Yin C, You SC, Zhou JJ, Ryan PB, Schuemie MJ, Krumholz HM, Hripcsak G, Suchard MA. Comparative Effectiveness of Second-line Antihyperglycemic Agents for Cardiovascular Outcomes: A Large-scale, Multinational, Federated Analysis of the LEGEND-T2DM Study. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.02.05.24302354. [PMID: 38370787 PMCID: PMC10871374 DOI: 10.1101/2024.02.05.24302354] [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] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
Background SGLT2 inhibitors (SGLT2is) and GLP-1 receptor agonists (GLP1-RAs) reduce major adverse cardiovascular events (MACE) in patients with type 2 diabetes mellitus (T2DM). However, their effectiveness relative to each other and other second-line antihyperglycemic agents is unknown, without any major ongoing head-to-head trials. Methods Across the LEGEND-T2DM network, we included ten federated international data sources, spanning 1992-2021. We identified 1,492,855 patients with T2DM and established cardiovascular disease (CVD) on metformin monotherapy who initiated one of four second-line agents (SGLT2is, GLP1-RAs, dipeptidyl peptidase 4 inhibitor [DPP4is], sulfonylureas [SUs]). We used large-scale propensity score models to conduct an active comparator, target trial emulation for pairwise comparisons. After evaluating empirical equipoise and population generalizability, we fit on-treatment Cox proportional hazard models for 3-point MACE (myocardial infarction, stroke, death) and 4-point MACE (3-point MACE + heart failure hospitalization) risk, and combined hazard ratio (HR) estimates in a random-effects meta-analysis. Findings Across cohorts, 16·4%, 8·3%, 27·7%, and 47·6% of individuals with T2DM initiated SGLT2is, GLP1-RAs, DPP4is, and SUs, respectively. Over 5·2 million patient-years of follow-up and 489 million patient-days of time at-risk, there were 25,982 3-point MACE and 41,447 4-point MACE events. SGLT2is and GLP1-RAs were associated with a lower risk for 3-point MACE compared with DPP4is (HR 0·89 [95% CI, 0·79-1·00] and 0·83 [0·70-0·98]), and SUs (HR 0·76 [0·65-0·89] and 0·71 [0·59-0·86]). DPP4is were associated with a lower 3-point MACE risk versus SUs (HR 0·87 [0·79-0·95]). The pattern was consistent for 4-point MACE for the comparisons above. There were no significant differences between SGLT2is and GLP1-RAs for 3-point or 4-point MACE (HR 1·06 [0·96-1·17] and 1·05 [0·97-1·13]). Interpretation In patients with T2DM and established CVD, we found comparable cardiovascular risk reduction with SGLT2is and GLP1-RAs, with both agents more effective than DPP4is, which in turn were more effective than SUs. These findings suggest that the use of GLP1-RAs and SGLT2is should be prioritized as second-line agents in those with established CVD. Funding National Institutes of Health, United States Department of Veterans Affairs.
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Affiliation(s)
- Rohan Khera
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University, New Haven, CT, 06510, USA
- Center for Outcomes Research and Evaluation (CORE), Yale New Haven Hospital, New Haven, CT, 06510, USA
- Section of Health Informatics, Department of Biostatistics, Yale School of Public Health, New Haven, CT, 06520, USA
| | - Arya Aminorroaya
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University, New Haven, CT, 06510, USA
| | - Lovedeep Singh Dhingra
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University, New Haven, CT, 06510, USA
| | - Phyllis M Thangaraj
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University, New Haven, CT, 06510, USA
| | - Aline Pedroso Camargos
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University, New Haven, CT, 06510, USA
| | - Fan Bu
- Department of Biostatistics, University of Michigan - Ann Arbor, Ann Arbor, MI, 48105, USA
| | - Xiyu Ding
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, 21205, USA
| | - Akihiko Nishimura
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, 21205, USA
| | - Tara V Anand
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, 10027, USA
| | - Faaizah Arshad
- Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Clair Blacketer
- Observational Health Data Analytics, Janssen Research and Development, LLC, Titusville, NJ, 8560, USA
| | - Yi Chai
- Department of Pharmacology and Pharmacy, LKS Faculty of Medicine, The University of Hong Kong
| | - Shounak Chattopadhyay
- Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Michael Cook
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, 21205, USA
| | - David A Dorr
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR, USA
| | - Talita Duarte-Salles
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, 8007, Spain
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Scott L DuVall
- Veterans Affairs Informatics and Computing Infrastructure, United States Department of Veterans Affairs, Salt Lake City, UT, USA
- Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Thomas Falconer
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, 10027, USA
| | - Tina E French
- Tennessee Valley Healthcare System, Veterans Affairs Medical Center, Nashville, TN, USA
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Elizabeth E Hanchrow
- Tennessee Valley Healthcare System, Veterans Affairs Medical Center, Nashville, TN, USA
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Guneet Kaur
- Division of Population Health and Genomics, School of Medicine, University of Dundee, Dundee, DD1 9SY, United Kingdom
| | - Wallis Cy Lau
- Research Department of Practice and Policy, School of Pharmacy, University College London, London, WC1H 9JP, United Kingdom
- Centre for Medicines Optimisation Research and Education, University College London Hospitals NHS Foundation Trust, London, United Kingdom
- Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong
- Laboratory of Data Discovery for Health (D24H), Hong Kong Science Park, Hong Kong, Hong Kong
| | - Jing Li
- Data Transformation, Analytics, and Artificial Intelligence, Real World Solutions, IQVIA, Durham, NC, USA
| | - Kelly Li
- Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Yuntian Liu
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University, New Haven, CT, 06510, USA
- Center for Outcomes Research and Evaluation (CORE), Yale New Haven Hospital, New Haven, CT, 06510, USA
| | - Yuan Lu
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University, New Haven, CT, 06510, USA
| | - Kenneth Kc Man
- Research Department of Practice and Policy, School of Pharmacy, University College London, London, WC1H 9JP, United Kingdom
- Centre for Medicines Optimisation Research and Education, University College London Hospitals NHS Foundation Trust, London, United Kingdom
- Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong
- Laboratory of Data Discovery for Health (D24H), Hong Kong Science Park, Hong Kong, Hong Kong
| | - Michael E Matheny
- Tennessee Valley Healthcare System, Veterans Affairs Medical Center, Nashville, TN, USA
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Nestoras Mathioudakis
- Division of Endocrinology, Diabetes, and Metabolism, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Jody-Ann McLeggon
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, 10027, USA
| | - Michael F McLemore
- Tennessee Valley Healthcare System, Veterans Affairs Medical Center, Nashville, TN, USA
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Evan Minty
- Faculty of Medicine, O'Brien Institute for Public Health, University of Calgary, Calgary, AB, T2N4N1, Canada
| | - Daniel R Morales
- Division of Population Health and Genomics, School of Medicine, University of Dundee, Dundee, DD1 9SY, United Kingdom
| | - Paul Nagy
- Division of Endocrinology, Diabetes, and Metabolism, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Anna Ostropolets
- Observational Health Data Analytics, Janssen Research and Development, LLC, Titusville, NJ, 8560, USA
| | - Andrea Pistillo
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, 8007, Spain
| | | | - Nicole Pratt
- Quality Use of Medicines and Pharmacy Research Centre, UniSA Clinical and Health Sciences, University of South Australia, Adelaide, Australia
| | - Carlen Reyes
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, 8007, Spain
| | - Lauren Richter
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, 10027, USA
| | - Joseph Ross
- Section of General Medicine and National Clinician Scholars Program, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
- Center for Outcomes Research and Evaluation (CORE), Yale New Haven Hospital, New Haven, CT, 06510, USA
| | - Elise Ruan
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, 10027, USA
| | - Sarah L Seager
- Data Transformation, Analytics, and Artificial Intelligence, Real World Solutions, IQVIA, London, UK
| | - Katherine R Simon
- Tennessee Valley Healthcare System, Veterans Affairs Medical Center, Nashville, TN, USA
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Benjamin Viernes
- Veterans Affairs Informatics and Computing Infrastructure, United States Department of Veterans Affairs, Salt Lake City, UT, USA
- Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Jianxiao Yang
- Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Can Yin
- Data Transformation, Analytics, and Artificial Intelligence, Real World Solutions, IQVIA, Shanghai, China
| | - Seng Chan You
- Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, South Korea
- Institute for Innovation in Digital Healthcare, Yonsei University College of Medicine, Seoul, South Korea
| | - Jin J Zhou
- Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, 90095, USA
- Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90024, USA
| | - Patrick B Ryan
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, 10027, USA
| | - Martijn J Schuemie
- Epidemiology, Office of the Chief Medical Officer, Johnson & Johnson, Titusville, NJ, 8560, USA
| | - Harlan M Krumholz
- Center for Outcomes Research and Evaluation (CORE), Yale New Haven Hospital, New Haven, CT, 06510, USA
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University, New Haven, CT, 06510, USA
- Section of Cardiovascular Medicine, Department of Health Policy and Management, Yale School of Public Health, New Haven, CT, 06510, USA
| | - George Hripcsak
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, 10027, USA
| | - Marc A Suchard
- Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, 90095, USA
- Department of Biomathematics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Veterans Affairs Informatics and Computing Infrastructure, United States Department of Veterans Affairs, Salt Lake City, UT, USA
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3
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Khera R, Dhingra LS, Aminorroaya A, Li K, Zhou JJ, Arshad F, Blacketer C, Bowring MG, Bu F, Cook M, Dorr DA, Duarte-Salles T, DuVall SL, Falconer T, French TE, Hanchrow EE, Horban S, Lau WCY, Li J, Liu Y, Lu Y, Man KKC, Matheny ME, Mathioudakis N, McLemore MF, Minty E, Morales DR, Nagy P, Nishimura A, Ostropolets A, Pistillo A, Posada JD, Pratt N, Reyes C, Ross JS, Seager S, Shah N, Simon K, Wan EYF, Yang J, Yin C, You SC, Schuemie MJ, Ryan PB, Hripcsak G, Krumholz H, Suchard MA. Multinational patterns of second line antihyperglycaemic drug initiation across cardiovascular risk groups: federated pharmacoepidemiological evaluation in LEGEND-T2DM. BMJ Med 2023; 2:e000651. [PMID: 37829182 PMCID: PMC10565313 DOI: 10.1136/bmjmed-2023-000651] [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] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Accepted: 07/07/2023] [Indexed: 10/14/2023]
Abstract
Objective To assess the uptake of second line antihyperglycaemic drugs among patients with type 2 diabetes mellitus who are receiving metformin. Design Federated pharmacoepidemiological evaluation in LEGEND-T2DM. Setting 10 US and seven non-US electronic health record and administrative claims databases in the Observational Health Data Sciences and Informatics network in eight countries from 2011 to the end of 2021. Participants 4.8 million patients (≥18 years) across US and non-US based databases with type 2 diabetes mellitus who had received metformin monotherapy and had initiated second line treatments. Exposure The exposure used to evaluate each database was calendar year trends, with the years in the study that were specific to each cohort. Main outcomes measures The outcome was the incidence of second line antihyperglycaemic drug use (ie, glucagon-like peptide-1 receptor agonists, sodium-glucose cotransporter-2 inhibitors, dipeptidyl peptidase-4 inhibitors, and sulfonylureas) among individuals who were already receiving treatment with metformin. The relative drug class level uptake across cardiovascular risk groups was also evaluated. Results 4.6 million patients were identified in US databases, 61 382 from Spain, 32 442 from Germany, 25 173 from the UK, 13 270 from France, 5580 from Scotland, 4614 from Hong Kong, and 2322 from Australia. During 2011-21, the combined proportional initiation of the cardioprotective antihyperglycaemic drugs (glucagon-like peptide-1 receptor agonists and sodium-glucose cotransporter-2 inhibitors) increased across all data sources, with the combined initiation of these drugs as second line drugs in 2021 ranging from 35.2% to 68.2% in the US databases, 15.4% in France, 34.7% in Spain, 50.1% in Germany, and 54.8% in Scotland. From 2016 to 2021, in some US and non-US databases, uptake of glucagon-like peptide-1 receptor agonists and sodium-glucose cotransporter-2 inhibitors increased more significantly among populations with no cardiovascular disease compared with patients with established cardiovascular disease. No data source provided evidence of a greater increase in the uptake of these two drug classes in populations with cardiovascular disease compared with no cardiovascular disease. Conclusions Despite the increase in overall uptake of cardioprotective antihyperglycaemic drugs as second line treatments for type 2 diabetes mellitus, their uptake was lower in patients with cardiovascular disease than in people with no cardiovascular disease over the past decade. A strategy is needed to ensure that medication use is concordant with guideline recommendations to improve outcomes of patients with type 2 diabetes mellitus.
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Affiliation(s)
- Rohan Khera
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University, New Haven, CT, USA
- Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, CT, USA
- Section of Health Informatics, Department of Biostatistics, Yale University School of Public Health, New Haven, CT, USA
| | - Lovedeep Singh Dhingra
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University, New Haven, CT, USA
| | - Arya Aminorroaya
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University, New Haven, CT, USA
| | - Kelly Li
- Department of Biostatistics, Fielding School of Public Health, University of California Los Angeles, Los Angeles, CA, USA
| | - Jin J Zhou
- Department of Biostatistics, Fielding School of Public Health, University of California Los Angeles, Los Angeles, CA, USA
- Department of Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Faaizah Arshad
- Department of Biostatistics, Fielding School of Public Health, University of California Los Angeles, Los Angeles, CA, USA
| | - Clair Blacketer
- Observational Health Data Analytics, Janssen Research and Development, Titusville, NJ, USA
| | - Mary G Bowring
- Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Fan Bu
- Department of Biostatistics, Fielding School of Public Health, University of California Los Angeles, Los Angeles, CA, USA
| | - Michael Cook
- Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - David A Dorr
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University School of Medicine, Portland, OR, USA
| | - Talita Duarte-Salles
- Real-World Epidemiology Research Group, Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | - Scott L DuVall
- Veterans Affairs Informatics and Computing Infrastructure, United States Department of Veterans Affairs, Salt Lake City, UT, USA
- The University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Thomas Falconer
- Department of Biomedical Informatics, Columbia University, New York, NY, USA
| | - Tina E French
- Tennessee Valley Healthcare System, Veterans Affairs Medical Center, Nashville, TN, USA
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Elizabeth E Hanchrow
- Tennessee Valley Healthcare System, Veterans Affairs Medical Center, Nashville, TN, USA
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Scott Horban
- Division of Population Health and Genomics, School of Medicine, University of Dundee, Dundee, UK
| | - Wallis CY Lau
- Research Department of Practice and Policy, School of Pharmacy, University College London, London, UK
- Centre for Medicines Optimisation Research and Education, University College London Hospitals NHS Foundation Trust, London, UK
- Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong, China
- Laboratory of Data Discovery for Health (D24H), Hong Kong Science Park, Hong Kong, China
| | - Jing Li
- Data Transformation, Analytics, and Artificial Intelligence, Real World Solutions, IQVIA Inc, Durham, NC, USA
| | - Yuntian Liu
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University, New Haven, CT, USA
- Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, CT, USA
| | - Yuan Lu
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University, New Haven, CT, USA
| | - Kenneth KC Man
- Research Department of Practice and Policy, School of Pharmacy, University College London, London, UK
- Centre for Medicines Optimisation Research and Education, University College London Hospitals NHS Foundation Trust, London, UK
- Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong, China
- Laboratory of Data Discovery for Health (D24H), Hong Kong Science Park, Hong Kong, China
| | - Michael E Matheny
- Tennessee Valley Healthcare System, Veterans Affairs Medical Center, Nashville, TN, USA
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Nestoras Mathioudakis
- Division of Endocrinology, Diabetes, and Metabolism, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Michael F McLemore
- Tennessee Valley Healthcare System, Veterans Affairs Medical Center, Nashville, TN, USA
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Evan Minty
- Faculty of Medicine, O'Brien Institute for Public Health, University of Calgary, Calgary, AB, Canada
| | - Daniel R Morales
- Division of Population Health and Genomics, School of Medicine, University of Dundee, Dundee, UK
| | - Paul Nagy
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Division of Health Science Informatics, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, USA
| | - Akihiko Nishimura
- Department of Biostatistics, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, USA
| | - Anna Ostropolets
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA
| | - Andrea Pistillo
- Real-World Epidemiology Research Group, Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | - Jose D Posada
- Systems Engineering and Computing, School of Engineering, Universidad del Norte, Barranquilla, Colombia
| | - Nicole Pratt
- Quality Use of Medicines and Pharmacy Research Centre, UniSA Clinical and Health Sciences, University of South Australia, Adelaide, SA, Australia
| | - Carlen Reyes
- Real-World Epidemiology Research Group, Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | - Joseph S Ross
- Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, CT, USA
- Section of General Medicine and National Clinician Scholars Program, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
- Department of Health Policy and Management, Yale University School of Public Health, New Haven, CT, USA
| | - Sarah Seager
- Data Transformation, Analytics, and Artificial Intelligence, Real World Solutions, IQVIA Inc, Durham, NC, USA
| | - Nigam Shah
- Center for Biomedical Informatics Research, Stanford University School of Medicine, Stanford, CA, USA
- Clinical Excellence Research Center, Stanford University School of Medicine, Stanford, CA, USA
- Technology and Digital Solutions, Stanford Health Care, Stanford, CA, USA
| | - Katherine Simon
- Tennessee Valley Healthcare System, Veterans Affairs Medical Center, Nashville, TN, USA
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Eric YF Wan
- Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong, China
- Laboratory of Data Discovery for Health (D24H), Hong Kong Science Park, Hong Kong, China
- Department of Family Medicine and Primary Care, School of Clinical Medicine, University of Hong Kong, Hong Kong, China
| | - Jianxiao Yang
- Department of Computational Medicine, University of California Los Angeles David Geffen School of Medicine, Los Angeles, CA, USA
| | - Can Yin
- Data Transformation, Analytics, and Artificial Intelligence, Real World Solutions, IQVIA Inc, Durham, NC, USA
| | - Seng Chan You
- Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, Republic of Korea (aka South Korea)
- Institute for Innovation in Digital Healthcare, Yonsei University, Seoul, Republic of Korea (aka South Korea)
| | - Martijn J Schuemie
- Epidemiology, Office of the Chief Medical Officer, Johnson & Johnson, Titusville, NJ, USA
| | - Patrick B Ryan
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA
| | - George Hripcsak
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA
| | - Harlan Krumholz
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University, New Haven, CT, USA
- Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, CT, USA
- Department of Health Policy and Management, Yale University School of Public Health, New Haven, CT, USA
| | - Marc A Suchard
- Department of Biostatistics, Fielding School of Public Health, University of California Los Angeles, Los Angeles, CA, USA
- Veterans Affairs Informatics and Computing Infrastructure, United States Department of Veterans Affairs, Salt Lake City, UT, USA
- Department of Biomathematics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
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4
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Minty E, Bray E, Bachus CB, Everett B, Smith KM, Matijevich E, Hajizadeh M, Armstrong DG, Liden B. Preventative Sensor-Based Remote Monitoring of the Diabetic Foot in Clinical Practice. Sensors (Basel) 2023; 23:6712. [PMID: 37571496 PMCID: PMC10422561 DOI: 10.3390/s23156712] [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] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 07/13/2023] [Accepted: 07/17/2023] [Indexed: 08/13/2023]
Abstract
Diabetes and its complications, particularly diabetic foot ulcers (DFUs), pose significant challenges to healthcare systems worldwide. DFUs result in severe consequences such as amputation, increased mortality rates, reduced mobility, and substantial healthcare costs. The majority of DFUs are preventable and treatable through early detection. Sensor-based remote patient monitoring (RPM) has been proposed as a possible solution to overcome limitations, and enhance the effectiveness, of existing foot care best practices. However, there are limited frameworks available on how to approach and act on data collected through sensor-based RPM in DFU prevention. This perspective article offers insights from deploying sensor-based RPM through digital DFU prevention regimens. We summarize the data domains and technical architecture that characterize existing commercially available solutions. We then highlight key elements for effective RPM integration based on these new data domains, including appropriate patient selection and the need for detailed clinical assessments to contextualize sensor data. Guidance on establishing escalation pathways for remotely monitored at-risk patients and the importance of predictive system management is provided. DFU prevention RPM should be integrated into a comprehensive disease management strategy to mitigate foot health concerns, reduce activity-associated risks, and thereby seek to be synergistic with other components of diabetes disease management. This integrated approach has the potential to enhance disease management in diabetes, positively impacting foot health and the healthspan of patients living with diabetes.
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Affiliation(s)
- Evan Minty
- Cumming School of Medicine, University of Calgary, Calgary, AB T2N 4N1, Canada
| | - Emily Bray
- Orpyx Medical Technologies, Inc., Calgary, AB T2G 1M8, Canada (E.M.); (M.H.)
| | - Courtney B. Bachus
- Orpyx Medical Technologies, Inc., Calgary, AB T2G 1M8, Canada (E.M.); (M.H.)
| | - Breanne Everett
- Orpyx Medical Technologies, Inc., Calgary, AB T2G 1M8, Canada (E.M.); (M.H.)
| | - Karen M. Smith
- Orpyx Medical Technologies, Inc., Calgary, AB T2G 1M8, Canada (E.M.); (M.H.)
| | - Emily Matijevich
- Orpyx Medical Technologies, Inc., Calgary, AB T2G 1M8, Canada (E.M.); (M.H.)
| | - Maryam Hajizadeh
- Orpyx Medical Technologies, Inc., Calgary, AB T2G 1M8, Canada (E.M.); (M.H.)
| | - David G. Armstrong
- Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
- USC Limb Preservation Program, Los Angeles, CA 90033, USA
- Southwestern Academic Limb Salvage Alliance (SALSA), Los Angeles, CA 90033, USA
- USC Center to Stream Healthcare in Place (C2SHIP), Los Angeles, CA 90033, USA
| | - Brock Liden
- Cutting Edge Research, Circleville, OH 43113, USA
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5
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Spotnitz M, Acharya N, Cimino JJ, Murphy S, Namjou B, Crimmins N, Walunas T, Liu C, Crosslin D, Benoit B, Rosenthal E, Pacheco JA, Ostropolets A, Reyes Nieva H, Patterson JS, Richter LR, Callahan TJ, Elhussein A, Pang C, Kiryluk K, Nestor J, Khan A, Mohan S, Minty E, Chung W, Wei WQ, Natarajan K, Weng C. A metadata framework for computational phenotypes. JAMIA Open 2023; 6:ooad032. [PMID: 37181728 PMCID: PMC10168627 DOI: 10.1093/jamiaopen/ooad032] [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] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 04/10/2023] [Accepted: 04/21/2023] [Indexed: 05/16/2023] Open
Abstract
With the burgeoning development of computational phenotypes, it is increasingly difficult to identify the right phenotype for the right tasks. This study uses a mixed-methods approach to develop and evaluate a novel metadata framework for retrieval of and reusing computational phenotypes. Twenty active phenotyping researchers from 2 large research networks, Electronic Medical Records and Genomics and Observational Health Data Sciences and Informatics, were recruited to suggest metadata elements. Once consensus was reached on 39 metadata elements, 47 new researchers were surveyed to evaluate the utility of the metadata framework. The survey consisted of 5-Likert multiple-choice questions and open-ended questions. Two more researchers were asked to use the metadata framework to annotate 8 type-2 diabetes mellitus phenotypes. More than 90% of the survey respondents rated metadata elements regarding phenotype definition and validation methods and metrics positively with a score of 4 or 5. Both researchers completed annotation of each phenotype within 60 min. Our thematic analysis of the narrative feedback indicates that the metadata framework was effective in capturing rich and explicit descriptions and enabling the search for phenotypes, compliance with data standards, and comprehensive validation metrics. Current limitations were its complexity for data collection and the entailed human costs.
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Affiliation(s)
- Matthew Spotnitz
- Department of Biomedical Informatics, Vagelos College of Physicians & Surgeons, Columbia University Irving Medical Center, New York, New York, USA
| | - Nripendra Acharya
- Department of Biomedical Informatics, Vagelos College of Physicians & Surgeons, Columbia University Irving Medical Center, New York, New York, USA
| | - James J Cimino
- Informatics Institute, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Shawn Murphy
- Laboratory of Computer Science, Mass General Brigham, Boston, Massachusetts, USA
- Department of Neurology, Mass General Brigham, Boston, Massachusetts, USA
| | - Bahram Namjou
- Department of Pediatrics, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, USA
| | - Nancy Crimmins
- Department of Pediatrics, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, USA
| | - Theresa Walunas
- Department of Medicine, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
| | - Cong Liu
- Department of Biomedical Informatics, Vagelos College of Physicians & Surgeons, Columbia University Irving Medical Center, New York, New York, USA
| | - David Crosslin
- Division of Biomedical Informatics and Genomics, Tulane University School of Medicine, New Orleans, Louisiana, USA
| | - Barbara Benoit
- Department of Research Information Science & Computing, Mass General Brigham, Boston, Massachusetts, USA
| | | | - Jennifer A Pacheco
- Center for Genetic Medicine, Northwestern University, Chicago, Illinois, USA
| | - Anna Ostropolets
- Department of Biomedical Informatics, Vagelos College of Physicians & Surgeons, Columbia University Irving Medical Center, New York, New York, USA
| | - Harry Reyes Nieva
- Department of Biomedical Informatics, Vagelos College of Physicians & Surgeons, Columbia University Irving Medical Center, New York, New York, USA
| | - Jason S Patterson
- Department of Biomedical Informatics, Vagelos College of Physicians & Surgeons, Columbia University Irving Medical Center, New York, New York, USA
| | - Lauren R Richter
- Department of Biomedical Informatics, Vagelos College of Physicians & Surgeons, Columbia University Irving Medical Center, New York, New York, USA
| | - Tiffany J Callahan
- Department of Biomedical Informatics, Vagelos College of Physicians & Surgeons, Columbia University Irving Medical Center, New York, New York, USA
| | - Ahmed Elhussein
- Department of Biomedical Informatics, Vagelos College of Physicians & Surgeons, Columbia University Irving Medical Center, New York, New York, USA
| | - Chao Pang
- Department of Biomedical Informatics, Vagelos College of Physicians & Surgeons, Columbia University Irving Medical Center, New York, New York, USA
| | - Krzysztof Kiryluk
- Division of Nephrology, Department of Medicine, Vagelos College of Physicians & Surgeons, Columbia University Irving Medical Center, New York, New York, USA
| | - Jordan Nestor
- Division of Nephrology, Department of Medicine, Vagelos College of Physicians & Surgeons, Columbia University Irving Medical Center, New York, New York, USA
| | - Atlas Khan
- Division of Nephrology, Department of Medicine, Vagelos College of Physicians & Surgeons, Columbia University Irving Medical Center, New York, New York, USA
| | - Sumit Mohan
- Division of Nephrology, Department of Medicine, Vagelos College of Physicians & Surgeons, Columbia University Irving Medical Center, New York, New York, USA
- Department of Epidemiology, Columbia University Mailman School of Public Health, New York, New York, USA
| | - Evan Minty
- Department of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Wendy Chung
- Department of Pediatrics, Vagelos College of Physicians & Surgeons, Columbia University Irving Medical Center, New York, New York, USA
| | - Wei-Qi Wei
- Department of Biomedical Informatics, Vanderbilt University, Nashville, Tennessee, USA
| | - Karthik Natarajan
- Department of Biomedical Informatics, Vagelos College of Physicians & Surgeons, Columbia University Irving Medical Center, New York, New York, USA
| | - Chunhua Weng
- Department of Biomedical Informatics, Vagelos College of Physicians & Surgeons, Columbia University Irving Medical Center, New York, New York, USA
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6
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Bechler KK, Stolyar L, Steinberg E, Posada J, Minty E, Shah NH. Predicting patients who are likely to develop Lupus Nephritis of those newly diagnosed with Systemic Lupus Erythematosus. AMIA Annu Symp Proc 2023; 2022:221-230. [PMID: 37128416 PMCID: PMC10148321] [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] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Patients diagnosed with systemic lupus erythematosus (SLE) suffer from a decreased quality of life, an increased risk of medical complications, and an increased risk of death. In particular, approximately 50% of SLE patients progress to develop lupus nephritis, which oftentimes leads to life-threatening end stage renal disease (ESRD) and requires dialysis or kidney transplant1. The challenge is that lupus nephritis is diagnosed via a kidney biopsy, which is typically performed only after noticeable decreased kidney function, leaving little room for proactive or preventative measures. The ability to predict which patients are most likely to develop lupus nephritis has the potential to shift lupus nephritis disease management from reactive to proactive. We present a clinically useful prediction model to predict which patients with newly diagnosed SLE will go on to develop lupus nephritis in the next five years.
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Affiliation(s)
- Katelyn K Bechler
- Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA
| | - Liya Stolyar
- Department of Medicine, Stanford University School of Medicine, Stanford, CA
| | - Ethan Steinberg
- Department of Computer Science, Stanford University, Stanford, CA
| | - Jose Posada
- Stanford Center for Biomedical Informatics Research, Stanford University School of Medicine, Stanford CA
- Department of Systems Engineering and Computing, Universidad del Norte, Barranquilla, Colombia
| | - Evan Minty
- O'Brien Institute for Public Health, Faculty of Medicine, University of Calgary, Canada
| | - Nigam H Shah
- Department of Medicine, Stanford University School of Medicine, Stanford, CA
- Stanford Center for Biomedical Informatics Research, Stanford University School of Medicine, Stanford CA
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7
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Ostropolets A, Albogami Y, Conover M, Banda JM, Baumgartner WA, Blacketer C, Desai P, DuVall SL, Fortin S, Gilbert JP, Golozar A, Ide J, Kanter AS, Kern DM, Kim C, Lai LYH, Li C, Liu F, Lynch KE, Minty E, Neves MI, Ng DQ, Obene T, Pera V, Pratt N, Rao G, Rappoport N, Reinecke I, Saroufim P, Shoaibi A, Simon K, Suchard MA, Swerdel JN, Voss EA, Weaver J, Zhang L, Hripcsak G, Ryan PB. Reproducible variability: assessing investigator discordance across 9 research teams attempting to reproduce the same observational study. J Am Med Inform Assoc 2023; 30:859-868. [PMID: 36826399 PMCID: PMC10114120 DOI: 10.1093/jamia/ocad009] [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: 11/25/2022] [Revised: 01/04/2023] [Accepted: 01/23/2023] [Indexed: 02/25/2023] Open
Abstract
OBJECTIVE Observational studies can impact patient care but must be robust and reproducible. Nonreproducibility is primarily caused by unclear reporting of design choices and analytic procedures. This study aimed to: (1) assess how the study logic described in an observational study could be interpreted by independent researchers and (2) quantify the impact of interpretations' variability on patient characteristics. MATERIALS AND METHODS Nine teams of highly qualified researchers reproduced a cohort from a study by Albogami et al. The teams were provided the clinical codes and access to the tools to create cohort definitions such that the only variable part was their logic choices. We executed teams' cohort definitions against the database and compared the number of subjects, patient overlap, and patient characteristics. RESULTS On average, the teams' interpretations fully aligned with the master implementation in 4 out of 10 inclusion criteria with at least 4 deviations per team. Cohorts' size varied from one-third of the master cohort size to 10 times the cohort size (2159-63 619 subjects compared to 6196 subjects). Median agreement was 9.4% (interquartile range 15.3-16.2%). The teams' cohorts significantly differed from the master implementation by at least 2 baseline characteristics, and most of the teams differed by at least 5. CONCLUSIONS Independent research teams attempting to reproduce the study based on its free-text description alone produce different implementations that vary in the population size and composition. Sharing analytical code supported by a common data model and open-source tools allows reproducing a study unambiguously thereby preserving initial design choices.
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Affiliation(s)
- Anna Ostropolets
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, New York, USA
| | - Yasser Albogami
- Department of Clinical Pharmacy, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia
| | - Mitchell Conover
- Observational Health Data Analytics, Janssen Research & Development, Titusville, New Jersey, USA
| | - Juan M Banda
- Department of Computer Science, Georgia State University, Atlanta, Georgia, USA
| | - William A Baumgartner
- Division of General Internal Medicine, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Clair Blacketer
- Observational Health Data Analytics, Janssen Research & Development, Titusville, New Jersey, USA
| | - Priyamvada Desai
- Research IT, Technology and Digital Solutions, Stanford Medicine, Stanford, California, USA
| | - Scott L DuVall
- VA Salt Lake City Health Care System, Salt Lake City, Utah, USA
- Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, Utah, USA
| | - Stephen Fortin
- Observational Health Data Analytics, Janssen Research & Development, Titusville, New Jersey, USA
| | - James P Gilbert
- Observational Health Data Analytics, Janssen Research & Development, Titusville, New Jersey, USA
| | | | - Joshua Ide
- Johnson & Johnson, Titusville, New Jersey, USA
| | - Andrew S Kanter
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, New York, USA
| | - David M Kern
- Observational Health Data Analytics, Janssen Research & Development, Titusville, New Jersey, USA
| | - Chungsoo Kim
- Department of Biomedical Sciences, Ajou University Graduate School of Medicine, Suwon, South Korea
| | - Lana Y H Lai
- Department of Informatics, Imaging & Data Sciences, University of Manchester, Manchester, UK
| | - Chenyu Li
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Feifan Liu
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA
| | - Kristine E Lynch
- VA Salt Lake City Health Care System, Salt Lake City, Utah, USA
- Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, Utah, USA
| | - Evan Minty
- O’Brien Institute for Public Health, Faculty of Medicine, University of Calgary, Calgary, Canada
| | | | - Ding Quan Ng
- Department of Pharmaceutical Sciences, School of Pharmacy & Pharmaceutical Sciences, University of California, Irvine, California, USA
| | - Tontel Obene
- Mississippi Urban Research Center, Jackson State University, Jackson, Mississippi, USA
| | - Victor Pera
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Nicole Pratt
- Quality Use of Medicines and Pharmacy Research Centre, University of South Australia, Adelaide, Australia
| | - Gowtham Rao
- Observational Health Data Analytics, Janssen Research & Development, Titusville, New Jersey, USA
| | - Nadav Rappoport
- Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Israel
| | - Ines Reinecke
- Institute for Medical Informatics and Biometry, Carl Gustav Carus Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
| | - Paola Saroufim
- Cleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, Ohio, USA
| | - Azza Shoaibi
- Observational Health Data Analytics, Janssen Research & Development, Titusville, New Jersey, USA
| | - Katherine Simon
- VA Tennessee Valley Health Care System, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Marc A Suchard
- Department of Biostatistics, University of California, Los Angeles, California, USA
- Department of Human Genetics, University of California, Los Angeles, California, USA
| | - Joel N Swerdel
- Observational Health Data Analytics, Janssen Research & Development, Titusville, New Jersey, USA
| | - Erica A Voss
- Observational Health Data Analytics, Janssen Research & Development, Titusville, New Jersey, USA
| | - James Weaver
- Observational Health Data Analytics, Janssen Research & Development, Titusville, New Jersey, USA
| | - Linying Zhang
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, New York, USA
| | - George Hripcsak
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, New York, USA
- Medical Informatics Services, New York-Presbyterian Hospital, New York, New York, USA
| | - Patrick B Ryan
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, New York, USA
- Observational Health Data Analytics, Janssen Research & Development, Titusville, New Jersey, USA
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8
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Khera R, Dhingra L, Li K, Aminorroaya A, Zhou J, Arshad F, Bu F, Dorr D, Falconer T, French T, Lau W, Lu Y, Man K, Matheny ME, Minty E, Nishimura A, Ostropolets A, Seager S, Y.F. WE, Yang J, Yin C, Hripcsak G, Krumholz HM. MULTINATIONAL PATTERNS OF SECOND-LINE ANTI-HYPERGLYCEMIC DRUG INITIATION IN ESTABLISHED CARDIOVASCULAR DISEASE: A FEDERATED PHARMACOEPIDEMIOLOGIC EVALUATION IN LEGEND-T2DM. J Am Coll Cardiol 2023. [DOI: 10.1016/s0735-1097(23)02090-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/06/2023]
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9
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Morales DR, Ostropolets A, Lai L, Sena A, Duvall S, Suchard M, Verhamme K, Rjinbeek P, Posada J, Ahmed W, Alshammary T, Alghoul H, Alser O, Areia C, Blacketer C, Burn E, Casajust P, You SC, Dawoud D, Golozar A, Gong M, Jonnagaddala J, Lynch K, Matheny M, Minty E, Nyberg F, Uribe A, Recalde M, Reich C, Scheumie M, Shah K, Shah N, Schilling L, Vizcaya D, Zhang L, Hripcsak G, Ryan P, Prieto-Alhambra D, Durate-Salles T, Kostka K. Characteristics and outcomes of COVID-19 patients with and without asthma from the United States, South Korea, and Europe. J Asthma 2023; 60:76-86. [PMID: 35012410 DOI: 10.1080/02770903.2021.2025392] [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] [Indexed: 12/17/2022]
Abstract
Objective: Large international comparisons describing the clinical characteristics of patients with COVID-19 are limited. The aim of the study was to perform a large-scale descriptive characterization of COVID-19 patients with asthma.Methods: We included nine databases contributing data from January to June 2020 from the US, South Korea (KR), Spain, UK and the Netherlands. We defined two cohorts of COVID-19 patients ('diagnosed' and 'hospitalized') based on COVID-19 disease codes. We followed patients from COVID-19 index date to 30 days or death. We performed descriptive analysis and reported the frequency of characteristics and outcomes in people with asthma defined by codes and prescriptions.Results: The diagnosed and hospitalized cohorts contained 666,933 and 159,552 COVID-19 patients respectively. Exacerbation in people with asthma was recorded in 1.6-8.6% of patients at presentation. Asthma prevalence ranged from 6.2% (95% CI 5.7-6.8) to 18.5% (95% CI 18.2-18.8) in the diagnosed cohort and 5.2% (95% CI 4.0-6.8) to 20.5% (95% CI 18.6-22.6) in the hospitalized cohort. Asthma patients with COVID-19 had high prevalence of comorbidity including hypertension, heart disease, diabetes and obesity. Mortality ranged from 2.1% (95% CI 1.8-2.4) to 16.9% (95% CI 13.8-20.5) and similar or lower compared to COVID-19 patients without asthma. Acute respiratory distress syndrome occurred in 15-30% of hospitalized COVID-19 asthma patients.Conclusion: The prevalence of asthma among COVID-19 patients varies internationally. Asthma patients with COVID-19 have high comorbidity. The prevalence of asthma exacerbation at presentation was low. Whilst mortality was similar among COVID-19 patients with and without asthma, this could be confounded by differences in clinical characteristics. Further research could help identify high-risk asthma patients.[Box: see text]Supplemental data for this article is available online at https://doi.org/10.1080/02770903.2021.2025392 .
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Affiliation(s)
- Daniel R Morales
- Division of Population Health and Genomics, University of Dundee, Dundee, United Kingdom of Great Britain and Northern Ireland.,Department of Public Health, University of Southern Denmark, Odense, Denmark
| | - Anna Ostropolets
- Department of Biomedical Informatics, Columbia University, New York, NY, USA
| | - Lana Lai
- The University of Manchester, University of Manchester, Manchester, United Kingdom of Great Britain and Northern Ireland
| | - Anthony Sena
- Janssen Research and Development LLC, Raritan, NJ, USA
| | - Scott Duvall
- University of Utah Health, Epidemiology, Salt Lake City, UT, USA
| | | | - Katia Verhamme
- Erasmus MC, Medical Informatics, Erasmus MC, Dr Molewaterplein, Rotterdam, CA, The Netherlands
| | - Peter Rjinbeek
- Erasmus MC, Medical Informatics, Erasmus MC, Dr Molewaterplein, Rotterdam, CA, The Netherlands
| | - Joe Posada
- Stanford University, Medicine, Stanford, CA, USA
| | - Waheed Ahmed
- Department of Orthopedics, University of Oxford, Oxford, United Kingdom of Great Britain and Northern Ireland
| | | | - Heba Alghoul
- Islamic University of Gaza, Medicine, Gaza, State of Palestine
| | - Osaid Alser
- Department of Orthopedics, University of Oxford, Oxford, United Kingdom of Great Britain and Northern Ireland
| | - Carlos Areia
- Department of Orthopedics, University of Oxford, Oxford, United Kingdom of Great Britain and Northern Ireland
| | - Clair Blacketer
- Erasmus MC, Medical Informatics, Erasmus MC, Dr Molewaterplein, Rotterdam, CA, The Netherlands
| | - Edward Burn
- Department of Orthopedics, University of Oxford, Oxford, United Kingdom of Great Britain and Northern Ireland
| | - Paula Casajust
- Trial Form Support, Real World Evidence, Barcelona, Spain
| | - Seng Chan You
- Ajou University, Medicine, Suwon, The Republic of Korea
| | - Dalia Dawoud
- Stanford University, Medicine, Stanford, CA, USA
| | - Asieh Golozar
- Johns Hopkins University, Epidemiology, Baltimore, MD, USA
| | | | | | - Kristine Lynch
- University of Utah Health, Epidemiology, Salt Lake City, UT, USA
| | - Michael Matheny
- University of Utah Health, Epidemiology, Salt Lake City, UT, USA
| | - Evan Minty
- University of Calgary, Public Health, Calgary, Alberta, Canada
| | - Fredrik Nyberg
- University of Gothenburg, Public health, Goteborg, Sweden
| | - Albert Uribe
- Department of Orthopedics, University of Oxford, Oxford, United Kingdom of Great Britain and Northern Ireland
| | | | | | | | - Karishma Shah
- Department of Orthopedics, University of Oxford, Oxford, United Kingdom of Great Britain and Northern Ireland
| | - Nigam Shah
- Stanford University, Medicine, Stanford, CA, USA
| | - Lisa Schilling
- University of Colorado, School of Medicine, Denver, CO, USA
| | | | - Lin Zhang
- Chinese Academy of Medical Sciences and Peking Union Medical College, Public health, Beijing, China
| | - George Hripcsak
- Department of Biomedical Informatics, Columbia University, New York, NY, USA
| | - Patrick Ryan
- Janssen Research and Development LLC, Raritan, NJ, USA
| | - Daniel Prieto-Alhambra
- Department of Orthopedics, University of Oxford, Oxford, United Kingdom of Great Britain and Northern Ireland
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Schuemie MJ, Arshad F, Pratt N, Nyberg F, Alshammari TM, Hripcsak G, Ryan P, Prieto-Alhambra D, Lai LYH, Li X, Fortin S, Minty E, Suchard MA. Corrigendum: Vaccine safety surveillance using routinely collected healthcare data-An empirical evaluation of epidemiological designs. Front Pharmacol 2022; 13:1088973. [PMID: 36506524 PMCID: PMC9731373 DOI: 10.3389/fphar.2022.1088973] [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] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Accepted: 11/04/2022] [Indexed: 11/25/2022] Open
Abstract
[This corrects the article DOI: 10.3389/fphar.2022.893484.].
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Affiliation(s)
- Martijn J. Schuemie
- Observational Health Data Sciences and Informatics, New York, NY, United States,Observational Health Data Analytics, Janssen R&D, Titusville, NJ, United States,Department of Biostatistics, University of California, Los Angeles, Los Angeles, CA, United States,*Correspondence: Martijn J. Schuemie,
| | - Faaizah Arshad
- Observational Health Data Sciences and Informatics, New York, NY, United States,Department of Biostatistics, University of California, Los Angeles, Los Angeles, CA, United States
| | - Nicole Pratt
- Quality Use of Medicines and Pharmacy Research Centre, Clinical and Health Sciences, University of South Australia, Adelaide, SA, Australia
| | - Fredrik Nyberg
- School of Public Health and Community Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | | | - George Hripcsak
- Observational Health Data Sciences and Informatics, New York, NY, United States,Department of Biomedical Informatics, Columbia University, New York, NY, United States
| | - Patrick Ryan
- Observational Health Data Sciences and Informatics, New York, NY, United States,Observational Health Data Analytics, Janssen R&D, Titusville, NJ, United States,Department of Biomedical Informatics, Columbia University, New York, NY, United States
| | - Daniel Prieto-Alhambra
- Centre for Statistics in Medicine, NDORMS, University of Oxford, Oxford, United Kingdom,Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, Netherlands
| | - Lana Y. H. Lai
- O’Brien Institute for Public Health, Faculty of Medicine, University of Calgary, Calgary, AB, Canada
| | - Xintong Li
- Division of Medical Sciences, University of Manchester, Manchester, United Kingdom
| | - Stephen Fortin
- Observational Health Data Analytics, Janssen R&D, Titusville, NJ, United States
| | - Evan Minty
- O’Brien Institute for Public Health, Faculty of Medicine, University of Calgary, Calgary, AB, Canada
| | - Marc A. Suchard
- Observational Health Data Sciences and Informatics, New York, NY, United States,Department of Biostatistics, University of California, Los Angeles, Los Angeles, CA, United States,Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, United States
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11
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Schuemie MJ, Arshad F, Pratt N, Nyberg F, Alshammari TM, Hripcsak G, Ryan P, Prieto-Alhambra D, Lai LYH, Li X, Fortin S, Minty E, Suchard MA. Vaccine Safety Surveillance Using Routinely Collected Healthcare Data-An Empirical Evaluation of Epidemiological Designs. Front Pharmacol 2022; 13:893484. [PMID: 35873596 PMCID: PMC9299244 DOI: 10.3389/fphar.2022.893484] [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] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Accepted: 06/13/2022] [Indexed: 12/13/2022] Open
Abstract
Background: Routinely collected healthcare data such as administrative claims and electronic health records (EHR) can complement clinical trials and spontaneous reports to detect previously unknown risks of vaccines, but uncertainty remains about the behavior of alternative epidemiologic designs to detect and declare a true risk early. Methods: Using three claims and one EHR database, we evaluate several variants of the case-control, comparative cohort, historical comparator, and self-controlled designs against historical vaccinations using real negative control outcomes (outcomes with no evidence to suggest that they could be caused by the vaccines) and simulated positive control outcomes. Results: Most methods show large type 1 error, often identifying false positive signals. The cohort method appears either positively or negatively biased, depending on the choice of comparator index date. Empirical calibration using effect-size estimates for negative control outcomes can bring type 1 error closer to nominal, often at the cost of increasing type 2 error. After calibration, the self-controlled case series (SCCS) design most rapidly detects small true effect sizes, while the historical comparator performs well for strong effects. Conclusion: When applying any method for vaccine safety surveillance we recommend considering the potential for systematic error, especially due to confounding, which for many designs appears to be substantial. Adjusting for age and sex alone is likely not sufficient to address differences between vaccinated and unvaccinated, and for the cohort method the choice of index date is important for the comparability of the groups. Analysis of negative control outcomes allows both quantification of the systematic error and, if desired, subsequent empirical calibration to restore type 1 error to its nominal value. In order to detect weaker signals, one may have to accept a higher type 1 error.
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Affiliation(s)
- Martijn J. Schuemie
- Observational Health Data Sciences and Informatics, New York, NY, United States,Observational Health Data Analytics, Janssen R&D, Titusville, NJ, United States,Department of Biostatistics, University of California, Los Angeles, Los Angeles, CA, United States,*Correspondence: Martijn J. Schuemie,
| | - Faaizah Arshad
- Observational Health Data Sciences and Informatics, New York, NY, United States,Department of Biostatistics, University of California, Los Angeles, Los Angeles, CA, United States
| | - Nicole Pratt
- Quality Use of Medicines and Pharmacy Research Centre, Clinical and Health Sciences, University of South Australia, Adelaide, SA, Australia
| | - Fredrik Nyberg
- School of Public Health and Community Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Thamir M Alshammari
- College of Pharmacy, Prince Sattam Bin Abdulaziz University, Riyadh, Saudi Arabia
| | - George Hripcsak
- Observational Health Data Sciences and Informatics, New York, NY, United States,Department of Biomedical Informatics, Columbia University, New York, NY, United States
| | - Patrick Ryan
- Observational Health Data Sciences and Informatics, New York, NY, United States,Observational Health Data Analytics, Janssen R&D, Titusville, NJ, United States,Department of Biomedical Informatics, Columbia University, New York, NY, United States
| | - Daniel Prieto-Alhambra
- Centre for Statistics in Medicine, NDORMS, University of Oxford, Oxford, United Kingdom,Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, Netherlands
| | - Lana Y. H. Lai
- O’Brien Institute for Public Health, Faculty of Medicine, University of Calgary, Calgary, AB, Canada
| | - Xintong Li
- Division of Medical Sciences, University of Manchester, Manchester, United Kingdom
| | - Stephen Fortin
- Observational Health Data Analytics, Janssen R&D, Titusville, NJ, United States
| | - Evan Minty
- O’Brien Institute for Public Health, Faculty of Medicine, University of Calgary, Calgary, AB, Canada
| | - Marc A. Suchard
- Observational Health Data Sciences and Informatics, New York, NY, United States,Department of Biostatistics, University of California, Los Angeles, Los Angeles, CA, United States,Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, United States
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12
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Kostka K, Duarte-Salles T, Prats-Uribe A, Sena AG, Pistillo A, Khalid S, Lai LYH, Golozar A, Alshammari TM, Dawoud DM, Nyberg F, Wilcox AB, Andryc A, Williams A, Ostropolets A, Areia C, Jung CY, Harle CA, Reich CG, Blacketer C, Morales DR, Dorr DA, Burn E, Roel E, Tan EH, Minty E, DeFalco F, de Maeztu G, Lipori G, Alghoul H, Zhu H, Thomas JA, Bian J, Park J, Martínez Roldán J, Posada JD, Banda JM, Horcajada JP, Kohler J, Shah K, Natarajan K, Lynch KE, Liu L, Schilling LM, Recalde M, Spotnitz M, Gong M, Matheny ME, Valveny N, Weiskopf NG, Shah N, Alser O, Casajust P, Park RW, Schuff R, Seager S, DuVall SL, You SC, Song S, Fernández-Bertolín S, Fortin S, Magoc T, Falconer T, Subbian V, Huser V, Ahmed WUR, Carter W, Guan Y, Galvan Y, He X, Rijnbeek PR, Hripcsak G, Ryan PB, Suchard MA, Prieto-Alhambra D. Unraveling COVID-19: A Large-Scale Characterization of 4.5 Million COVID-19 Cases Using CHARYBDIS. Clin Epidemiol 2022; 14:369-384. [PMID: 35345821 PMCID: PMC8957305 DOI: 10.2147/clep.s323292] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Accepted: 01/27/2022] [Indexed: 01/20/2023] Open
Abstract
Purpose Routinely collected real world data (RWD) have great utility in aiding the novel coronavirus disease (COVID-19) pandemic response. Here we present the international Observational Health Data Sciences and Informatics (OHDSI) Characterizing Health Associated Risks and Your Baseline Disease In SARS-COV-2 (CHARYBDIS) framework for standardisation and analysis of COVID-19 RWD. Patients and Methods We conducted a descriptive retrospective database study using a federated network of data partners in the United States, Europe (the Netherlands, Spain, the UK, Germany, France and Italy) and Asia (South Korea and China). The study protocol and analytical package were released on 11th June 2020 and are iteratively updated via GitHub. We identified three non-mutually exclusive cohorts of 4,537,153 individuals with a clinical COVID-19 diagnosis or positive test, 886,193 hospitalized with COVID-19, and 113,627 hospitalized with COVID-19 requiring intensive services. Results We aggregated over 22,000 unique characteristics describing patients with COVID-19. All comorbidities, symptoms, medications, and outcomes are described by cohort in aggregate counts and are readily available online. Globally, we observed similarities in the USA and Europe: more women diagnosed than men but more men hospitalized than women, most diagnosed cases between 25 and 60 years of age versus most hospitalized cases between 60 and 80 years of age. South Korea differed with more women than men hospitalized. Common comorbidities included type 2 diabetes, hypertension, chronic kidney disease and heart disease. Common presenting symptoms were dyspnea, cough and fever. Symptom data availability was more common in hospitalized cohorts than diagnosed. Conclusion We constructed a global, multi-centre view to describe trends in COVID-19 progression, management and evolution over time. By characterising baseline variability in patients and geography, our work provides critical context that may otherwise be misconstrued as data quality issues. This is important as we perform studies on adverse events of special interest in COVID-19 vaccine surveillance.
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Affiliation(s)
- Kristin Kostka
- IQVIA, Cambridge, MA, USA
- OHDSI Center at The Roux Institute, Northeastern University, Portland, ME, USA
| | - Talita Duarte-Salles
- Fundació Institut Universitari per a la recerca a l’Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | - Albert Prats-Uribe
- Centre for Statistics in Medicine, NDORMS, University of Oxford, Oxford, UK
| | - Anthony G Sena
- Janssen Research & Development, Titusville, NJ, USA
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Andrea Pistillo
- Fundació Institut Universitari per a la recerca a l’Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | - Sara Khalid
- Centre for Statistics in Medicine, NDORMS, University of Oxford, Oxford, UK
| | - Lana Y H Lai
- School of Medical Sciences, University of Manchester, Manchester, UK
| | - Asieh Golozar
- Regeneron Pharmaceuticals, Tarrytown, NY, USA
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | | | - Dalia M Dawoud
- National Institute for Health and Care Excellence, London, UK
| | - Fredrik Nyberg
- School of Public Health and Community Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Adam B Wilcox
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA
- Unviersity of Washington Medicine, Seattle, WA, USA
| | - Alan Andryc
- Janssen Research & Development, Titusville, NJ, USA
| | - Andrew Williams
- Tufts Institute for Clinical Research and Health Policy Studies, Boston, MA, USA
| | - Anna Ostropolets
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA
| | - Carlos Areia
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Chi Young Jung
- Division of Respiratory and Critical Care Medicine, Department of Internal Medicine, Daegu Catholic University Medical Center, Daegu, South Korea
| | | | - Christian G Reich
- IQVIA, Cambridge, MA, USA
- OHDSI Center at The Roux Institute, Northeastern University, Portland, ME, USA
| | - Clair Blacketer
- Janssen Research & Development, Titusville, NJ, USA
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Daniel R Morales
- Division of Population Health and Genomics, University of Dundee, Dundee, UK
| | - David A Dorr
- Department of Medical Informatics & Clinical Epidemiology, Oregon Health & Science University, Portland, OR, USA
| | - Edward Burn
- Fundació Institut Universitari per a la recerca a l’Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
- Centre for Statistics in Medicine, NDORMS, University of Oxford, Oxford, UK
| | - Elena Roel
- Fundació Institut Universitari per a la recerca a l’Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
- Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Eng Hooi Tan
- Centre for Statistics in Medicine, NDORMS, University of Oxford, Oxford, UK
| | - Evan Minty
- O’Brien Institute for Public Health, Faculty of Medicine, University of Calgary, Calgary, Canada
| | | | | | - Gigi Lipori
- University of Florida Health, Gainesville, FL, USA
| | - Hiba Alghoul
- Faculty of Medicine, Islamic University of Gaza, Gaza, Palestine
| | - Hong Zhu
- Nanfang Hospital, Southern Medical University, Guangzhou, People’s Republic of China
| | - Jason A Thomas
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA
| | - Jiang Bian
- University of Florida Health, Gainesville, FL, USA
| | - Jimyung Park
- Department of Biomedical Sciences, Ajou University Graduate School of Medicine, Suwon, South Korea
| | - Jordi Martínez Roldán
- Director of Innovation and Digital Transformation, Hospital del Mar, Barcelona, Spain
| | - Jose D Posada
- Department of Medicine, School of Medicine, Stanford University, Redwood City, CA, USA
| | - Juan M Banda
- Georgia State University, Department of Computer Science, Atlanta, GA, USA
| | - Juan P Horcajada
- Department of Infectious Diseases, Hospital del Mar, Institut Hospital del Mar d’Investigació Mèdica (IMIM), Universitat Autònoma de Barcelona, Universitat Pompeu Fabra, Barcelona, Spain
| | - Julianna Kohler
- United States Agency for International Development, Washington, DC, USA
| | - Karishma Shah
- Botnar Research Centre, NDORMS, University of Oxford, Oxford, UK
| | - Karthik Natarajan
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA
- New York-Presbyterian Hospital, New York, NY, 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, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Li Liu
- Biomedical Big Data Center, Nanfang Hospital, Southern Medical University, Guangzhou, People’s Republic of China
| | - Lisa M Schilling
- Data Science to Patient Value Program, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Martina Recalde
- Fundació Institut Universitari per a la recerca a l’Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
- Universitat Autònoma de Barcelona, Barcelona, Spain
| | | | - Mengchun Gong
- Institute of Health Management, Southern Medical University, Guangzhou, People’s Republic of China
| | - Michael E Matheny
- Tennessee Valley Healthcare System, Veterans Affairs Medical Center, Nashville, TN, USA
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | | | - Nicole G Weiskopf
- Department of Medical Informatics & Clinical Epidemiology, Oregon Health & Science University, Portland, OR, USA
| | - Nigam Shah
- Department of Medicine, School of Medicine, Stanford University, Redwood City, CA, USA
| | - Osaid Alser
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | | | - Rae Woong Park
- Department of Biomedical Sciences, Ajou University Graduate School of Medicine, Suwon, South Korea
- Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, South Korea
| | - Robert Schuff
- Department of Medical Informatics & Clinical Epidemiology, Oregon Health & Science University, Portland, OR, 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, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Seng Chan You
- Department of Preventive Medicine, Yonsei University College of Medicine, Seoul, South Korea
| | - Seokyoung Song
- Department of Anesthesiology and Pain Medicine, Catholic University of Daegu, School of Medicine, Daegu, South Korea
| | - Sergio Fernández-Bertolín
- Fundació Institut Universitari per a la recerca a l’Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | | | - Tanja Magoc
- University of Florida Health, Gainesville, FL, USA
| | - Thomas Falconer
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA
| | - Vignesh Subbian
- College of Engineering, The University of Arizona, Tucson, AZ, USA
| | - Vojtech Huser
- National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | - Waheed-Ul-Rahman Ahmed
- Botnar Research Centre, NDORMS, University of Oxford, Oxford, UK
- College of Medicine and Health, University of Exeter, St Luke’s Campus, Exeter, UK
| | - William Carter
- Data Science to Patient Value Program, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Yin Guan
- DHC Technologies Co. Ltd., Beijing, People’s Republic of China
| | | | - Xing He
- University of Florida Health, Gainesville, FL, USA
| | - Peter R Rijnbeek
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - George Hripcsak
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA
- New York-Presbyterian Hospital, New York, NY, USA
| | - Patrick B Ryan
- Janssen Research & Development, Titusville, NJ, USA
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA
| | - Marc A Suchard
- Departments of Biostatistics, Computational Medicine, and Human Genetics, University of California, Los Angeles, CA, USA
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Woiceshyn J, Huq JL, Kannappan S, Fabreau G, Minty E, Pendharkar S, Bharwani A. We need to work differently in a crisis: peer-professional leadership to redesign physicians’ work. leader 2021; 6:98-103. [DOI: 10.1136/leader-2021-000490] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Accepted: 07/16/2021] [Indexed: 11/04/2022]
Abstract
BackgroundUnderstanding physician leadership is critical during pandemics and other health crises when formal organisational leaders may be unable to respond expeditiously. This study examined how physician leaders managed to quickly design a new model for acute-care physicians’ work, adopted across four large hospitals in a public health authority in Canada during the COVID-19 pandemic.MethodsThe research employed a qualitative case study methodology, with inductive analysis of interview transcripts and documents. Shortly after a physician work model redesign, we interviewed key informants: the physician leaders and others who participated in or supported the model’s development. Participants were chosen based on their leadership role and through snowballing. All those who were approached agreed to participate.ResultsA process model describes leadership actions during four phases of work model development (priming, early planning, readying for operations and transition). These actions were: (1) recognising the threat, (2) committing to action, (3) forming and organising, (4) building and relying on relationships, (5) developing supporting processes and (6) designing functions and structure. We offer three additional contributions to knowledge about leadership in a time of crisis: (1) leveraging peer-professional leadership to initiate, formalise and organise change processes, (2) designing a new work model on existing and emerging evidence and (3) building and relying on relationships to unify various actors.ConclusionsThe model of peer-professional leadership can deepen understanding of how to lead professionals. Our findings could assist peer-professional and organisational leaders to encourage quick redesign of professionals’ work in response to new phases of the COVID-19 pandemic or other crises.
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Pendharkar SR, Minty E, Shukalek CB, Kerr B, MacMullan P, Lail P, Cheema K, Alimohamed N, Allen T, Vlasschaert MEO, Kachra R, Ma IWY, Johannson KA, Gibson PS, Elliott MJ, Papini A, Smith S, Lemaire J, Ruzycki SM, Hunter A, Desjardins-Kallar W, Schaefer JP, Zarnke KB, Bharwani A, Fabreau GE. Description of a Multi-faceted COVID-19 Pandemic Physician Workforce Plan at a Multi-site Academic Health System. J Gen Intern Med 2021; 36:1310-1318. [PMID: 33564947 PMCID: PMC7872510 DOI: 10.1007/s11606-020-06543-1] [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] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/20/2020] [Accepted: 12/21/2020] [Indexed: 11/28/2022]
Abstract
BACKGROUND The evolving COVID-19 pandemic has and continues to present a threat to health system capacity. Rapidly expanding an existing acute care physician workforce is critical to pandemic response planning in large urban academic health systems. INTERVENTION The Medical Emergency-Pandemic Operations Command (MEOC)-a multi-specialty team of physicians, operational leaders, and support staff within an academic Department of Medicine in Calgary, Canada-partnered with its provincial health system to rapidly develop a comprehensive, scalable pandemic physician workforce plan for non-ventilated inpatients with COVID-19 across multiple hospitals. The MEOC Pandemic Plan comprised seven components, each with unique structure and processes. METHODS In this manuscript, we describe MEOC's Pandemic Plan that was designed and implemented from March to May 2020 and re-escalated in October 2020. We report on the plan's structure and process, early implementation outcomes, and unforeseen challenges. Data sources included MEOC documents, health system, public health, and physician engagement implementation data. KEY RESULTS From March 5 to October 26, 2020, 427 patients were admitted to COVID-19 units in Calgary hospitals. In the initial implementation period (March-May 2020), MEOC communications reached over 2500 physicians, leading to 1446 physicians volunteering to provide care on COVID-19 units. Of these, 234 physicians signed up for hospital shifts, and 227 physicians received in-person personal protective equipment simulation training. Ninety-three physicians were deployed on COVID-19 units at four large acute care hospitals. The resurgence of cases in September 2020 has prompted re-escalation including re-activation of COVID-19 units. CONCLUSIONS MEOC leveraged an academic health system partnership to rapidly design, implement, and refine a comprehensive, scalable COVID-19 acute care physician workforce plan whose components are readily applicable across jurisdictions or healthcare crises. This description may guide other institutions responding to COVID-19 and future health emergencies.
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Affiliation(s)
- Sachin R Pendharkar
- Department of Medicine, Cumming School of Medicine, University of Calgary, Foothills Medical Centre - North Tower, Calgary, AB, Canada.
- Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.
- O'Brien Institute for Public Health, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.
| | - Evan Minty
- Department of Medicine, Cumming School of Medicine, University of Calgary, Foothills Medical Centre - North Tower, Calgary, AB, Canada
- O'Brien Institute for Public Health, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Caley B Shukalek
- Department of Medicine, Cumming School of Medicine, University of Calgary, Foothills Medical Centre - North Tower, Calgary, AB, Canada
- O'Brien Institute for Public Health, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Brendan Kerr
- Department of Medicine, Cumming School of Medicine, University of Calgary, Foothills Medical Centre - North Tower, Calgary, AB, Canada
| | - Paul MacMullan
- Department of Medicine, Cumming School of Medicine, University of Calgary, Foothills Medical Centre - North Tower, Calgary, AB, Canada
| | - Parabhdeep Lail
- Department of Medicine, Cumming School of Medicine, University of Calgary, Foothills Medical Centre - North Tower, Calgary, AB, Canada
| | - Kim Cheema
- Department of Medicine, Cumming School of Medicine, University of Calgary, Foothills Medical Centre - North Tower, Calgary, AB, Canada
| | - Nimira Alimohamed
- Department of Medicine, Cumming School of Medicine, University of Calgary, Foothills Medical Centre - North Tower, Calgary, AB, Canada
| | - Thomas Allen
- Cumming School of Medicine, University of Calgary, Health Sciences Centre, Calgary, AB, Canada
| | - Meghan E O Vlasschaert
- Department of Medicine, Cumming School of Medicine, University of Calgary, Foothills Medical Centre - North Tower, Calgary, AB, Canada
| | - Rahim Kachra
- Department of Medicine, Cumming School of Medicine, University of Calgary, Foothills Medical Centre - North Tower, Calgary, AB, Canada
- Cumming School of Medicine, University of Calgary, Health Sciences Centre, Calgary, AB, Canada
| | - Irene W Y Ma
- Department of Medicine, Cumming School of Medicine, University of Calgary, Foothills Medical Centre - North Tower, Calgary, AB, Canada
- Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Kerri A Johannson
- Department of Medicine, Cumming School of Medicine, University of Calgary, Foothills Medical Centre - North Tower, Calgary, AB, Canada
- Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- O'Brien Institute for Public Health, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Paul S Gibson
- Department of Medicine, Cumming School of Medicine, University of Calgary, Foothills Medical Centre - North Tower, Calgary, AB, Canada
| | - Meghan J Elliott
- Department of Medicine, Cumming School of Medicine, University of Calgary, Foothills Medical Centre - North Tower, Calgary, AB, Canada
- O'Brien Institute for Public Health, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Adam Papini
- Department of Medicine, Cumming School of Medicine, University of Calgary, Foothills Medical Centre - North Tower, Calgary, AB, Canada
| | - Stephanie Smith
- Cumming School of Medicine, University of Calgary, Health Sciences Centre, Calgary, AB, Canada
| | - Jane Lemaire
- Department of Medicine, Cumming School of Medicine, University of Calgary, Foothills Medical Centre - North Tower, Calgary, AB, Canada
| | - Shannon M Ruzycki
- Department of Medicine, Cumming School of Medicine, University of Calgary, Foothills Medical Centre - North Tower, Calgary, AB, Canada
- Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Angela Hunter
- Department of Medicine, Cumming School of Medicine, University of Calgary, Foothills Medical Centre - North Tower, Calgary, AB, Canada
| | - Wendy Desjardins-Kallar
- Department of Medicine, Cumming School of Medicine, University of Calgary, Foothills Medical Centre - North Tower, Calgary, AB, Canada
| | - Jeffrey P Schaefer
- Department of Medicine, Cumming School of Medicine, University of Calgary, Foothills Medical Centre - North Tower, Calgary, AB, Canada
| | - Kelly B Zarnke
- Department of Medicine, Cumming School of Medicine, University of Calgary, Foothills Medical Centre - North Tower, Calgary, AB, Canada
- O'Brien Institute for Public Health, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Aleem Bharwani
- Department of Medicine, Cumming School of Medicine, University of Calgary, Foothills Medical Centre - North Tower, Calgary, AB, Canada
- O'Brien Institute for Public Health, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Gabriel E Fabreau
- Department of Medicine, Cumming School of Medicine, University of Calgary, Foothills Medical Centre - North Tower, Calgary, AB, Canada
- Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- O'Brien Institute for Public Health, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
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Prieto-Alhambra D, Kostka K, Duarte-Salles T, Prats-Uribe A, Sena A, Pistillo A, Khalid S, Lai L, Golozar A, Alshammari TM, Dawoud D, Nyberg F, Wilcox A, Andryc A, Williams A, Ostropolets A, Areia C, Jung CY, Harle C, Reich C, Blacketer C, Morales D, Dorr DA, Burn E, Roel E, Tan EH, Minty E, DeFalco F, de Maeztu G, Lipori G, Alghoul H, Zhu H, Thomas J, Bian J, Park J, Roldán JM, Posada J, Banda JM, Horcajada JP, Kohler J, Shah K, Natarajan K, Lynch K, Liu L, Schilling L, Recalde M, Spotnitz M, Gong M, Matheny M, Valveny N, Weiskopf N, Shah N, Alser O, Casajust P, Park RW, Schuff R, Seager S, DuVall S, You SC, Song S, Fernández-Bertolín S, Fortin S, Magoc T, Falconer T, Subbian V, Huser V, Ahmed WUR, Carter W, Guan Y, Galvan Y, He X, Rijnbeek P, Hripcsak G, Ryan P, Suchard M. Unraveling COVID-19: a large-scale characterization of 4.5 million COVID-19 cases using CHARYBDIS. Res Sq 2021:rs.3.rs-279400. [PMID: 33688639 PMCID: PMC7941629 DOI: 10.21203/rs.3.rs-279400/v1] [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] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
Background: Routinely collected real world data (RWD) have great utility in aiding the novel coronavirus disease (COVID-19) pandemic response [1,2]. Here we present the international Observational Health Data Sciences and Informatics (OHDSI) [3] Characterizing Health Associated Risks, and Your Baseline Disease In SARS-COV-2 (CHARYBDIS) framework for standardisation and analysis of COVID-19 RWD. Methods: We conducted a descriptive cohort study using a federated network of data partners in the United States, Europe (the Netherlands, Spain, the UK, Germany, France and Italy) and Asia (South Korea and China). The study protocol and analytical package were released on 11 th June 2020 and are iteratively updated via GitHub [4]. Findings: We identified three non-mutually exclusive cohorts of 4,537,153 individuals with a clinical COVID-19 diagnosis or positive test, 886,193 hospitalized with COVID-19 , and 113,627 hospitalized with COVID-19 requiring intensive services . All comorbidities, symptoms, medications, and outcomes are described by cohort in aggregate counts, and are available in an interactive website: https://data.ohdsi.org/Covid19CharacterizationCharybdis/. Interpretation: CHARYBDIS findings provide benchmarks that contribute to our understanding of COVID-19 progression, management and evolution over time. This can enable timely assessment of real-world outcomes of preventative and therapeutic options as they are introduced in clinical practice.
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Affiliation(s)
- Daniel Prieto-Alhambra
- Centre for Statistics in Medicine (CSM), Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences (NDROMS), University of Oxford, UK
| | | | - Talita Duarte-Salles
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | | | - Anthony Sena
- Janssen R&D, Titusville NJ, USA, 2) Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Andrea Pistillo
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | - Sara Khalid
- Centre for Statistics in Medicine, NDORMS, University of Oxford, UK
| | - Lana Lai
- Division of Cancer Sciences, School of Medical Sciences, University of Manchester, UK
| | - Asieh Golozar
- Regeneron Pharmaceuticals, NY USA, Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, MD USA
| | - Thamir M Alshammari
- Medication Safety Research Chair, King Saud University, Riyadh, Saudi Arabia
| | - Dalia Dawoud
- National Institute for Health and Care Excellence, London, UK
| | - Fredrik Nyberg
- School of Public Health and Community Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Adam Wilcox
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA, 2) UW Medicine, Seattle, WA, USA
| | | | - Andrew Williams
- Tufts Institute for Clinical Research and Health Policy Studies, US
| | - Anna Ostropolets
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Carlos Areia
- Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | - Chi Young Jung
- Division of Respiratory and Critical Care Medicine, Department of Internal Medicine, Daegu Catholic University Medical Center, Daegu, Korea
| | | | | | - Clair Blacketer
- Janssen R&D, Titusville NJ, USA, 2) Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Daniel Morales
- Division of Population Health and Genomics, University of Dundee, UK
| | - David A Dorr
- Department of Medical Informatics & Clinical Epidemiology, Oregon Health & Science University, Portland, OR, USA
| | - Edward Burn
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | | | - Eng Hooi Tan
- Centre for Statistics in Medicine, NDORMS, University of Oxford, UK
| | - Evan Minty
- O'Brien Institute for Public Health, Faculty of Medicine, University of Calgary, Canada
| | | | | | | | - Heba Alghoul
- Faculty of Medicine, Islamic University of Gaza, Palestine
| | - Hong Zhu
- Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Jason Thomas
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA
| | | | - Jimyung Park
- Department of Biomedical Sciences, Ajou University Graduate School of Medicine, Suwon, Korea
| | - Jordi Martínez Roldán
- Director of Innovation and Digital Transformation, Hospital del Mar, Barcelona, Spain
| | - Jose Posada
- Stanford University School of Medicine, Stanford, California, USA
| | - Juan M Banda
- Georgia State University, Department of Computer Science, Atlanta, GA, USA
| | - Juan P Horcajada
- Department of Infectious Diseases, Hospital del Mar, Institut Hospital del Mar d'Investigació Mèdica (IMIM), Universitat Autònoma de Barcelona. Universitat Pompeu Fabra, Barcelo
| | - Julianna Kohler
- United States Agency for International Development, Washington, DC, USA
| | - Karishma Shah
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Karthik Natarajan
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY 10032, USA, 2) New York-Presbyterian Hospital, 622 W 168 St, PH20 New York, NY 10032 USA
| | - Kristine Lynch
- VINCI, VA Salt Lake City Health Care System, Salt Lake City, VA, & Division of Epidemiology, University of Utah, Salt Lake City, UT
| | - Li Liu
- Biomedical Big Data Center, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Lisa Schilling
- Data Science to Patient Value Program, University of Colorado Anschutz Medical Campus
| | - Martina Recalde
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | - Matthew Spotnitz
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY 10032, USA
| | | | - Michael Matheny
- VINCI, Tennessee Valley Healthcare System VA, Nashville, TN & Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN
| | | | - Nicole Weiskopf
- Department of Medical Informatics & Clinical Epidemiology, Oregon Health & Science University, Portland, OR, USA
| | | | - Osaid Alser
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | | | - Rae Woong Park
- Department of Biomedical Sciences, Ajou University Graduate School of Medicine, Suwon, Korea
| | - Robert Schuff
- Knight Cancer Institute, Oregon Health & Science University
| | | | - Scott DuVall
- VA Informatics and Computing Infrastructure, VA Salt Lake City Health Care System, Salt Lake City, UT, USA
| | - Seng Chan You
- Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, South Korea
| | - Seokyoung Song
- Department of Anesthesiology and Pain Medicine, Catholic University of Daegu, School of Medicine, Daegu, Korea
| | - Sergio Fernández-Bertolín
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | - Stephen Fortin
- Observational Health Data Analytics, Janssen Research and Development, Raritan, NJ, USA
| | | | - Thomas Falconer
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Vignesh Subbian
- College of Engineering, The University of Arizona, Tucson, Arizona, USA
| | - Vojtech Huser
- National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | - Waheed-Ul-Rahman Ahmed
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK, 2) College of Medicine and Health, University of Exeter, St Luke's Campus, E
| | - William Carter
- Data Science to Patient Value Program, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Yin Guan
- DHC Technologies Co. Ltd, Beijing, China
| | | | - Xing He
- University of Florida Health
| | - Peter Rijnbeek
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - George Hripcsak
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY 10032, USA, 2) New York-Presbyterian Hospital, 622 W 168 St, PH20 New York, NY 10032 USA
| | | | - Marc Suchard
- Department of Biostatistics, UCLA Fielding School of Public Health, University of California, Los Angeles
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16
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Ostropolets A, Reich C, Ryan P, Weng C, Molinaro A, DeFalco F, Jonnagaddala J, Liaw ST, Jeon H, Park RW, Spotnitz ME, Natarajan K, Argyriou G, Kostka K, Miller R, Williams A, Minty E, Posada J, Hripcsak G. Characterizing database granularity using SNOMED-CT hierarchy. AMIA Annu Symp Proc 2021; 2020:983-992. [PMID: 33936474 PMCID: PMC8075504] [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] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Multi-center observational studies require recognition and reconciliation of differences in patient representations arising from underlying populations, disparate coding practices and specifics of data capture. This leads to different granularity or detail of concepts representing the clinical facts. For researchers studying certain populations of interest, it is important to ensure that concepts at the right level are used for the definition of these populations. We studied the granularity of concepts within 22 data sources in the OHDSI network and calculated a composite granularity score for each dataset. Three alternative SNOMED-based approaches for such score showed consistency in classifying data sources into three levels of granularity (low, moderate and high), which correlated with the provenance of data and country of origin. However, they performed unsatisfactorily in ordering data sources within these groups and showed inconsistency for small data sources. Further studies on examining approaches to data source granularity are needed.
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Affiliation(s)
| | | | - Patrick Ryan
- Columbia University, New York, NY, USA
- Janssen Epidemiology Analytics, Janssen Research & Development, Titusville, NJ, USA
| | | | - Anthony Molinaro
- Janssen Epidemiology Analytics, Janssen Research & Development, Titusville, NJ, USA
| | - Frank DeFalco
- Janssen Epidemiology Analytics, Janssen Research & Development, Titusville, NJ, USA
| | | | | | - Hokyun Jeon
- Ajou University Graduate School of Medicine, Suwon, Gyeonggi-do, Republic of Korea
| | - Rae Woong Park
- Ajou University Graduate School of Medicine, Suwon, Gyeonggi-do, Republic of Korea
| | | | | | | | | | - Robert Miller
- Tufts Medical Center, Institute for Clinical Research and Health Policy Studies, Boston, MA, USA
| | - Andrew Williams
- Tufts Medical Center, Institute for Clinical Research and Health Policy Studies, Boston, MA, USA
| | - Evan Minty
- O'Brien Centre for Population Health, Faculty of Medicine, University of Calgary, Canada
| | - Jose Posada
- Stanford Center for Biomedical Informatics Research, Stanford, CA, USA
| | - George Hripcsak
- Columbia University, New York, NY, USA
- Medical Informatics Services, New York-Presbyterian Hospital, New York, NY, USA
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17
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Howarth M, Bhatt M, Benterud E, Wolska A, Minty E, Choi KY, Devrome A, Harrison TG, Baylis B, Dixon E, Datta I, Pannu N, James MT. Development and initial implementation of electronic clinical decision supports for recognition and management of hospital-acquired acute kidney injury. BMC Med Inform Decis Mak 2020; 20:287. [PMID: 33148237 PMCID: PMC7640650 DOI: 10.1186/s12911-020-01303-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [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: 07/25/2020] [Accepted: 10/22/2020] [Indexed: 12/29/2022] Open
Abstract
Background Acute kidney injury (AKI) is common in hospitalized patients and is associated with poor patient outcomes and high costs of care. The implementation of clinical decision support tools within electronic medical record (EMR) could improve AKI care and outcomes. While clinical decision support tools have the potential to enhance recognition and management of AKI, there is limited description in the literature of how these tools were developed and whether they meet end-user expectations.
Methods We developed and evaluated the content, acceptability, and usability of electronic clinical decision support tools for AKI care. Multi-component tools were developed within a hospital EMR (Sunrise Clinical Manager™, Allscripts Healthcare Solutions Inc.) currently deployed in Calgary, Alberta, and included: AKI stage alerts, AKI adverse medication warnings, AKI clinical summary dashboard, and an AKI order set. The clinical decision support was developed for use by multiple healthcare providers at the time and point of care on general medical and surgical units. Functional and usability testing for the alerts and clinical summary dashboard was conducted via in-person evaluation sessions, interviews, and surveys of care providers. Formal user acceptance testing with clinical end-users, including physicians and nursing staff, was conducted to evaluate the AKI order set.
Results Considerations for appropriate deployment of both non-disruptive and interruptive functions was important to gain acceptability by clinicians. Functional testing and usability surveys for the alerts and clinical summary dashboard indicated that the tools were operating as desired and 74% (17/23) of surveyed healthcare providers reported that these tools were easy to use and could be learned quickly. Over three-quarters of providers (18/23) reported that they would utilize the tools in their practice. Three-quarters of the participants (13/17) in user acceptance testing agreed that recommendations within the order set were useful. Overall, 88% (15/17) believed that the order set would improve the care and management of AKI patients. Conclusions Development and testing of EMR-based decision support tools for AKI with clinicians led to high acceptance by clinical end-users. Subsequent implementation within clinical environments will require end-user education and engagement in system-level initiatives to use the tools to improve care.
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Affiliation(s)
- Megan Howarth
- Department of Medicine, Cumming School of Medicine, University of Calgary, 3280 Hospital Drive NW, Calgary, AB, T2N 4Z6, Canada
| | - Meha Bhatt
- Department of Medicine, Cumming School of Medicine, University of Calgary, 3280 Hospital Drive NW, Calgary, AB, T2N 4Z6, Canada
| | - Eleanor Benterud
- Department of Medicine, Cumming School of Medicine, University of Calgary, 3280 Hospital Drive NW, Calgary, AB, T2N 4Z6, Canada
| | - Anna Wolska
- Alberta Health Services, Calgary, AB, Canada
| | - Evan Minty
- Department of Medicine, Cumming School of Medicine, University of Calgary, 3280 Hospital Drive NW, Calgary, AB, T2N 4Z6, Canada
| | - Kyoo-Yoon Choi
- Department of Surgery, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Andrea Devrome
- Department of Surgery, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Tyrone G Harrison
- Department of Medicine, Cumming School of Medicine, University of Calgary, 3280 Hospital Drive NW, Calgary, AB, T2N 4Z6, Canada.,Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Barry Baylis
- Department of Medicine, Cumming School of Medicine, University of Calgary, 3280 Hospital Drive NW, Calgary, AB, T2N 4Z6, Canada
| | - Elijah Dixon
- Department of Surgery, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Indraneel Datta
- Department of Surgery, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Neesh Pannu
- Department of Medicine, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, AB, Canada
| | - Matthew T James
- Department of Medicine, Cumming School of Medicine, University of Calgary, 3280 Hospital Drive NW, Calgary, AB, T2N 4Z6, Canada. .,Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada. .,O'Brien Institute for Public Health, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada. .,Libin Cardiovascular Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.
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18
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Reps JM, Williams RD, You SC, Falconer T, Minty E, Callahan A, Ryan PB, Park RW, Lim HS, Rijnbeek P. Feasibility and evaluation of a large-scale external validation approach for patient-level prediction in an international data network: validation of models predicting stroke in female patients newly diagnosed with atrial fibrillation. BMC Med Res Methodol 2020; 20:102. [PMID: 32375693 PMCID: PMC7201646 DOI: 10.1186/s12874-020-00991-3] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [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: 07/09/2019] [Accepted: 04/23/2020] [Indexed: 12/17/2022] Open
Abstract
Background To demonstrate how the Observational Healthcare Data Science and Informatics (OHDSI) collaborative network and standardization can be utilized to scale-up external validation of patient-level prediction models by enabling validation across a large number of heterogeneous observational healthcare datasets. Methods Five previously published prognostic models (ATRIA, CHADS2, CHADS2VASC, Q-Stroke and Framingham) that predict future risk of stroke in patients with atrial fibrillation were replicated using the OHDSI frameworks. A network study was run that enabled the five models to be externally validated across nine observational healthcare datasets spanning three countries and five independent sites. Results The five existing models were able to be integrated into the OHDSI framework for patient-level prediction and they obtained mean c-statistics ranging between 0.57–0.63 across the 6 databases with sufficient data to predict stroke within 1 year of initial atrial fibrillation diagnosis for females with atrial fibrillation. This was comparable with existing validation studies. The validation network study was run across nine datasets within 60 days once the models were replicated. An R package for the study was published at https://github.com/OHDSI/StudyProtocolSandbox/tree/master/ExistingStrokeRiskExternalValidation. Conclusion This study demonstrates the ability to scale up external validation of patient-level prediction models using a collaboration of researchers and a data standardization that enable models to be readily shared across data sites. External validation is necessary to understand the transportability or reproducibility of a prediction model, but without collaborative approaches it can take three or more years for a model to be validated by one independent researcher. In this paper we show it is possible to both scale-up and speed-up external validation by showing how validation can be done across multiple databases in less than 2 months. We recommend that researchers developing new prediction models use the OHDSI network to externally validate their models.
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Affiliation(s)
- Jenna M Reps
- Janssen Research and Development, 1125 Trenton Harbourton Rd, Titusville, NJ, 08560, USA.
| | - Ross D Williams
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Seng Chan You
- Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, Republic of Korea
| | - Thomas Falconer
- Department of Biomedical Informatics, Columbia University Medical Center, New York, USA
| | - Evan Minty
- O'Brien Institute for Public Health, Faculty of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Alison Callahan
- Center for Biomedical Informatics Research, School of Medicine, Stanford University, Stanford, CA, USA
| | - Patrick B Ryan
- Janssen Research and Development, 1125 Trenton Harbourton Rd, Titusville, NJ, 08560, USA
| | - Rae Woong Park
- Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, Republic of Korea.,Department of Biomedical Sciences, Ajou University Graduate School of Medicine, Suwon, Republic of Korea
| | - Hong-Seok Lim
- Department of Cardiology, Ajou University Medical Centre, Suwon, Republic of Korea
| | - Peter Rijnbeek
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
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19
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Ling AY, Alsentzer E, Chen J, Banda JM, Tamang S, Minty E. Scalable Electronic Phenotyping For Studying Patient Comorbidities. AMIA Annu Symp Proc 2018; 2018:740-749. [PMID: 30815116 PMCID: PMC6371288] [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] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Over 75 million Americans have multiple concurrent chronic conditions and medical decision making for these patients is mostly based on retrospective cohort studies. Current methods to generate cohorts of patients with comorbidities are neither scalable nor generalizable. We propose a supervised machine learning algorithm for learning comorbidity phenotypes without requiring manually created training sets. First, we generated myocardial infarction (MI) and type-2 diabetes (T2DM) patient cohorts using ICD9-based imperfectly labeled samples upon which LASSO logistic regression models were trained. Second, we assessed the effects of training sample size, inclusion of physician input, and inclusion of clinical text features on model performance. Using ICD9 codes as our labeling heuristic, we achieved comparable performance to models created using keywords as labeling heuristic. We found that expert input and higher training sample sizes could compensate for the lack of clinical text derived features. However, our best performing model included clinical text as features with a large training sample size.
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Affiliation(s)
- Albee Y Ling
- Biomedical Informatics Training Program, Stanford University, Stanford, CA
| | - Emily Alsentzer
- Biomedical Informatics Training Program, Stanford University, Stanford, CA
| | - Josephine Chen
- Biomedical Informatics Training Program, Stanford University, Stanford, CA
| | - Juan M Banda
- Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, CA
| | - Suzanne Tamang
- Department of Biomedical Data Science, Stanford University, Stanford, CA
| | - Evan Minty
- Biomedical Informatics Training Program, Stanford University, Stanford, CA
- Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, CA
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20
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Fabreau G, Elliott M, Khanna S, Minty E, Wallace JE, de Grood J, Lewin A, Brown G, Bharwani A, Gilmour J, Lemaire JB. Shifting perceptions: a pre-post study to assess the impact of a senior resident rotation bundle. BMC Med Educ 2013; 13:115. [PMID: 23987729 PMCID: PMC3766268 DOI: 10.1186/1472-6920-13-115] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/19/2012] [Accepted: 08/27/2013] [Indexed: 05/29/2023]
Abstract
BACKGROUND Extended duty hours for residents are associated with negative consequences. Strategies to accommodate duty hour restrictions may also have unintended impacts. To eliminate extended duty hours and potentially lessen these impacts, we developed a senior resident rotation bundle that integrates a night float system, educational sessions on sleep hygiene, an electronic handover tool, and a simulation-based medical education curriculum. The aim of this study was to assess internal medicine residents' perceptions of the impact of the bundle on three domains: the senior residents' wellness, ability to deliver quality health care, and medical education experience. METHODS This prospective study compared eligible residents' experiences (N = 67) before and after a six-month trial of the bundle at a training program in western Canada. Data was collected using an on-line survey. Pre- and post-intervention scores for the final sample (N = 50) were presented as means and compared using the t-test for paired samples. RESULTS Participants felt that most aspects of the three domains were unaffected by the introduction of the bundle. Four improved and two worsened perception shifts emerged post-intervention: less exposure to personal harm, reduced potential for medical error, more successful teaching, fewer disruptions to other rotations, increased conflicting role demands and less staff physician supervision. CONCLUSIONS The rotation bundle integrates components that potentially ease some of the perceived negative consequences of night float rotations and duty hour restrictions. Future areas of study should include objective measures of the three domains to validate our study participants' perceptions.
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Affiliation(s)
- Gabriel Fabreau
- Faculty of Medicine, Health Sciences Centre, Foothills Campus, University of Calgary, 3330 Hospital Drive NW, Calgary AB T2N 4N1, Canada
| | - Meghan Elliott
- Faculty of Medicine, Health Sciences Centre, Foothills Campus, University of Calgary, 3330 Hospital Drive NW, Calgary AB T2N 4N1, Canada
| | - Suneil Khanna
- Faculty of Medicine, Health Sciences Centre, Foothills Campus, University of Calgary, 3330 Hospital Drive NW, Calgary AB T2N 4N1, Canada
| | - Evan Minty
- Faculty of Medicine, Health Sciences Centre, Foothills Campus, University of Calgary, 3330 Hospital Drive NW, Calgary AB T2N 4N1, Canada
| | - Jean E Wallace
- University of Calgary, 2500 University Dr NW, Calgary AB T2N 1N4, Canada
| | - Jill de Grood
- W21C Research and Innovation Center, GD01 TRW Building, 3280 Hospital Drive, NW, Calgary AB T2N 4Z6, Canada
| | - Adriane Lewin
- Faculty of Medicine, Health Sciences Centre, Foothills Campus, University of Calgary, 3330 Hospital Drive NW, Calgary AB T2N 4N1, Canada
| | - Garielle Brown
- W21C Research and Innovation Center, GD01 TRW Building, 3280 Hospital Drive, NW, Calgary AB T2N 4Z6, Canada
| | - Aleem Bharwani
- Faculty of Medicine, Health Sciences Centre, Foothills Campus, University of Calgary, 3330 Hospital Drive NW, Calgary AB T2N 4N1, Canada
| | - Janet Gilmour
- Faculty of Medicine, Health Sciences Centre, Foothills Campus, University of Calgary, 3330 Hospital Drive NW, Calgary AB T2N 4N1, Canada
| | - Jane B Lemaire
- Faculty of Medicine, Health Sciences Centre, Foothills Campus, University of Calgary, 3330 Hospital Drive NW, Calgary AB T2N 4N1, Canada
- W21C Research and Innovation Center, GD01 TRW Building, 3280 Hospital Drive, NW, Calgary AB T2N 4Z6, Canada
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