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Seehusen KE, Remaley AT, Sampson M, Meeusen JW, Larson NB, Decker PA, Killian JM, Takahashi PY, Roger VL, Manemann SM, Lam R, Bielinski SJ. Discordance Between Very Low-Density Lipoprotein Cholesterol and Low-Density Lipoprotein Cholesterol Increases Cardiovascular Disease Risk in a Geographically Defined Cohort. J Am Heart Assoc 2024; 13:e031878. [PMID: 38591325 DOI: 10.1161/jaha.123.031878] [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] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Accepted: 02/08/2024] [Indexed: 04/10/2024]
Abstract
BACKGROUND Clinical risk scores are used to identify those at high risk of atherosclerotic cardiovascular disease (ASCVD). Despite preventative efforts, residual risk remains for many individuals. Very low-density lipoprotein cholesterol (VLDL-C) and lipid discordance could be contributors to the residual risk of ASCVD. METHODS AND RESULTS Cardiovascular disease-free residents, aged ≥40 years, living in Olmsted County, Minnesota, were identified through the Rochester Epidemiology Project. Low-density lipoprotein cholesterol (LDL-C) and VLDL-C were estimated from clinically ordered lipid panels using the Sampson equation. Participants were categorized into concordant and discordant lipid pairings based on clinical cut points. Rates of incident ASCVD, including percutaneous coronary intervention, coronary artery bypass grafting, stroke, or myocardial infarction, were calculated during follow-up. The association of LDL-C and VLDL-C with ASCVD was assessed using Cox proportional hazards regression. Interaction between LDL-C and VLDL-C was assessed. The study population (n=39 098) was primarily White race (94%) and female sex (57%), with a mean age of 54 years. VLDL-C (per 10-mg/dL increase) was significantly associated with an increased risk of incident ASCVD (hazard ratio, 1.07 [95% CI, 1.05-1.09]; P<0.001]) after adjustment for traditional risk factors. The interaction between LDL-C and VLDL-C was not statistically significant (P=0.11). Discordant individuals with high VLDL-C and low LDL-C experienced the highest rate of incident ASCVD events, 16.9 per 1000 person-years, during follow-up. CONCLUSIONS VLDL-C and lipid discordance are associated with a greater risk of ASCVD and can be estimated from clinically ordered lipid panels to improve ASCVD risk assessment.
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Affiliation(s)
| | - Alan T Remaley
- Lipoprotein Metabolism Laboratory, Translational Vascular Medicine Branch National Heart, Lung, and Blood Institute, National Institutes of Health Bethesda MD
| | - Maureen Sampson
- Clinical Center, Department of Laboratory Medicine National Institutes of Health Bethesda MD
| | - Jeffrey W Meeusen
- Department of Laboratory Medicine and Pathology Mayo Clinic Rochester MN
| | | | - Paul A Decker
- Department of Quantitative Health Sciences Mayo Clinic Rochester MN
| | - Jill M Killian
- Department of Quantitative Health Sciences Mayo Clinic Rochester MN
| | - Paul Y Takahashi
- Division of Community Internal Medicine, Department of Medicine National Heart, Lung, and Blood Institute, National Institutes of Health Bethesda MD
| | - Véronique L Roger
- Department of Quantitative Health Sciences Mayo Clinic Rochester MN
- Epidemiology and Community Health Branch National Heart, Lung, and Blood Institute, National Institutes of Health Bethesda MD
| | | | - Reyna Lam
- Department of Quantitative Health Sciences Mayo Clinic Rochester MN
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2
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Bielinski SJ, Manemann SM, Lopes GS, Jiang R, Weston SA, Reichard RR, Norman AD, Vachon CM, Takahashi PY, Singh M, Larson NB, Roger VL, St Sauver JL. The Importance of Estimating Excess Deaths Regionally During the COVID-19 Pandemic. Mayo Clin Proc 2024; 99:437-444. [PMID: 38432749 PMCID: PMC10914321 DOI: 10.1016/j.mayocp.2023.11.007] [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] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Revised: 10/24/2023] [Accepted: 11/14/2023] [Indexed: 03/05/2024]
Abstract
National or statewide estimates of excess deaths have limited value to understanding the impact of the COVID-19 pandemic regionally. We assessed excess deaths in a 9-county geographically defined population that had low rates of COVID-19 and widescale availability of testing early in the pandemic, well-annotated clinical data, and coverage by 2 medical examiner's offices. We compared mortality rates (MRs) per 100,000 person-years in 2020 and 2021 with those in the 2019 reference period and MR ratios (MRRs). In 2020 and 2021, 177 and 219 deaths, respectively, were attributed to COVID-19 (MR = 52 and 66 per 100,000 person-years, respectively). COVID-19 MRs were highest in males, older persons, those living in rural areas, and those with 7 or more chronic conditions. Compared with 2019, we observed a 10% excess death rate in 2020 (MRR = 1.10 [95% CI, 1.04 to 1.15]), with excess deaths in females, older adults, and those with 7 or more chronic conditions. In contrast, we did not observe excess deaths overall in 2021 compared with 2019 (MRR = 1.04 [95% CI, 0.99 to 1.10]). However, those aged 18 to 39 years (MRR = 1.36 [95% CI, 1.03 to 1.80) and those with 0 or 1 chronic condition (MRR = 1.28 [95% CI, 1.05 to 1.56]) or 7 or more chronic conditions (MRR = 1.09 [95% CI, 1.03 to 1.15]) had increased mortality compared with 2019. This work highlights the value of leveraging regional populations that experienced a similar pandemic wave timeline, mitigation strategies, testing availability, and data quality.
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Affiliation(s)
- Suzette J Bielinski
- Division of Epidemiology, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN.
| | - Sheila M Manemann
- Division of Epidemiology, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN
| | - Guilherme S Lopes
- Division of Clinical Trials and Biostatistics, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN
| | - Ruoxiang Jiang
- Division of Clinical Trials and Biostatistics, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN
| | - Susan A Weston
- Division of Clinical Trials and Biostatistics, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN
| | - R Ross Reichard
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN
| | - Aaron D Norman
- Division of Epidemiology, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN
| | - Celine M Vachon
- Division of Epidemiology, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN
| | - Paul Y Takahashi
- Division of Community Internal Medicine, Department of Medicine, Mayo Clinic, Rochester, MN
| | - Mandeep Singh
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN
| | - Nicholas B Larson
- Division of Clinical Trials and Biostatistics, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN
| | - Véronique L Roger
- Division of Epidemiology, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN; Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN; Epidemiology and Community Health Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD
| | - Jennifer L St Sauver
- Division of Epidemiology, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN
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Axford D, Sohel F, Abedi V, Zhu Y, Zand R, Barkoudah E, Krupica T, Iheasirim K, Sharma UM, Dugani SB, Takahashi PY, Bhagra S, Murad MH, Saposnik G, Yousufuddin M. Development and internal validation of machine learning-based models and external validation of existing risk scores for outcome prediction in patients with ischaemic stroke. Eur Heart J Digit Health 2024; 5:109-122. [PMID: 38505491 PMCID: PMC10944684 DOI: 10.1093/ehjdh/ztad073] [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] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 10/14/2023] [Accepted: 10/30/2023] [Indexed: 03/21/2024]
Abstract
Aims We developed new machine learning (ML) models and externally validated existing statistical models [ischaemic stroke predictive risk score (iScore) and totalled health risks in vascular events (THRIVE) scores] for predicting the composite of recurrent stroke or all-cause mortality at 90 days and at 3 years after hospitalization for first acute ischaemic stroke (AIS). Methods and results In adults hospitalized with AIS from January 2005 to November 2016, with follow-up until November 2019, we developed three ML models [random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGBOOST)] and externally validated the iScore and THRIVE scores for predicting the composite outcomes after AIS hospitalization, using data from 721 patients and 90 potential predictor variables. At 90 days and 3 years, 11 and 34% of patients, respectively, reached the composite outcome. For the 90-day prediction, the area under the receiver operating characteristic curve (AUC) was 0.779 for RF, 0.771 for SVM, 0.772 for XGBOOST, 0.720 for iScore, and 0.664 for THRIVE. For 3-year prediction, the AUC was 0.743 for RF, 0.777 for SVM, 0.773 for XGBOOST, 0.710 for iScore, and 0.675 for THRIVE. Conclusion The study provided three ML-based predictive models that achieved good discrimination and clinical usefulness in outcome prediction after AIS and broadened the application of the iScore and THRIVE scoring system for long-term outcome prediction. Our findings warrant comparative analyses of ML and existing statistical method-based risk prediction tools for outcome prediction after AIS in new data sets.
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Affiliation(s)
- Daniel Axford
- Department of Information Technology, Mathematics and Statistics, College of Science, Health, Engineering and Education, Murdoch University, Murdoch, Australia
| | - Ferdous Sohel
- Department of Information Technology, Mathematics and Statistics, College of Science, Health, Engineering and Education, Murdoch University, Murdoch, Australia
| | - Vida Abedi
- Department of Public Health Science, Penn State College of Medicine, Hershey, PA, USA
| | - Ye Zhu
- Robert D. and Patricia E. Kern Centre for the Science of Healthcare Delivery, Mayo Clinic, Rochester, MN, USA
| | - Ramin Zand
- Neuroscience Institute, Geisinger Health System, 100 North Academy Ave, Danville, PA 17822, USA
- Neuroscience Institute, The Pennsylvania State University, Hershey, PA 17033, USA
| | - Ebrahim Barkoudah
- Internal Medicine/Hospital Medicine, Brigham and Women’s Hospital, Harvard University, Boston, MA, USA
| | - Troy Krupica
- Internal Medicine/Hospital Medicine, West Virginial University, Morgantown, WV, USA
| | - Kingsley Iheasirim
- Internal Medicine/Hospital Internal Medicine, Mayo Clinic Health System, Mankato, MN, USA
| | - Umesh M Sharma
- Hospital Internal Medicine, Mayo Clinic, Phoenix, AZ, USA
| | - Sagar B Dugani
- Hospital Internal Medicine, Mayo Clinic, Rochester, MN, USA
| | | | - Sumit Bhagra
- Endocrinology, Diabetes and Metabolism, Mayo Clinic Health System, Austin, MN, USA
| | - Mohammad H Murad
- Division of Public Health, Infectious Diseases, and Occupational Medicine, Mayo Clinic, Rochester, MN, USA
| | - Gustavo Saposnik
- Stroke Outcomes and Decision Neuroscience Research Unit, Division of Neurology, Department of Medicine and Li Ka Shing Knowledge Institute, St.Michael’s Hospital, University of Toronto, Toronto, Ontario, Canada
| | - Mohammed Yousufuddin
- Hospital Internal Medicine, Mayo Clinic Health System, 1000 1st Drive NW, Austin, MN 55912, USA
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Takahashi PY, Thorsteinsdottir B, McCoy RG, Ramar P, Canning RE, Hanson GJ, Baumbach LJ, Chandra A, Philpot LM. Impact of Program Changes Including Telemedicine and Telephonic Care During the COVID-19 Pandemic in Preventing 30-Day Hospital Readmission for Patients in a Care Transitions Program. J Prim Care Community Health 2024; 15:21501319241226547. [PMID: 38270059 PMCID: PMC10812102 DOI: 10.1177/21501319241226547] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Revised: 12/07/2023] [Accepted: 12/13/2023] [Indexed: 01/26/2024] Open
Abstract
INTRODUCTION/OBJECTIVES To describe health outcomes of older adults enrolled in the Mayo Clinic Care Transitions (MCCT) program before and during the COVID-19 pandemic compared to unenrolled patients. METHODS We conducted a retrospective cohort study of adults (age >60 years) in the MCCT program compared to a usual care control group from January 1, 2019, to September 20, 2022. The MCCT program involved a home, telephonic, or telemedicine visit by an advanced care provider. Outcomes were 30- and 180-day hospital readmissions, emergency department (ED) visit, and mortality. We performed a subgroup analysis after March 1, 2020 (during the pandemic). We analyzed data with Cox proportional hazards regression models and hazard ratios (HRs) with 95% CIs. RESULTS Of the 1,012 patients total, 354 were in the MCCT program and 658 were in the usual care group with a mean (SD) age of 81.1 (9.1) years overall. Thirty-day readmission was 16.9% (60 of 354) for MCCT patients and 14.7% (97 of 658) for usual care patients (HR, 1.24; 95% CI, 0.88-1.75). During the pandemic, the 30-day readmission rate was 15.1% (28 of 186) for MCCT patients and 14.9% (68 of 455) for usual care patients (HR, 1.20; 95% CI, 0.75-1.91). There was no difference between groups for 180-day hospitalization, 30- or 180-day ED visit, and 30- or 180-day mortality. CONCLUSIONS Numerous factors involving patients, providers, and health care delivery systems during the pandemic most likely contributed to these findings.
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Affiliation(s)
| | | | - Rozalina G. McCoy
- Mayo Clinic, Rochester, MN, USA
- University of Maryland School of Medicine, Baltimore, MD
- University of Maryland Institute for Health Computing, Bethesda, MD, USA
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5
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Moser ED, Manemann SM, Larson NB, St Sauver JL, Takahashi PY, Mielke MM, Rocca WA, Olson JE, Roger VL, Remaley AT, Decker PA, Killian JM, Bielinski SJ. Association Between Fluctuations in Blood Lipid Levels Over Time With Incident Alzheimer Disease and Alzheimer Disease-Related Dementias. Neurology 2023; 101:e1127-e1136. [PMID: 37407257 PMCID: PMC10513892 DOI: 10.1212/wnl.0000000000207595] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Accepted: 05/12/2023] [Indexed: 07/07/2023] Open
Abstract
BACKGROUND AND OBJECTIVES Prevention strategies for Alzheimer disease and Alzheimer disease-related dementias (AD/ADRDs) are urgently needed. Lipid variability, or fluctuations in blood lipid levels at different points in time, has not been examined extensively and may contribute to the risk of AD/ADRD. Lipid panels are a part of routine screening in clinical practice and routinely available in electronic health records (EHR). Thus, in a large geographically defined population-based cohort, we investigated the variation of multiple lipid types and their association to the development of AD/ADRD. METHODS All residents living in Olmsted County, Minnesota on the index date January 1, 2006, aged 60 years or older without an AD/ADRD diagnosis were identified. Persons with ≥3 lipid measurements including total cholesterol, triglycerides, low-density lipoprotein cholesterol (LDL-C), or high-density lipoprotein cholesterol (HDL-C) in the 5 years before index date were included. Lipid variation was defined as any change in individual's lipid levels over time regardless of direction and was measured using variability independent of the mean (VIM). Associations between lipid variation quintiles and incident AD/ADRD were assessed using Cox proportional hazards regression. Participants were followed through 2018 for incident AD/ADRD. RESULTS The final analysis included 11,571 participants (mean age 71 years; 54% female). Median follow-up was 12.9 years with 2,473 incident AD/ADRD cases. After adjustment for confounding variables including sex, race, baseline lipid measurements, education, BMI, and lipid-lowering treatment, participants in the highest quintile of total cholesterol variability had a 19% increased risk of incident AD/ADRD, and those in highest quintile of triglycerides, variability had a 23% increased risk. DISCUSSION In a large EHR derived cohort, those in the highest quintile of variability for total cholesterol and triglyceride levels had an increased risk of incident AD/ADRD. Further studies to identify the mechanisms behind this association are needed.
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Affiliation(s)
- Ethan D Moser
- From the Department of Quantitative Health Sciences (E.D.M., S.M.M., N.B.L., J.L.S.S., M.M.M., W.A.R., J.E.O., V.L.R., P.A.D., J.M.K., S.J.B.); Division of Community Internal Medicine (P.Y.T.), Department of Medicine, Mayo Clinic; Department of Neurology (M.M.M., W.A.R.), Rochester, MN; Department of Epidemiology and Prevention (M.M.M.), Wake Forest University School of Medicine, Winston-Salem, NC; Mayo Clinic Women's Health Research Center (W.A.R.); Department of Cardiovascular Medicine (V.L.R.), Mayo Clinic, Rochester, MN; Epidemiology and Community Branch (V.L.R.), National Heart, Lung, and Blood Institute, National Institutes of Health; and Lipoprotein Metabolism Laboratory (A.T.R.), Translational Vascular Medicine Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD
| | - Sheila M Manemann
- From the Department of Quantitative Health Sciences (E.D.M., S.M.M., N.B.L., J.L.S.S., M.M.M., W.A.R., J.E.O., V.L.R., P.A.D., J.M.K., S.J.B.); Division of Community Internal Medicine (P.Y.T.), Department of Medicine, Mayo Clinic; Department of Neurology (M.M.M., W.A.R.), Rochester, MN; Department of Epidemiology and Prevention (M.M.M.), Wake Forest University School of Medicine, Winston-Salem, NC; Mayo Clinic Women's Health Research Center (W.A.R.); Department of Cardiovascular Medicine (V.L.R.), Mayo Clinic, Rochester, MN; Epidemiology and Community Branch (V.L.R.), National Heart, Lung, and Blood Institute, National Institutes of Health; and Lipoprotein Metabolism Laboratory (A.T.R.), Translational Vascular Medicine Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD
| | - Nicholas B Larson
- From the Department of Quantitative Health Sciences (E.D.M., S.M.M., N.B.L., J.L.S.S., M.M.M., W.A.R., J.E.O., V.L.R., P.A.D., J.M.K., S.J.B.); Division of Community Internal Medicine (P.Y.T.), Department of Medicine, Mayo Clinic; Department of Neurology (M.M.M., W.A.R.), Rochester, MN; Department of Epidemiology and Prevention (M.M.M.), Wake Forest University School of Medicine, Winston-Salem, NC; Mayo Clinic Women's Health Research Center (W.A.R.); Department of Cardiovascular Medicine (V.L.R.), Mayo Clinic, Rochester, MN; Epidemiology and Community Branch (V.L.R.), National Heart, Lung, and Blood Institute, National Institutes of Health; and Lipoprotein Metabolism Laboratory (A.T.R.), Translational Vascular Medicine Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD
| | - Jennifer L St Sauver
- From the Department of Quantitative Health Sciences (E.D.M., S.M.M., N.B.L., J.L.S.S., M.M.M., W.A.R., J.E.O., V.L.R., P.A.D., J.M.K., S.J.B.); Division of Community Internal Medicine (P.Y.T.), Department of Medicine, Mayo Clinic; Department of Neurology (M.M.M., W.A.R.), Rochester, MN; Department of Epidemiology and Prevention (M.M.M.), Wake Forest University School of Medicine, Winston-Salem, NC; Mayo Clinic Women's Health Research Center (W.A.R.); Department of Cardiovascular Medicine (V.L.R.), Mayo Clinic, Rochester, MN; Epidemiology and Community Branch (V.L.R.), National Heart, Lung, and Blood Institute, National Institutes of Health; and Lipoprotein Metabolism Laboratory (A.T.R.), Translational Vascular Medicine Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD
| | - Paul Y Takahashi
- From the Department of Quantitative Health Sciences (E.D.M., S.M.M., N.B.L., J.L.S.S., M.M.M., W.A.R., J.E.O., V.L.R., P.A.D., J.M.K., S.J.B.); Division of Community Internal Medicine (P.Y.T.), Department of Medicine, Mayo Clinic; Department of Neurology (M.M.M., W.A.R.), Rochester, MN; Department of Epidemiology and Prevention (M.M.M.), Wake Forest University School of Medicine, Winston-Salem, NC; Mayo Clinic Women's Health Research Center (W.A.R.); Department of Cardiovascular Medicine (V.L.R.), Mayo Clinic, Rochester, MN; Epidemiology and Community Branch (V.L.R.), National Heart, Lung, and Blood Institute, National Institutes of Health; and Lipoprotein Metabolism Laboratory (A.T.R.), Translational Vascular Medicine Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD
| | - Michelle M Mielke
- From the Department of Quantitative Health Sciences (E.D.M., S.M.M., N.B.L., J.L.S.S., M.M.M., W.A.R., J.E.O., V.L.R., P.A.D., J.M.K., S.J.B.); Division of Community Internal Medicine (P.Y.T.), Department of Medicine, Mayo Clinic; Department of Neurology (M.M.M., W.A.R.), Rochester, MN; Department of Epidemiology and Prevention (M.M.M.), Wake Forest University School of Medicine, Winston-Salem, NC; Mayo Clinic Women's Health Research Center (W.A.R.); Department of Cardiovascular Medicine (V.L.R.), Mayo Clinic, Rochester, MN; Epidemiology and Community Branch (V.L.R.), National Heart, Lung, and Blood Institute, National Institutes of Health; and Lipoprotein Metabolism Laboratory (A.T.R.), Translational Vascular Medicine Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD
| | - Walter A Rocca
- From the Department of Quantitative Health Sciences (E.D.M., S.M.M., N.B.L., J.L.S.S., M.M.M., W.A.R., J.E.O., V.L.R., P.A.D., J.M.K., S.J.B.); Division of Community Internal Medicine (P.Y.T.), Department of Medicine, Mayo Clinic; Department of Neurology (M.M.M., W.A.R.), Rochester, MN; Department of Epidemiology and Prevention (M.M.M.), Wake Forest University School of Medicine, Winston-Salem, NC; Mayo Clinic Women's Health Research Center (W.A.R.); Department of Cardiovascular Medicine (V.L.R.), Mayo Clinic, Rochester, MN; Epidemiology and Community Branch (V.L.R.), National Heart, Lung, and Blood Institute, National Institutes of Health; and Lipoprotein Metabolism Laboratory (A.T.R.), Translational Vascular Medicine Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD
| | - Janet E Olson
- From the Department of Quantitative Health Sciences (E.D.M., S.M.M., N.B.L., J.L.S.S., M.M.M., W.A.R., J.E.O., V.L.R., P.A.D., J.M.K., S.J.B.); Division of Community Internal Medicine (P.Y.T.), Department of Medicine, Mayo Clinic; Department of Neurology (M.M.M., W.A.R.), Rochester, MN; Department of Epidemiology and Prevention (M.M.M.), Wake Forest University School of Medicine, Winston-Salem, NC; Mayo Clinic Women's Health Research Center (W.A.R.); Department of Cardiovascular Medicine (V.L.R.), Mayo Clinic, Rochester, MN; Epidemiology and Community Branch (V.L.R.), National Heart, Lung, and Blood Institute, National Institutes of Health; and Lipoprotein Metabolism Laboratory (A.T.R.), Translational Vascular Medicine Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD
| | - Véronique L Roger
- From the Department of Quantitative Health Sciences (E.D.M., S.M.M., N.B.L., J.L.S.S., M.M.M., W.A.R., J.E.O., V.L.R., P.A.D., J.M.K., S.J.B.); Division of Community Internal Medicine (P.Y.T.), Department of Medicine, Mayo Clinic; Department of Neurology (M.M.M., W.A.R.), Rochester, MN; Department of Epidemiology and Prevention (M.M.M.), Wake Forest University School of Medicine, Winston-Salem, NC; Mayo Clinic Women's Health Research Center (W.A.R.); Department of Cardiovascular Medicine (V.L.R.), Mayo Clinic, Rochester, MN; Epidemiology and Community Branch (V.L.R.), National Heart, Lung, and Blood Institute, National Institutes of Health; and Lipoprotein Metabolism Laboratory (A.T.R.), Translational Vascular Medicine Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD
| | - Alan T Remaley
- From the Department of Quantitative Health Sciences (E.D.M., S.M.M., N.B.L., J.L.S.S., M.M.M., W.A.R., J.E.O., V.L.R., P.A.D., J.M.K., S.J.B.); Division of Community Internal Medicine (P.Y.T.), Department of Medicine, Mayo Clinic; Department of Neurology (M.M.M., W.A.R.), Rochester, MN; Department of Epidemiology and Prevention (M.M.M.), Wake Forest University School of Medicine, Winston-Salem, NC; Mayo Clinic Women's Health Research Center (W.A.R.); Department of Cardiovascular Medicine (V.L.R.), Mayo Clinic, Rochester, MN; Epidemiology and Community Branch (V.L.R.), National Heart, Lung, and Blood Institute, National Institutes of Health; and Lipoprotein Metabolism Laboratory (A.T.R.), Translational Vascular Medicine Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD
| | - Paul A Decker
- From the Department of Quantitative Health Sciences (E.D.M., S.M.M., N.B.L., J.L.S.S., M.M.M., W.A.R., J.E.O., V.L.R., P.A.D., J.M.K., S.J.B.); Division of Community Internal Medicine (P.Y.T.), Department of Medicine, Mayo Clinic; Department of Neurology (M.M.M., W.A.R.), Rochester, MN; Department of Epidemiology and Prevention (M.M.M.), Wake Forest University School of Medicine, Winston-Salem, NC; Mayo Clinic Women's Health Research Center (W.A.R.); Department of Cardiovascular Medicine (V.L.R.), Mayo Clinic, Rochester, MN; Epidemiology and Community Branch (V.L.R.), National Heart, Lung, and Blood Institute, National Institutes of Health; and Lipoprotein Metabolism Laboratory (A.T.R.), Translational Vascular Medicine Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD
| | - Jill M Killian
- From the Department of Quantitative Health Sciences (E.D.M., S.M.M., N.B.L., J.L.S.S., M.M.M., W.A.R., J.E.O., V.L.R., P.A.D., J.M.K., S.J.B.); Division of Community Internal Medicine (P.Y.T.), Department of Medicine, Mayo Clinic; Department of Neurology (M.M.M., W.A.R.), Rochester, MN; Department of Epidemiology and Prevention (M.M.M.), Wake Forest University School of Medicine, Winston-Salem, NC; Mayo Clinic Women's Health Research Center (W.A.R.); Department of Cardiovascular Medicine (V.L.R.), Mayo Clinic, Rochester, MN; Epidemiology and Community Branch (V.L.R.), National Heart, Lung, and Blood Institute, National Institutes of Health; and Lipoprotein Metabolism Laboratory (A.T.R.), Translational Vascular Medicine Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD
| | - Suzette J Bielinski
- From the Department of Quantitative Health Sciences (E.D.M., S.M.M., N.B.L., J.L.S.S., M.M.M., W.A.R., J.E.O., V.L.R., P.A.D., J.M.K., S.J.B.); Division of Community Internal Medicine (P.Y.T.), Department of Medicine, Mayo Clinic; Department of Neurology (M.M.M., W.A.R.), Rochester, MN; Department of Epidemiology and Prevention (M.M.M.), Wake Forest University School of Medicine, Winston-Salem, NC; Mayo Clinic Women's Health Research Center (W.A.R.); Department of Cardiovascular Medicine (V.L.R.), Mayo Clinic, Rochester, MN; Epidemiology and Community Branch (V.L.R.), National Heart, Lung, and Blood Institute, National Institutes of Health; and Lipoprotein Metabolism Laboratory (A.T.R.), Translational Vascular Medicine Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD.
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Chacon Osorio GR, Wyles SP, Comfere NI, Takahashi PY, Manggaard JM, Fischer KM, Jett HN, Singla A, Vidal NY. Skin Cancer Associated With Chronic Leg Ulcers in the Population of Olmsted County, Minnesota. Mayo Clin Proc 2023; 98:1035-1041. [PMID: 37419572 PMCID: PMC10898998 DOI: 10.1016/j.mayocp.2023.02.020] [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] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 02/15/2023] [Accepted: 02/23/2023] [Indexed: 07/09/2023]
Abstract
Malignant skin tumors in the setting of chronic leg ulcers (CLUs) are often underdiagnosed which may contribute to treatment delay and poor outcomes. The aims of our study were to determine the incidence and clinical characteristics of skin cancers in leg ulcers in the Olmsted County population from 1995 to 2020. We used the Rochester Epidemiology Project (a collaboration between health care providers) infrastructure to describe this epidemiology, allowing "population-based" research. Electronic medical records of adult patients with International Classification of Diseases diagnosis codes for leg ulcers and skin cancers on the legs were queried. Thirty-seven individuals with skin cancers in nonhealing ulcers were identified. The cumulative incidence of skin cancer over the 25-year period was 37:7864 (0.47%). The overall incidence rate was 470 per 100,000 patients. Eleven (29.7%) men and 26 (70.3%) women were identified with mean age of 77 years. History of venous insufficiency was present in 30 (81.1%) patients and diabetes in 13 (35.1%) patients. Clinical characteristics of CLU with skin cancer included abnormal granulation tissue in 36 (94.7%) and irregular borders in 35 (94.6%) cases. Skin cancers among CLUs included 17 (41.5%) basal cell carcinomas, 17 (41.5%) squamous cell carcinomas, 2 (4.9%) melanomas, 2 (4.9%) porocarcinomas, 1 (2.4%) basosquamous cell carcinoma, and 1 (2.4%) eccrine adenocarcinoma. The apparent association between chronic wounds and subsequent biopsy-proven skin cancer of the same site was primarily observed in elderly patients; malignant transformation of wounds favored basal cell carcinoma and squamous cell carcinoma. This retrospective cohort study further characterizes the association between skin cancers and chronic leg wounds.
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Affiliation(s)
| | | | | | - Paul Y Takahashi
- Department of Internal Medicine, Mayo Clinic, Rochester, MN, USA
| | | | | | | | - Abhinav Singla
- Department of General Internal Medicine, Mayo Clinic, Rochester, MN, USA
| | - Nahid Y Vidal
- Department of Dermatology, Mayo Clinic, Rochester, MN, USA.
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Ryu E, Wi CI, Wheeler PH, King KS, Carlson RE, Juhn YJ, Takahashi PY. The Role of Individual-Level Socioeconomic Status on Nursing Home Placement Accounting for Neighborhood Characteristics. J Am Med Dir Assoc 2023; 24:1048-1053.e2. [PMID: 36841262 PMCID: PMC10962058 DOI: 10.1016/j.jamda.2023.01.016] [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: 10/18/2022] [Revised: 01/19/2023] [Accepted: 01/21/2023] [Indexed: 02/25/2023]
Abstract
OBJECTIVE Independent living is desirable for many older adults. Although several factors such as physical and cognitive functions are important predictors for nursing home placement (NHP), it is also reported that socioeconomic status (SES) affects the risk of NHP. In this study, we aimed to examine whether an individual-level measure of SES is associated with the risk of NHP after accounting for neighborhood characteristics. DESIGN A population-based study (Olmsted County, Minnesota, USA). SETTING AND PARTICIPANTS Older adults (age 65+ years) with no prior history of NHP. METHODS Electronic health records (EHR) were used to identify individuals with any NHP between April 1, 2012 (baseline date) and April 30, 2019. Association between the (HOUsing-based index of SocioEconomic Status (HOUSES) index, an individual-level SES measure based on housing characteristics of current residence, and risk of NHP was tested using random effects Cox proportional hazard model adjusting for area deprivation index (ADI), an aggregated SES measure that captures neighborhood characteristics, and other pertinent confounders such as age and chronic disease burden. RESULTS Among 15,031 older adults, 3341 (22.2%) experienced NHP during follow-up period (median: 7.1 years). At baseline date, median age was 73 years old with 55% female persons, 91% non-Hispanic Whites, and median number of chronic conditions of 4. Accounting for pertinent confounders, the HOUSES index was strongly associated with risk of NHP (hazard ratio 1.89; 95% confidence interval 1.66‒2.15 for comparing the lowest vs highest quartiles), which was not influenced by further accounting for ADI. CONCLUSIONS AND IMPLICATIONS This study demonstrates that an individual-level SES measure capturing current individual-specific socioeconomic circumstances plays a significant role for predicting NHP independent of neighborhood characteristics where they reside. This study suggests that older adults who are at higher risk of NHP can be identified by utilizing the HOUSES index and potential individual-level intervention strategies can be applied to reduce the risk for those with higher risk.
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Affiliation(s)
- Euijung Ryu
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA.
| | - Chung-Il Wi
- Department of Pediatric and Adolescent Medicine, Mayo Clinic, Rochester, MN, USA
| | - Philip H Wheeler
- Department of Pediatric and Adolescent Medicine, Mayo Clinic, Rochester, MN, USA
| | - Katherine S King
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Rachel E Carlson
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Young J Juhn
- Department of Pediatric and Adolescent Medicine, Mayo Clinic, Rochester, MN, USA
| | - Paul Y Takahashi
- Division of Primary Care and Internal Medicine, Mayo Clinic, Rochester, MN, USA
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8
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Bielinski SJ, Yanes Cardozo LL, Takahashi PY, Larson NB, Castillo A, Podwika A, De Filippis E, Hernandez V, Mahajan GJ, Gonzalez C, Shubhangi, Decker PA, Killian JM, Olson JE, St. Sauver JL, Shah P, Vella A, Ryu E, Liu H, Marshall GD, Cerhan JR, Singh D, Summers RL. Predictors of Metformin Failure: Repurposing Electronic Health Record Data to Identify High-Risk Patients. J Clin Endocrinol Metab 2023; 108:1740-1746. [PMID: 36617249 PMCID: PMC10271218 DOI: 10.1210/clinem/dgac759] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Revised: 12/21/2022] [Accepted: 12/28/2022] [Indexed: 01/09/2023]
Abstract
CONTEXT Metformin is the first-line drug for treating diabetes but has a high failure rate. OBJECTIVE To identify demographic and clinical factors available in the electronic health record (EHR) that predict metformin failure. METHODS A cohort of patients with at least 1 abnormal diabetes screening test that initiated metformin was identified at 3 sites (Arizona, Mississippi, and Minnesota). We identified 22 047 metformin initiators (48% female, mean age of 57 ± 14 years) including 2141 African Americans, 440 Asians, 962 Other/Multiracial, 1539 Hispanics, and 16 764 non-Hispanic White people. We defined metformin failure as either the lack of a target glycated hemoglobin (HbA1c) (<7%) within 18 months of index or the start of dual therapy. We used tree-based extreme gradient boosting (XGBoost) models to assess overall risk prediction performance and relative contribution of individual factors when using EHR data for risk of metformin failure. RESULTS In this large diverse population, we observed a high rate of metformin failure (43%). The XGBoost model that included baseline HbA1c, age, sex, and race/ethnicity corresponded to high discrimination performance (C-index of 0.731; 95% CI 0.722, 0.740) for risk of metformin failure. Baseline HbA1c corresponded to the largest feature performance with higher levels associated with metformin failure. The addition of other clinical factors improved model performance (0.745; 95% CI 0.737, 0.754, P < .0001). CONCLUSION Baseline HbA1c was the strongest predictor of metformin failure and additional factors substantially improved performance suggesting that routinely available clinical data could be used to identify patients at high risk of metformin failure who might benefit from closer monitoring and earlier treatment intensification.
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Affiliation(s)
- Suzette J Bielinski
- Division of Epidemiology, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN 55905, USA
| | - Licy L Yanes Cardozo
- Department of Cell and Molecular Biology, University of Mississippi Medical Center, Jackson, MS 39216, USA
- Department of Medicine, University of Mississippi Medical Center, Jackson, MS 39216, USA
- Mississippi Center of Excellence in Perinatal Research, University of Mississippi Medical Center, Jackson, MS 39216, USA
- Women's Health Research Center, University of Mississippi Medical Center, Jackson, MS 39216, USA
| | - Paul Y Takahashi
- Division of Community Internal Medicine, Department of Internal Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Nicholas B Larson
- Division of Clinical Trials and Biostatistics, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN 55905, USA
| | - Alexandra Castillo
- Center for Informatics and Analytics, University of Mississippi Medical Center, Jackson, MS 39216, USA
| | | | - Eleanna De Filippis
- Division of Endocrinology, Diabetes, and Metabolism Department of Medicine, Mayo Clinic Arizona, Scottsdale, AZ 85259, USA
| | | | - Gouri J Mahajan
- UMMC Biobank-School of Medicine, University of Mississippi Medical Center, Jackson, MS 39216, USA
| | | | - Shubhangi
- Mountain Park Health Center, Phoenix, AZ 85012, USA
| | - Paul A Decker
- Division of Clinical Trials and Biostatistics, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN 55905, USA
| | - Jill M Killian
- Division of Clinical Trials and Biostatistics, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN 55905, USA
| | - Janet E Olson
- Division of Epidemiology, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN 55905, USA
- Center for Individualized Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Jennifer L St. Sauver
- Division of Epidemiology, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN 55905, USA
- Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN 55905, USA
| | - Pankaj Shah
- Division of Endocrinology, Diabetes, Metabolism, and Nutrition, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Adrian Vella
- Division of Endocrinology, Diabetes, Metabolism, and Nutrition, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Euijung Ryu
- Division of Computational Biology, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN 55905, USA
| | - Hongfang Liu
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN 55905, USA
| | - Gailen D Marshall
- Department of Medicine, University of Mississippi Medical Center, Jackson, MS 39216, USA
| | - James R Cerhan
- Division of Epidemiology, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN 55905, USA
| | | | - Richard L Summers
- Department of Cell and Molecular Biology, University of Mississippi Medical Center, Jackson, MS 39216, USA
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9
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Manemann SM, Weston SA, Jiang R, Larson NB, Roger VL, Takahashi PY, Chamberlain AM, Singh M, St Sauver JL, Bielinski SJ. Health Care Utilization and Death in Patients With Heart Failure During the COVID-19 Pandemic. Mayo Clin Proc Innov Qual Outcomes 2023; 7:194-202. [PMID: 37229286 PMCID: PMC10099179 DOI: 10.1016/j.mayocpiqo.2023.04.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Revised: 03/31/2023] [Accepted: 04/05/2023] [Indexed: 05/27/2023] Open
Abstract
Objective To compare the 1-year health care utilization and mortality in persons living with heart failure (HF) before and during the coronavirus disease 2019 (COVID-19) pandemic. Patients and Methods Residents of a 9-county area in southeastern Minnesota aged 18 years or older with a HF diagnosis on January 1, 2019; January 1, 2020; and January 1, 2021, were identified and followed up for 1-year for vital status, emergency department (ED) visits, and hospitalizations. Results We identified 5631 patients with HF (mean age, 76 years; 53% men) on January 1, 2019, 5996 patients (mean age, 76 years; 52% men) on January 1, 2020, and 6162 patients (mean age, 75 years; 54% men) on January 1, 2021. After adjustment for comorbidities and risk factors, patients with HF in 2020 and patients with HF in 2021 experienced similar risks of mortality compared with those in 2019. After adjustment, patients with HF in 2020 and 2021 were less likely to experience all-cause hospitalizations (2020: rate ratio [RR], 0.88; 95% CI, 0.81-0.95; 2021: RR, 0.90; 95% CI, 0.83-0.97) compared with patients in 2019. Patients with HF in 2020 were also less likely to experience ED visits (RR, 0.85; 95% CI, 0.80-0.92). Conclusion In this large population-based study in southeastern Minnesota, we observed an approximately 10% decrease in hospitalizations among patients with HF in 2020 and 2021 and a 15% decrease in ED visits in 2020 compared with those in 2019. Despite the change in health care utilization, we found no difference in the 1-year mortality between patients with HF in 2020 and those in 2021 compared with those in 2019. It is unknown whether any longer-term consequences will be observed.
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Affiliation(s)
- Sheila M Manemann
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN
| | - Susan A Weston
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN
| | - Ruoxiang Jiang
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN
| | - Nicholas B Larson
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN
| | - Véronique L Roger
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN
- National Institutes of Health, Epidemiology and Community Health Branch, National Heart, Lung, and Blood Institute, Bethesda, MD
| | - Paul Y Takahashi
- Division of Community Internal Medicine, Department of Medicine, Mayo Clinic, Rochester, MN
| | - Alanna M Chamberlain
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN
| | - Mandeep Singh
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN
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10
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Bartley MM, Manggaard JM, Fischer KM, Holland DE, Takahashi PY. Dementia Care in the Last Year of Life: Experiences in a Community Practice and in Skilled Nursing Facilities. J Palliat Care 2023; 38:135-142. [PMID: 36148476 PMCID: PMC10026163 DOI: 10.1177/08258597221125607] [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] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
OBJECTIVE People living with dementia often have high care needs at the end-of-life. We compared care delivery in the last year of life for people living with dementia in the community (home or assisted living facilities [ALFs]) versus those in skilled nursing facilities (SNFs). METHODS A retrospective study was performed of older adults with a dementia diagnosis who died in the community or SNFs from 2013 through 2018. Primary outcomes were numbers of hospitalizations and emergency department visits in the last year of life. Secondary outcomes were completed advance care plans, hospice enrollment, time in hospice, practitioner visits, and intensive care unit admissions. RESULTS Of 1203 older adults with dementia, 622 (51.7%) lived at home/ALFs; 581 (48.3%) lived in SNFs. At least 1 hospitalization was recorded for 70.7% living at home/ALFs versus 50.8% in SNFs (P < .001), similar to percentages of emergency department visits (80.2% vs 58.0% of the home/ALF and SNF groups, P < .001). SNF residents had more practitioner visits than home/ALF residents: median (IQR), 9.0 (6.0-12.0) versus 5.0 (3.0-9.0; P < .001). No advance care plan was documented for 12.2% (n = 76) of the home/ALF group versus 4.6% (n = 27) of the SNF group (P < .001). Nearly 57% of SNF residents were enrolled in hospice versus 68.3% at home/ALFs (P < .001). The median time in hospice was 26.5 days in SNFs versus 30.0 days at home/ALFs (P = .67). CONCLUSIONS Older adults with dementia frequently receive acute care in their last year of life. Hospice care was more common for home/ALF residents. Time in hospice was short.
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Affiliation(s)
- Mairead M Bartley
- Division of Community Internal Medicine, 384842Mayo Clinic, Rochester, MN, USA
| | | | - Karen M Fischer
- Division of Clinical Trials and Biostatistics, 384842Mayo Clinic, Rochester, MN, USA
| | - Diane E Holland
- Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, 384842Mayo Clinic, Rochester, MN, USA
| | - Paul Y Takahashi
- Division of Community Internal Medicine, 384842Mayo Clinic, Rochester, MN, USA
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11
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Kwok SWH, Wang G, Sohel F, Kashani KB, Zhu Y, Wang Z, Antpack E, Khandelwal K, Pagali SR, Nanda S, Abdalrhim AD, Sharma UM, Bhagra S, Dugani S, Takahashi PY, Murad MH, Yousufuddin M. An artificial intelligence approach for predicting death or organ failure after hospitalization for COVID-19: development of a novel risk prediction tool and comparisons with ISARIC-4C, CURB-65, qSOFA, and MEWS scoring systems. Respir Res 2023; 24:79. [PMID: 36915107 PMCID: PMC10010216 DOI: 10.1186/s12931-023-02386-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 03/07/2023] [Indexed: 03/14/2023] Open
Abstract
BACKGROUND We applied machine learning (ML) algorithms to generate a risk prediction tool [Collaboration for Risk Evaluation in COVID-19 (CORE-COVID-19)] for predicting the composite of 30-day endotracheal intubation, intravenous administration of vasopressors, or death after COVID-19 hospitalization and compared it with the existing risk scores. METHODS This is a retrospective study of adults hospitalized with COVID-19 from March 2020 to February 2021. Patients, each with 92 variables, and one composite outcome underwent feature selection process to identify the most predictive variables. Selected variables were modeled to build four ML algorithms (artificial neural network, support vector machine, gradient boosting machine, and Logistic regression) and an ensemble model to generate a CORE-COVID-19 model to predict the composite outcome and compared with existing risk prediction scores. The net benefit for clinical use of each model was assessed by decision curve analysis. RESULTS Of 1796 patients, 278 (15%) patients reached primary outcome. Six most predictive features were identified. Four ML algorithms achieved comparable discrimination (P > 0.827) with c-statistics ranged 0.849-0.856, calibration slopes 0.911-1.173, and Hosmer-Lemeshow P > 0.141 in validation dataset. These 6-variable fitted CORE-COVID-19 model revealed a c-statistic of 0.880, which was significantly (P < 0.04) higher than ISARIC-4C (0.751), CURB-65 (0.735), qSOFA (0.676), and MEWS (0.674) for outcome prediction. The net benefit of the CORE-COVID-19 model was greater than that of the existing risk scores. CONCLUSION The CORE-COVID-19 model accurately assigned 88% of patients who potentially progressed to 30-day composite events and revealed improved performance over existing risk scores, indicating its potential utility in clinical practice.
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Affiliation(s)
| | - Guanjin Wang
- Department of Information Technology, Murdoch University, Murdoch, Australia
| | - Ferdous Sohel
- Department of Information Technology, Murdoch University, Murdoch, Australia
| | - Kianoush B Kashani
- Division of Nephrology and Hypertension, Mayo Clinic, Rochester, MN, USA
| | - Ye Zhu
- Robert D. and Patricia E. Kern Centre for the Science of Healthcare Delivery, Mayo Clinic, Rochester, MN, USA
| | - Zhen Wang
- Robert D. and Patricia E. Kern Centre for the Science of Healthcare Delivery, Mayo Clinic, Rochester, MN, USA
| | - Eduardo Antpack
- Division of Hospital Internal Medicine, Mayo Clinic Health System, Austin, MN, USA
| | | | - Sandeep R Pagali
- Division of Hospital Internal Medicine, Mayo Clinic, Rochester, MN, USA
| | - Sanjeev Nanda
- Division of General Internal Medicine, Mayo Clinic, Rochester, MN, USA
| | - Ahmed D Abdalrhim
- Division of General Internal Medicine, Mayo Clinic, Rochester, MN, USA
| | - Umesh M Sharma
- Division of Hospital Internal Medicine, Mayo Clinic, Phoenix, AZ, USA
| | - Sumit Bhagra
- Department of Endocrine and Metabolism, Mayo Clinic Health System, Austin, MN, USA
| | - Sagar Dugani
- Division of Hospital Internal Medicine, Mayo Clinic, Rochester, MN, USA
| | - Paul Y Takahashi
- Division of Community Internal Medicine, Mayo Clinic, Rochester, MN, USA
| | - Mohammad H Murad
- Robert D. and Patricia E. Kern Centre for the Science of Healthcare Delivery, Mayo Clinic, Rochester, MN, USA.,Division of Preventive Medicine, Mayo Clinic, Rochester, MN, USA
| | - Mohammed Yousufuddin
- Division of Surgery, Mayo Clinic, Rochester, MN, USA. .,Hospital Internal Medicine, Mayo Clinic Health System, Mayo Clinic, 1000 1st Drive NW, Austin, MN, USA.
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12
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Manemann SM, Bielinski SJ, Moser ED, St Sauver JL, Takahashi PY, Roger VL, Olson JE, Chamberlain AM, Remaley AT, Decker PA, Killian JM, Larson NB. Variability in Lipid Levels and Risk for Cardiovascular Disease: An Electronic Health Record-Based Population Cohort Study. J Am Heart Assoc 2023; 12:e027639. [PMID: 36870945 PMCID: PMC10111433 DOI: 10.1161/jaha.122.027639] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/06/2023]
Abstract
Background Larger within-patient variability of lipid levels has been associated with increased risk of cardiovascular disease (CVD); however, measures of lipid variability require ≥3 measurements and are not currently used clinically. We investigated the feasibility of calculating lipid variability within a large electronic health record-based population cohort and assessed associations with incident CVD. Methods and Results We identified all individuals ≥40 years of age who resided in Olmsted County, MN, on January 1, 2006 (index date), without prior CVD, defined as myocardial infarction, coronary artery bypass graft surgery, percutaneous coronary intervention, or CVD death. Patients with ≥3 measurements of total cholesterol, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, or triglycerides during the 5 years before the index date were retained. Lipid variability was calculated using variability independent of the mean. Patients were followed through December 31, 2020 for incident CVD. We identified 19 652 individuals (mean age 61 years; 55% female), who were CVD-free and had variability independent of the mean calculated for at least 1 lipid type. After adjustment, those with highest total cholesterol variability had a 20% increased risk of CVD (Q5 versus Q1 hazard ratio, 1.20 [95% CI, 1.06-1.37]). Results were similar for low-density lipoprotein cholesterol and high-density lipoprotein cholesterol. Conclusions In a large electronic health record-based population cohort, high variability in total cholesterol, high-density lipoprotein cholesterol, and low-density lipoprotein cholesterol was associated with an increased risk of CVD, independent of traditional risk factors, suggesting it may be a possible risk marker and target for intervention. Lipid variability can be calculated in the electronic health record environment, but more research is needed to determine its clinical utility.
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Affiliation(s)
| | | | - Ethan D Moser
- Department of Quantitative Health Sciences Mayo Clinic Rochester MN
| | | | - Paul Y Takahashi
- Division of Community Internal Medicine, Department of Medicine Mayo Clinic Rochester MN
| | - Véronique L Roger
- Department of Quantitative Health Sciences Mayo Clinic Rochester MN.,Department of Cardiovascular Medicine Mayo Clinic Rochester MN.,Epidemiology and Community Health Branch National Institutes of Health Bethesda MD
| | - Janet E Olson
- Department of Quantitative Health Sciences Mayo Clinic Rochester MN
| | - Alanna M Chamberlain
- Department of Quantitative Health Sciences Mayo Clinic Rochester MN.,Department of Cardiovascular Medicine Mayo Clinic Rochester MN
| | - Alan T Remaley
- Lipoprotein Metabolism Laboratory, Translational Vascular Medicine Branch, National Heart, Lung, and Blood Institute National Institutes of Health Bethesda MD
| | - Paul A Decker
- Department of Quantitative Health Sciences Mayo Clinic Rochester MN
| | - Jill M Killian
- Department of Quantitative Health Sciences Mayo Clinic Rochester MN
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13
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Juhn YJ, Wi CI, Takahashi PY, Ryu E, King KS, Hickman JA, Yao JD, Binnicker MJ, Natoli TL, Evans TK, Sampathkumar P, Patten C, Luyts D, Pirçon JY, Damaso S, Pignolo RJ. Incidence of Respiratory Syncytial Virus Infection in Older Adults Before and During the COVID-19 Pandemic. JAMA Netw Open 2023; 6:e2250634. [PMID: 36662530 PMCID: PMC9860520 DOI: 10.1001/jamanetworkopen.2022.50634] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Accepted: 11/20/2022] [Indexed: 01/21/2023] Open
Abstract
Importance Little is known about the burden and outcomes of respiratory syncytial virus (RSV)-positive acute respiratory infection (ARI) in community-dwelling older adults. Objective To assess the incidence of RSV-positive ARI before and during the COVID-19 pandemic, and to assess outcomes for RSV-positive ARI in older adults. Design, Setting, and Participants This was a community-based cohort study of adults residing in southeast Minnesota that followed up with 2325 adults aged 50 years or older for 2 RSV seasons (2019-2021) to assess the incidence of RSV-positive ARI. The study assessed outcomes at 2 to 4 weeks, 6 to 7 months, and 12 to 13 months after RSV-positive ARI. Exposure RSV-positive and -negative ARI. Main Outcomes and Measures RSV status was the main study outcome. Incidence and attack rates of RSV-positive ARI were calculated during each RSV season, including before (October 2019 to April 2020) and during (October 2020 to April 2021) COVID-19 pandemic, and further calculated during non-RSV season (May to September 2021) for assessing impact of COVID-19. The self-reported quality of life (QOL) by Short-Form Health Survey-36 (SF-36) and physical functional measures (eg, 6-minute walk and spirometry) at each time point was assessed. Results In this study of 2325 participants, the median (range) age of study participants was 67 (50-98) years, 1380 (59%) were female, and 2240 (96%) were non-Hispanic White individuals. The prepandemic incidence rate of RSV-positive ARI was 48.6 (95% CI, 36.9-62.9) per 1000 person-years with a 2.50% (95% CI, 1.90%-3.21%) attack rate. No RSV-positive ARI case was identified during the COVID-19 pandemic RSV season. Incidence of 10.2 (95% CI, 4.1-21.1) per 1000 person-years and attack rate of 0.42%; (95% CI, 0.17%-0.86%) were observed during the summer of 2021. Based on prepandemic RSV season results, participants with RSV-positive ARI (vs matched RSV-negative ARI) reported significantly lower QOL adjusted mean difference (limitations due to physical health, -16.7 [95% CI, -31.8 to -1.8]; fatigue, -8.4 [95% CI, -14.3 to -2.4]; and difficulty in social functioning, -11.9 [95% CI, -19.8 to -4.0] within 2 to 4 weeks after RSV-positive ARI [ie, short-term outcome]). Compared with participants with RSV-negative ARI, those with RSV-positive ARI also had lower QOL (fatigue: -4.0 [95% CI, -8.5 to -1.3]; difficulty in social functioning, -5.8 [95% CI, -10.3 to -1.3]; and limitation due to emotional problem, -7.0 [95% CI, -12.7 to -1.3] at 6 to 7 months after RSV-positive ARI [intermediate-term outcome]; fatigue, -4.4 [95% CI, -7.3 to -1.5]; difficulty in social functioning, -5.2 [95% CI, -8.7 to -1.7] and limitation due to emotional problem, -5.7 [95% CI, -10.7 to -0.6] at 12-13 months after RSV-positive ARI [ie, long-term outcomes]) independent of age, sex, race and/or ethnicity, socioeconomic status, and high-risk comorbidities. Conclusions and Relevance In this cohort study, the burden of RSV-positive ARI in older adults during the pre-COVID-19 period was substantial. After a reduction of RSV-positive ARI incidence from October 2020 to April 2021, RSV-positive ARI re-emerged during the summer of 2021. RSV-positive ARI was associated with significant long-term lower QOL beyond the short-term lower QOL in older adults.
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Affiliation(s)
- Young J. Juhn
- Department of Pediatric and Adolescent Medicine and Internal Medicine, Mayo Clinic, Rochester, Minnesota
| | - Chung-Il Wi
- Department of Pediatric and Adolescent Medicine and Internal Medicine, Mayo Clinic, Rochester, Minnesota
| | - Paul Y. Takahashi
- Division of Community Internal Medicine, Mayo Clinic, Rochester, Minnesota
| | - Euijung Ryu
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota
| | - Katherine S. King
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota
| | - Joel A. Hickman
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota
| | - Joseph D. Yao
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota
| | - Matthew J. Binnicker
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota
| | - Traci L. Natoli
- Department of Pediatric and Adolescent Medicine and Internal Medicine, Mayo Clinic, Rochester, Minnesota
| | - Tamara K. Evans
- Department of Medicine Research, Mayo Clinic, Rochester, Minnesota
| | | | - Christi Patten
- Behavioral Health Research Program, Department of Psychiatry and Psychology, Mayo Clinic, Rochester, Minnesota
| | | | | | | | - Robert J. Pignolo
- Divisions of Hospital Internal Medicine, Endocrinology, and Geriatric Medicine and Gerontology, Department of Medicine, Mayo Clinic, Rochester, Minnesota
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Yousufuddin M, Murad MH, Peters JL, Ambriz TJ, Blocker KR, Khandelwal K, Pagali SR, Nanda S, Abdalrhim A, Patel U, Dugani S, Arumaithurai K, Takahashi PY, Kashani KB. Within-Person Blood Pressure Variability During Hospitalization and Clinical Outcomes Following First-Ever Acute Ischemic Stroke. Am J Hypertens 2023; 36:23-32. [PMID: 36130108 DOI: 10.1093/ajh/hpac106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2022] [Accepted: 09/19/2022] [Indexed: 12/30/2022] Open
Abstract
BACKGROUND Uncertainty remains over the relationship between blood pressure (BP) variability (BPV), measured in hospital settings, and clinical outcomes following acute ischemic stroke (AIS). We examined the association between within-person systolic blood pressure (SBP) variability (SBPV) during hospitalization and readmission-free survival, all-cause readmission, or all-cause mortality 1 year after AIS. METHODS In a cohort of 862 consecutive patients (age [mean ± SD] 75 ± 15 years, 55% women) with AIS (2005-2018, follow-up through 2019), we measured SBPV as quartiles of standard deviations (SD) and coefficient of variation (CV) from a median of 16 SBP readings obtained throughout hospitalization. RESULTS In the cumulative cohort, the measured SD and CV of SBP in mmHg were 16 ± 6 and 10 ± 5, respectively. The hazard ratios (HR) for readmission-free survival between the highest vs. lowest quartiles were 1.44 (95% confidence interval [CI] 1.04-1.81) for SD and 1.29 (95% CI 0.94-1.78) for CV after adjustment for demographics and comorbidities. Similarly, incident readmission or mortality remained consistent between the highest vs. lowest quartiles of SD and CV (readmission: HR 1.29 [95% CI 0.90-1.78] for SD, HR 1.29 [95% CI 0.94-1.78] for CV; mortality: HR 1.15 [95% CI 0.71-1.87] for SD, HR 0.86 [95% CI 0.55-1.36] for CV). CONCULSIONS In patients with first AIS, SBPV measured as quartiles of SD or CV based on multiple readings throughout hospitalization has no independent prognostic implications for the readmission-free survival, readmission, or mortality. This underscores the importance of overall patient care rather than a specific focus on BP parameters during hospitalization for AIS.
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Affiliation(s)
- Mohammed Yousufuddin
- Department of Hospital Internal Medicine, Mayo Clinic Health System, Austin, Minnesota, USA
| | - M H Murad
- Robert D. and Patricia E. Kern Center for the Science of Healthcare Delivery, Mayo Clinic, Rochester, Minnesota, USA.,Division of Public Health, Infectious Diseases, and Occupational Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Jessica L Peters
- Department of Hospital Internal Medicine, Mayo Clinic Health System, Austin, Minnesota, USA
| | - Taylor J Ambriz
- Department of Hospital Internal Medicine, Mayo Clinic Health System, Austin, Minnesota, USA
| | - Katherine R Blocker
- Department of Hospital Internal Medicine, Mayo Clinic Health System, Austin, Minnesota, USA
| | - Kanika Khandelwal
- Department of Hospital Internal Medicine, Mayo Clinic Health System, Austin, Minnesota, USA
| | - Sandeep R Pagali
- Division of Hospital Internal Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Sanjeev Nanda
- Division of Internal Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Ahmed Abdalrhim
- Division of Internal Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Urvish Patel
- Icahn School of Medicine, Mount Sinai, New York, USA
| | - Sagar Dugani
- Division of Hospital Internal Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | | | - Paul Y Takahashi
- Division of Community Internal Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Kianoush B Kashani
- Division of Nephrology and Hypertension, Mayo Clinic, Rochester, Minnesota, USA
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15
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Wi CI, King KS, Ryu E, Natoli TL, Miller RP, Spiten MJ, Borah BJ, Takahashi PY, Yao X, Noseworthy PA, Pignolo RJ, Juhn YJ. Application of Innovative Subject Recruitment System for Batch Enrollment: A Pilot Study. J Prim Care Community Health 2023; 14:21501319231194967. [PMID: 37646152 PMCID: PMC10467239 DOI: 10.1177/21501319231194967] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 07/24/2023] [Accepted: 07/26/2023] [Indexed: 09/01/2023] Open
Abstract
INTRODUCTION Using a digital process that leverages electronic health records (EHRs) can ease many of the challenges presented by the traditional enrollment process for clinical trials. We tested if automated batch enrollment using a technology-enabled subject recruitment system (TESRS) enhances recruitment while preserving representation of research subjects for the study population in our study setting. METHODS An ongoing community-based prospective adult cohort study was used to randomize 600 subjects who were eligible by age and residential address to TESRS (n = 300) and standard mailing method (n = 300), respectively, for 3 months. Then, TESRS was initiated and included automatic identification of patients' preference for being contacted (online patient portal vs postal mail) from EHRs and automatic sending out of invitation letters followed by completion of a short online survey for checking eligibility and the digital consent process if eligible. We compared (1) median time to consent from invitation sent out per subject and total subjects recruited after a 3-month recruitment period, (2) the estimated study staff's time, and (3) representation of sociodemographic characteristics (e.g., age, sex, race, SES measured by HOUSES index, and rural residence) between subjects recruited via TESRS and those via traditional mailing methods. RESULTS Median age of randomized subjects (n = 600) was 63 years with 52.0% female and 89.2% non-Hispanic White. Over a 3-month period, results showed consent rate via TESRS was 13% (39/297) similar to 11% (31/295) via standard mailing. However, recruitment was significantly faster with the TESRS approach (median 7 vs 26 days) given the study staff's effort. Study staff's time saved by using TESRS compared to standard mailing approach was estimated at 40 min per subject (equivalent to 200 h for 300 subjects). No significant differences in characteristics of research subjects from the study population were found. CONCLUSION Our study demonstrated the utility of TESRS as a subject recruitment digital technology which significantly enhanced the recruitment effort while reducing the study staff burden of recruitment while maintaining the consistency of characteristics of recruited subjects. The strategy and support for implementing and testing TESRS in other study settings should be considered.
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Affiliation(s)
- Chung-Il Wi
- Department of Pediatric and Adolescent Medicine, Mayo Clinic, Rochester, MN, USA
- Precision Population Science Lab, Mayo Clinic, Rochester, MN, USA
| | - Katherine S. King
- Division of Clinical Trials and Biostatistics, Mayo Clinic, Rochester, MN, USA
| | - Euijung Ryu
- Precision Population Science Lab, Mayo Clinic, Rochester, MN, USA
- Division of Computational Biology, Mayo Clinic, Rochester, MN, USA
| | - Traci L. Natoli
- Department of Pediatric and Adolescent Medicine, Mayo Clinic, Rochester, MN, USA
- Precision Population Science Lab, Mayo Clinic, Rochester, MN, USA
| | - Ryan P. Miller
- Department of Information Technology, Mayo Clinic, Phoenix, AZ, USA
| | - Matthew J. Spiten
- Department of Pediatric and Adolescent Medicine, Mayo Clinic, Rochester, MN, USA
- Precision Population Science Lab, Mayo Clinic, Rochester, MN, USA
| | - Bijan J. Borah
- Department of Health Services Research, Mayo Clinic, Rochester, MN, USA
| | - Paul Y. Takahashi
- Division of Community Internal Medicine, Mayo Clinic, Rochester, MN, USA
| | - Xiaoxi Yao
- Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, USA
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | | | - Robert J. Pignolo
- Department of Medicine, Divisions of Hospital Internal Medicine, Endocrinology, and Geriatric Medicine and Gerontology, Mayo Clinic, Rochester, MN, USA
| | - Young J. Juhn
- Department of Pediatric and Adolescent Medicine, Mayo Clinic, Rochester, MN, USA
- Precision Population Science Lab, Mayo Clinic, Rochester, MN, USA
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Bogin MH, Chandra A, Manggaard J, Thorsteinsdottir B, Hanson GJ, Takahashi PY. Telehealth Use and Hospital Readmission Rates in Long-term Care Facilities in Southeastern Minnesota During the COVID-19 Pandemic. Mayo Clin Proc Innov Qual Outcomes 2022; 6:186-192. [PMID: 35281694 PMCID: PMC8904139 DOI: 10.1016/j.mayocpiqo.2022.03.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022] Open
Abstract
Objective To determine whether the length of a telehealth visit predicted the risk of hospital readmission at 30 days in skilled nursing facilities (SNFs) in southeastern Minnesota during the coronavirus disease 2019 pandemic. Patients and Methods This was a retrospective cohort study conducted in SNFs located in southeastern Minnesota from March 1, 2020 through July 15, 2020. The primary outcomes included hospitalization within 30 days of a video visit, and the secondary outcome was the number of provider video visits during the stay at an SNF. The primary predictor was the duration of video visits, and we collected the data regarding other known predictors of hospitalization. We used the χ2 test for categorical variables and multivariate conditional logistic regression. Results We included 722 patients (mean age, 82.8 years [SD, 10.8 years]). Of those, 76 SNF residents (10.5%) were rehospitalized within 30 days. The average length of a video visit was 34.0 minutes (SD, 22.7 minutes) in admitted residents compared with 30.0 minutes (SD, 15.9 minutes) in nonadmitted residents. After full adjustment, there was no difference in the video visit duration between admitted and nonadmitted residents (odds ratio, 1.01; 95% CI, 0.99-1.03). The number of subsequent provider video visits was 2.26 (SD, 1.9) in admitted residents vs 1.58 (SD, 1.6), which was significant after adjustment (odds ratio, 1.17; 95% CI, 1.02-1.34). Conclusion There was no difference in the length of video visits for hospitalized SNF residents vs those who were not hospitalized within 30 days of a video visit. There were more visits in residents with hospital readmission. This may reflect the acuity of care for patients requiring a hospital stay. More research is needed to determine the ideal use of telehealth during the coronavirus disease 2019 pandemic in the postacute and long-term care environment.
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Affiliation(s)
| | - Anupam Chandra
- Department of Internal Medicine, Division of Community Internal Medicine, Mayo Clinic, Rochester, MN
| | - Jennifer Manggaard
- Department of Internal Medicine, Division of Community Internal Medicine, Mayo Clinic, Rochester, MN
| | - Bjoerg Thorsteinsdottir
- Department of Internal Medicine, Division of Community Internal Medicine, Mayo Clinic, Rochester, MN
| | - Gregory J Hanson
- Department of Internal Medicine, Division of Community Internal Medicine, Mayo Clinic, Rochester, MN
| | - Paul Y Takahashi
- Department of Internal Medicine, Division of Community Internal Medicine, Mayo Clinic, Rochester, MN
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17
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Flynn MJ, Kronebusch BJ, Sikkink LA, Swanson KM, Niccum KJ, Crane SJ, Aoun B, Takahashi PY. Impact of the Registered Nurse Clinical Liaison Role in Ambulatory Care on Transitions of Care: A Retrospective Cohort Study. Prof Case Manag 2022; 27:58-66. [PMID: 35099419 DOI: 10.1097/ncm.0000000000000538] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
PURPOSE OF STUDY To determine the relationship between engagement with the novel register nurse care liaison (RNCL) and enrollment in care management compared with usual care in hospitalized patients. PRIMARY PRACTICE SETTING Patients in the hospital from January 1, 2019, to September 30, 2019, who would be eligible for care management. METHODOLOGY AND SAMPLE This was a retrospective cohort study. The authors compared a group of 419 patients who utilized the services of the RNCL at any time during their hospital stay with the RNCL to a propensity matched control group of 833 patients, which consisted of patients who were hospitalized during the same time as the RNCL intervention group. Our primary outcome was enrollment in care management programs. Our secondary outcome was 30-day readmissions, emergency department (ED) use, and office visits. The authors compared baseline characteristics and outcomes across groups using Wilcoxon-Mann-Whitney and χ2 tests and performed an adjusted analysis using conditional logistic regression models controlling for patient education and previous health care utilization. RESULTS The authors matched 419 patients who had engaged an RNCL to 833 patients in the usual care group; this comprised the analytic cohort for this study. The authors found 67.1% of patients enrolled in a care management program with RNCL compared with only 15.3% in usual care (p < .0001). The authors found higher rates of enrollment in all programs of care management. After the full adjustment, the odds ratio for enrollment in any program was 13.7 (95% confidence interval: 9.3, 20.2) for RNCL compared with usual care. There was no difference between groups with 30-day hospitalization or ED visit. CONCLUSION In this matched study of 419 patients with RNCL engagement, the authors found significantly higher enrollment in all care management programs. IMPLICATIONS FOR CASE MANAGEMENT PRACTICE These findings encourage further study of this care model. This could help enhance enrollment in care management programs, increase relationships between inpatient practice and ambulatory practice, as well as increase communication across the continuum of care.
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Affiliation(s)
- Mollie J Flynn
- Mollie J. Flynn, BSN, RN , is a bachelor's prepared registered nurse at Mayo Clinic in Rochester. She serves as the sole nurse clinical liaison for the Mayo Clinic Rochester/Kasson primary care clinic in Rochester, Minnesota
- Beckie J. Kronebusch, MS, APRN, CNS , is a master's prepared clinical nurse specialist. She currently works at Mayo Clinic Health System in La Crosse, Wisconsin
- Laura A. Sikkink, MSN, RN , is a master's prepared registered nurse. She is an ambulatory nurse manager, who manages the care coordination groups at the Mayo Clinic Rochester/Kasson primary care clinic in Rochester, Minnesota
- Kristi M. Swanson, MS , is a master's prepared principal health services analyst. She currently works for Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery at Mayo Clinic in Rochester, Minnesota
- Kelly J. Niccum, CCRP, PMP , is a project manager for the Mayo Clinic Robert D. and Patricia E. Kern Center for the Science of Healthcare Delivery in Rochester, Minnesota
- Sarah J. Crane, MD , is a consultant in Community Internal Medicine at the Mayo Clinic Rochester/Kasson primary care clinic
- Bernard Aoun, MD , is a consultant in Community Internal Medicine at the Mayo Clinic Rochester/Kasson primary care clinic. He is an instructor of medicine and is board certified in Geriatrics
- Paul Y. Takahashi, MD , is a consultant and section head in Community Internal Medicine at the Mayo Clinic Rochester/Kasson primary care clinic
| | - Beckie J Kronebusch
- Mollie J. Flynn, BSN, RN , is a bachelor's prepared registered nurse at Mayo Clinic in Rochester. She serves as the sole nurse clinical liaison for the Mayo Clinic Rochester/Kasson primary care clinic in Rochester, Minnesota
- Beckie J. Kronebusch, MS, APRN, CNS , is a master's prepared clinical nurse specialist. She currently works at Mayo Clinic Health System in La Crosse, Wisconsin
- Laura A. Sikkink, MSN, RN , is a master's prepared registered nurse. She is an ambulatory nurse manager, who manages the care coordination groups at the Mayo Clinic Rochester/Kasson primary care clinic in Rochester, Minnesota
- Kristi M. Swanson, MS , is a master's prepared principal health services analyst. She currently works for Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery at Mayo Clinic in Rochester, Minnesota
- Kelly J. Niccum, CCRP, PMP , is a project manager for the Mayo Clinic Robert D. and Patricia E. Kern Center for the Science of Healthcare Delivery in Rochester, Minnesota
- Sarah J. Crane, MD , is a consultant in Community Internal Medicine at the Mayo Clinic Rochester/Kasson primary care clinic
- Bernard Aoun, MD , is a consultant in Community Internal Medicine at the Mayo Clinic Rochester/Kasson primary care clinic. He is an instructor of medicine and is board certified in Geriatrics
- Paul Y. Takahashi, MD , is a consultant and section head in Community Internal Medicine at the Mayo Clinic Rochester/Kasson primary care clinic
| | - Laura A Sikkink
- Mollie J. Flynn, BSN, RN , is a bachelor's prepared registered nurse at Mayo Clinic in Rochester. She serves as the sole nurse clinical liaison for the Mayo Clinic Rochester/Kasson primary care clinic in Rochester, Minnesota
- Beckie J. Kronebusch, MS, APRN, CNS , is a master's prepared clinical nurse specialist. She currently works at Mayo Clinic Health System in La Crosse, Wisconsin
- Laura A. Sikkink, MSN, RN , is a master's prepared registered nurse. She is an ambulatory nurse manager, who manages the care coordination groups at the Mayo Clinic Rochester/Kasson primary care clinic in Rochester, Minnesota
- Kristi M. Swanson, MS , is a master's prepared principal health services analyst. She currently works for Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery at Mayo Clinic in Rochester, Minnesota
- Kelly J. Niccum, CCRP, PMP , is a project manager for the Mayo Clinic Robert D. and Patricia E. Kern Center for the Science of Healthcare Delivery in Rochester, Minnesota
- Sarah J. Crane, MD , is a consultant in Community Internal Medicine at the Mayo Clinic Rochester/Kasson primary care clinic
- Bernard Aoun, MD , is a consultant in Community Internal Medicine at the Mayo Clinic Rochester/Kasson primary care clinic. He is an instructor of medicine and is board certified in Geriatrics
- Paul Y. Takahashi, MD , is a consultant and section head in Community Internal Medicine at the Mayo Clinic Rochester/Kasson primary care clinic
| | - Kristi M Swanson
- Mollie J. Flynn, BSN, RN , is a bachelor's prepared registered nurse at Mayo Clinic in Rochester. She serves as the sole nurse clinical liaison for the Mayo Clinic Rochester/Kasson primary care clinic in Rochester, Minnesota
- Beckie J. Kronebusch, MS, APRN, CNS , is a master's prepared clinical nurse specialist. She currently works at Mayo Clinic Health System in La Crosse, Wisconsin
- Laura A. Sikkink, MSN, RN , is a master's prepared registered nurse. She is an ambulatory nurse manager, who manages the care coordination groups at the Mayo Clinic Rochester/Kasson primary care clinic in Rochester, Minnesota
- Kristi M. Swanson, MS , is a master's prepared principal health services analyst. She currently works for Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery at Mayo Clinic in Rochester, Minnesota
- Kelly J. Niccum, CCRP, PMP , is a project manager for the Mayo Clinic Robert D. and Patricia E. Kern Center for the Science of Healthcare Delivery in Rochester, Minnesota
- Sarah J. Crane, MD , is a consultant in Community Internal Medicine at the Mayo Clinic Rochester/Kasson primary care clinic
- Bernard Aoun, MD , is a consultant in Community Internal Medicine at the Mayo Clinic Rochester/Kasson primary care clinic. He is an instructor of medicine and is board certified in Geriatrics
- Paul Y. Takahashi, MD , is a consultant and section head in Community Internal Medicine at the Mayo Clinic Rochester/Kasson primary care clinic
| | - Kelly J Niccum
- Mollie J. Flynn, BSN, RN , is a bachelor's prepared registered nurse at Mayo Clinic in Rochester. She serves as the sole nurse clinical liaison for the Mayo Clinic Rochester/Kasson primary care clinic in Rochester, Minnesota
- Beckie J. Kronebusch, MS, APRN, CNS , is a master's prepared clinical nurse specialist. She currently works at Mayo Clinic Health System in La Crosse, Wisconsin
- Laura A. Sikkink, MSN, RN , is a master's prepared registered nurse. She is an ambulatory nurse manager, who manages the care coordination groups at the Mayo Clinic Rochester/Kasson primary care clinic in Rochester, Minnesota
- Kristi M. Swanson, MS , is a master's prepared principal health services analyst. She currently works for Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery at Mayo Clinic in Rochester, Minnesota
- Kelly J. Niccum, CCRP, PMP , is a project manager for the Mayo Clinic Robert D. and Patricia E. Kern Center for the Science of Healthcare Delivery in Rochester, Minnesota
- Sarah J. Crane, MD , is a consultant in Community Internal Medicine at the Mayo Clinic Rochester/Kasson primary care clinic
- Bernard Aoun, MD , is a consultant in Community Internal Medicine at the Mayo Clinic Rochester/Kasson primary care clinic. He is an instructor of medicine and is board certified in Geriatrics
- Paul Y. Takahashi, MD , is a consultant and section head in Community Internal Medicine at the Mayo Clinic Rochester/Kasson primary care clinic
| | - Sarah J Crane
- Mollie J. Flynn, BSN, RN , is a bachelor's prepared registered nurse at Mayo Clinic in Rochester. She serves as the sole nurse clinical liaison for the Mayo Clinic Rochester/Kasson primary care clinic in Rochester, Minnesota
- Beckie J. Kronebusch, MS, APRN, CNS , is a master's prepared clinical nurse specialist. She currently works at Mayo Clinic Health System in La Crosse, Wisconsin
- Laura A. Sikkink, MSN, RN , is a master's prepared registered nurse. She is an ambulatory nurse manager, who manages the care coordination groups at the Mayo Clinic Rochester/Kasson primary care clinic in Rochester, Minnesota
- Kristi M. Swanson, MS , is a master's prepared principal health services analyst. She currently works for Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery at Mayo Clinic in Rochester, Minnesota
- Kelly J. Niccum, CCRP, PMP , is a project manager for the Mayo Clinic Robert D. and Patricia E. Kern Center for the Science of Healthcare Delivery in Rochester, Minnesota
- Sarah J. Crane, MD , is a consultant in Community Internal Medicine at the Mayo Clinic Rochester/Kasson primary care clinic
- Bernard Aoun, MD , is a consultant in Community Internal Medicine at the Mayo Clinic Rochester/Kasson primary care clinic. He is an instructor of medicine and is board certified in Geriatrics
- Paul Y. Takahashi, MD , is a consultant and section head in Community Internal Medicine at the Mayo Clinic Rochester/Kasson primary care clinic
| | - Bernard Aoun
- Mollie J. Flynn, BSN, RN , is a bachelor's prepared registered nurse at Mayo Clinic in Rochester. She serves as the sole nurse clinical liaison for the Mayo Clinic Rochester/Kasson primary care clinic in Rochester, Minnesota
- Beckie J. Kronebusch, MS, APRN, CNS , is a master's prepared clinical nurse specialist. She currently works at Mayo Clinic Health System in La Crosse, Wisconsin
- Laura A. Sikkink, MSN, RN , is a master's prepared registered nurse. She is an ambulatory nurse manager, who manages the care coordination groups at the Mayo Clinic Rochester/Kasson primary care clinic in Rochester, Minnesota
- Kristi M. Swanson, MS , is a master's prepared principal health services analyst. She currently works for Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery at Mayo Clinic in Rochester, Minnesota
- Kelly J. Niccum, CCRP, PMP , is a project manager for the Mayo Clinic Robert D. and Patricia E. Kern Center for the Science of Healthcare Delivery in Rochester, Minnesota
- Sarah J. Crane, MD , is a consultant in Community Internal Medicine at the Mayo Clinic Rochester/Kasson primary care clinic
- Bernard Aoun, MD , is a consultant in Community Internal Medicine at the Mayo Clinic Rochester/Kasson primary care clinic. He is an instructor of medicine and is board certified in Geriatrics
- Paul Y. Takahashi, MD , is a consultant and section head in Community Internal Medicine at the Mayo Clinic Rochester/Kasson primary care clinic
| | - Paul Y Takahashi
- Mollie J. Flynn, BSN, RN , is a bachelor's prepared registered nurse at Mayo Clinic in Rochester. She serves as the sole nurse clinical liaison for the Mayo Clinic Rochester/Kasson primary care clinic in Rochester, Minnesota
- Beckie J. Kronebusch, MS, APRN, CNS , is a master's prepared clinical nurse specialist. She currently works at Mayo Clinic Health System in La Crosse, Wisconsin
- Laura A. Sikkink, MSN, RN , is a master's prepared registered nurse. She is an ambulatory nurse manager, who manages the care coordination groups at the Mayo Clinic Rochester/Kasson primary care clinic in Rochester, Minnesota
- Kristi M. Swanson, MS , is a master's prepared principal health services analyst. She currently works for Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery at Mayo Clinic in Rochester, Minnesota
- Kelly J. Niccum, CCRP, PMP , is a project manager for the Mayo Clinic Robert D. and Patricia E. Kern Center for the Science of Healthcare Delivery in Rochester, Minnesota
- Sarah J. Crane, MD , is a consultant in Community Internal Medicine at the Mayo Clinic Rochester/Kasson primary care clinic
- Bernard Aoun, MD , is a consultant in Community Internal Medicine at the Mayo Clinic Rochester/Kasson primary care clinic. He is an instructor of medicine and is board certified in Geriatrics
- Paul Y. Takahashi, MD , is a consultant and section head in Community Internal Medicine at the Mayo Clinic Rochester/Kasson primary care clinic
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Chandra A, Takahashi PY, McCoy RG, Thorsteinsdottir B, Hanson GJ, Chaudhry R, Rahman PA, Storlie CB, Murphree DH. Risk Prediction Model for 6-Month Mortality for Patients Discharged to Skilled Nursing Facilities. J Am Med Dir Assoc 2022; 23:1403-1408. [PMID: 35227666 DOI: 10.1016/j.jamda.2022.01.069] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Revised: 01/19/2022] [Accepted: 01/23/2022] [Indexed: 11/24/2022]
Abstract
OBJECTIVE Hospitalized patients discharged to skilled nursing facilities (SNFs) for post-acute care are at high risk for adverse outcomes. Yet, absence of effective prognostic tools hinders optimal care planning and decision making. Our objective was to develop and validate a risk prediction model for 6-month all-cause death among hospitalized patients discharged to SNFs. DESIGN Retrospective cohort study. SETTING AND PARTICIPANTS Patients discharged from 1 of 2 hospitals to 1 of 10 SNFs for post-acute care in an integrated health care delivery system between January 1, 2009, and December 31, 2016. METHODS Gradient-boosting machine modeling was used to predict all-cause death within 180 days of hospital discharge with use of patient demographic characteristics, comorbidities, pattern of prior health care use, and clinical parameters from the index hospitalization. Area under the receiver operating characteristic curve (AUC) was assessed for out-of-sample observations under 10-fold cross-validation. RESULTS We identified 9803 unique patients with 11,647 hospital-to-SNF discharges [mean (SD) age, 80.72 (9.71) years; female sex, 61.4%]. These discharges involved 9803 patients alive at 180 days and 1844 patients who died between day 1 and day 180 of discharge. Age, comorbid burden, health care use in prior 6 months, abnormal laboratory parameters, and mobility status during hospital stay were the most important predictors of 6-month death (model AUC, 0.82). CONCLUSION AND IMPLICATIONS We derived a robust prediction model with parameters available at discharge to SNFs to calculate risk of death within 6 months. This work may be useful to guide other clinicians wishing to develop mortality prediction instruments specific to their post-acute SNF populations.
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Affiliation(s)
- Anupam Chandra
- Division of Community Internal Medicine, Mayo Clinic, Rochester, MN, USA; Division of Geriatric Medicine and Gerontology, Mayo Clinic, Rochester, MN, USA.
| | - Paul Y Takahashi
- Division of Community Internal Medicine, Mayo Clinic, Rochester, MN, USA; Division of Geriatric Medicine and Gerontology, Mayo Clinic, Rochester, MN, USA
| | - Rozalina G McCoy
- Division of Community Internal Medicine, Mayo Clinic, Rochester, MN, USA; Division of Geriatric Medicine and Gerontology, Mayo Clinic, Rochester, MN, USA; Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, USA
| | | | - Gregory J Hanson
- Division of Community Internal Medicine, Mayo Clinic, Rochester, MN, USA; Division of Geriatric Medicine and Gerontology, Mayo Clinic, Rochester, MN, USA
| | - Rajeev Chaudhry
- Division of Community Internal Medicine, Mayo Clinic, Rochester, MN, USA; Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN, USA
| | - Parvez A Rahman
- Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, USA
| | - Curtis B Storlie
- Division of Clinical Trials and Biostatistics, Mayo Clinic, Rochester, MN, USA
| | - Dennis H Murphree
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN, USA
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Lopes GS, Manemann SM, Weston SA, Jiang R, Larson NB, Moser ED, Roger VL, Takahashi PY, Sandoval Y, Bell MR, Chamberlain AM, Brewer LC, Singh M, St Sauver JL, Bielinski SJ. Minnesota COVID-19 Lockdowns - The Effect on Acute Myocardial Infarctions and Revascularizations in the Community. Mayo Clin Proc Innov Qual Outcomes 2021; 6:77-85. [PMID: 34926992 PMCID: PMC8666289 DOI: 10.1016/j.mayocpiqo.2021.12.002] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Objective To study associations between the Minnesota coronavirus disease 2019 (COVID-19) mitigation strategies on incidence rates of acute myocardial infarction (MI) or revascularization among residents of Southeast Minnesota. Methods Using the Rochester Epidemiology Project, all adult residents of a nine-county region of Southeast Minnesota who had an incident MI or revascularization between January 1, 2015, and December 31, 2020, were identified. Events were defined as primary in-patient diagnosis of MI or undergoing revascularization. We estimated age- and sex-standardized incidence rates and incidence rate ratios (IRRs) stratified by key factors, comparing 2020 to 2015–2019. We also calculated IRRs by periods corresponding to Minnesota’s COVID-19 mitigation timeline: “Pre-lockdown” (January 1–March 11, 2020), “First lockdown” (March 12–May 31, 2020), “Between lockdowns” (June 1–November 20, 2020), and “Second lockdown” (November 21–December 31, 2020). Results The incidence rate in 2020 was 32% lower than in 2015–2019 (24 vs 36 events/100,000 person-months; IRR, 0.68; 95% CI, 0.62-0.74). Incidence rates were lower in 2020 versus 2015–2019 during the first lockdown (IRR, 0.54; 95% CI, 0.44-0.66), in between lockdowns (IRR, 0.70; 95% CI, 0.61-0.79), and during the second lockdown (IRR, 0.54; 95% CI, 0.41-0.72). April had the lowest IRR (IRR 0.48; 95% CI, 0.34-0.68), followed by August (IRR, 0.55; 95% CI, 0.40-0.76) and December (IRR, 0.56; 95% CI, 0.41-0.77). Similar declines were observed across sex and all age groups, and in both urban and rural residents. Conclusion Mitigation measures for COVID-19 were associated with a reduction in hospitalizations for acute MI and revascularization in Southeast Minnesota. The reduction was most pronounced during the lockdown periods but persisted between lockdowns.
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Affiliation(s)
- Guilherme S Lopes
- Division of Clinical Trials and Biostatistics, Department of Quantitative Health Sciences Mayo Clinic, Rochester, MN
| | - Sheila M Manemann
- Division of Epidemiology, Department of Quantitative Health Sciences Mayo Clinic, Rochester, MN
| | - Susan A Weston
- Division of Clinical Trials and Biostatistics, Department of Quantitative Health Sciences Mayo Clinic, Rochester, MN
| | - Ruoxiang Jiang
- Division of Clinical Trials and Biostatistics, Department of Quantitative Health Sciences Mayo Clinic, Rochester, MN
| | - Nicholas B Larson
- Division of Clinical Trials and Biostatistics, Department of Quantitative Health Sciences Mayo Clinic, Rochester, MN
| | - Ethan D Moser
- Division of Epidemiology, Department of Quantitative Health Sciences Mayo Clinic, Rochester, MN
| | | | - Paul Y Takahashi
- Division of Community Internal Medicine, Department of Medicine Mayo Clinic, Rochester, MN
| | - Yader Sandoval
- Department of Cardiovascular Medicine Mayo Clinic, Rochester, MN
| | - Malcolm R Bell
- Department of Cardiovascular Medicine Mayo Clinic, Rochester, MN
| | - Alanna M Chamberlain
- Division of Epidemiology, Department of Quantitative Health Sciences Mayo Clinic, Rochester, MN
| | | | - Mandeep Singh
- Department of Cardiovascular Medicine Mayo Clinic, Rochester, MN
| | - Jennifer L St Sauver
- Division of Epidemiology, Department of Quantitative Health Sciences Mayo Clinic, Rochester, MN
| | - Suzette J Bielinski
- Division of Epidemiology, Department of Quantitative Health Sciences Mayo Clinic, Rochester, MN
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Bell EJ, Bielinski SJ, St Sauver JL, Chen LY, Rooney MR, Larson NB, Takahashi PY, Folsom AR. Association of Proton Pump Inhibitors With Higher Risk of Cardiovascular Disease and Heart Failure. Mayo Clin Proc 2021; 96:2540-2549. [PMID: 34607633 PMCID: PMC8631442 DOI: 10.1016/j.mayocp.2021.02.025] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 02/17/2021] [Accepted: 02/23/2021] [Indexed: 01/03/2023]
Abstract
OBJECTIVE To examine associations of cumulative exposure to proton pump inhibitors (PPIs) with total cardiovascular disease (CVD; composed of stroke, coronary heart disease, and heart failure [HF]) and HF alone in a cohort study of White and African American participants of the Atherosclerosis Risk in Communities (ARIC) study. METHODS Use of PPIs was assessed by pill bottle inspection at visit 1 (January 1, 1987 to 1989) and up to 10 additional times before baseline (visit 5; 2011 to 2013). We calculated cumulative exposure to PPIs as days of use from visit 1 to baseline. Participants (n=4346 free of total CVD at visit 5; mean age, 75 years) were observed for incident total CVD and HF events through December 31, 2016. We used Cox regression to measure associations of PPIs with total CVD and HF. RESULTS After adjustment for potential confounding variables, participants with a cumulative exposure to PPIs of more than 5.1 years had a 2.02-fold higher risk of total CVD (95% CI, 1.50 to 2.72) and a 2.21-fold higher risk of HF (95% CI, 1.51 to 3.23) than nonusers. CONCLUSION Long-term PPI use was associated with twice the risk of total CVD and HF compared with nonusers. Our findings are in concordance with other research and suggest another reason to be cautious of PPI overuse.
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Affiliation(s)
| | - Suzette J Bielinski
- Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, MN
| | - Jennifer L St Sauver
- Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, MN
| | - Lin Y Chen
- Cardiovascular Division, University of Minnesota Medical School, Minneapolis
| | - Mary R Rooney
- Department of Epidemiology, Johns Hopkins University, Baltimore, MD
| | - Nicholas B Larson
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN
| | - Paul Y Takahashi
- Division of Community Internal Medicine, Department of Internal Medicine, Mayo Clinic, Rochester, MN
| | - Aaron R Folsom
- Division of Epidemiology and Community Health, University of Minnesota's School of Public Health, Minneapolis
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21
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Manemann SM, St Sauver JL, Liu H, Larson NB, Moon S, Takahashi PY, Olson JE, Rocca WA, Miller VM, Therneau TM, Ngufor CG, Roger VL, Zhao Y, Decker PA, Killian JM, Bielinski SJ. Longitudinal cohorts for harnessing the electronic health record for disease prediction in a US population. BMJ Open 2021; 11:e044353. [PMID: 34103314 PMCID: PMC8190051 DOI: 10.1136/bmjopen-2020-044353] [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] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
PURPOSE The depth and breadth of clinical data within electronic health record (EHR) systems paired with innovative machine learning methods can be leveraged to identify novel risk factors for complex diseases. However, analysing the EHR is challenging due to complexity and quality of the data. Therefore, we developed large electronic population-based cohorts with comprehensive harmonised and processed EHR data. PARTICIPANTS All individuals 30 years of age or older who resided in Olmsted County, Minnesota on 1 January 2006 were identified for the discovery cohort. Algorithms to define a variety of patient characteristics were developed and validated, thus building a comprehensive risk profile for each patient. Patients are followed for incident diseases and ageing-related outcomes. Using the same methods, an independent validation cohort was assembled by identifying all individuals 30 years of age or older who resided in the largely rural 26-county area of southern Minnesota and western Wisconsin on 1 January 2013. FINDINGS TO DATE For the discovery cohort, 76 255 individuals (median age 49; 53% women) were identified from which a total of 9 644 221 laboratory results; 9 513 840 diagnosis codes; 10 924 291 procedure codes; 1 277 231 outpatient drug prescriptions; 966 136 heart rate measurements and 1 159 836 blood pressure (BP) measurements were retrieved during the baseline time period. The most prevalent conditions in this cohort were hyperlipidaemia, hypertension and arthritis. For the validation cohort, 333 460 individuals (median age 54; 52% women) were identified and to date, a total of 19 926 750 diagnosis codes, 10 527 444 heart rate measurements and 7 356 344 BP measurements were retrieved during baseline. FUTURE PLANS Using advanced machine learning approaches, these electronic cohorts will be used to identify novel sex-specific risk factors for complex diseases. These approaches will allow us to address several challenges with the use of EHR.
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Affiliation(s)
- Sheila M Manemann
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA
| | - Jennifer L St Sauver
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA
| | - Hongfang Liu
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, Minnesota, USA
| | - Nicholas B Larson
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA
| | - Sungrim Moon
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, Minnesota, USA
| | - Paul Y Takahashi
- Division of Community Internal Medicine, Department of Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Janet E Olson
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA
| | - Walter A Rocca
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA, Mayo Clinic, Rochester, Minnesota, USA
- Mayo Clinic Women's Health Research Center, Mayo Clinic, Rochester, Minnesota, USA
| | - Virginia M Miller
- Mayo Clinic Women's Health Research Center, Mayo Clinic, Rochester, Minnesota, USA
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, Minnesota, USA
- Department of Surgery, Mayo Clinic, Rochester, Minnesota, USA
- Mayo Clinic Specialized Center of Research Excellence, Mayo Clinic Rochester, Minnesota, USA, Mayo Clinic, Rochester, Minnesota, USA
| | - Terry M Therneau
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA
| | - Che G Ngufor
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, Minnesota, USA
| | - Veronique L Roger
- Division of Cardiovascular Diseases, Department of Internal Medicine, Mayo Clinic, Rochester, Minnesota, USA
- Epidemiology and Community Health Branch National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Yiqing Zhao
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA
| | - Paul A Decker
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA
| | - Jill M Killian
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA
| | - Suzette J Bielinski
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA
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22
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Takahashi PY, Chandra A, McCoy RG, Borkenhagen LS, Larson ME, Thorsteinsdottir B, Hickman JA, Swanson KM, Hanson GJ, Naessens JM. Outcomes of a Nursing Home-to-Community Care Transition Program. J Am Med Dir Assoc 2021; 22:2440-2446.e2. [PMID: 33984293 DOI: 10.1016/j.jamda.2021.04.010] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Revised: 04/08/2021] [Accepted: 04/08/2021] [Indexed: 11/29/2022]
Abstract
OBJECTIVES Most transitional care initiatives to reduce rehospitalization have focused on the transition that occurs between a patient's hospital discharge and return home. However, many patients are discharged from a skilled nursing facility (SNF) to their homes. The goal was to evaluate the effectiveness of the Mayo Clinic Care Transitions (MCCT) program (hereafter called program) among patients discharged from SNFs to their homes. DESIGN Propensity-matched control-intervention trial. INTERVENTION Patients in the intervention group received care management following nursing stay (a home visit and nursing phone calls). SETTING AND PARTICIPANTS Patients enrolled after discharge from an SNF to home were matched to patients who did not receive intervention because of refusal, program capacity, or distance. Patients were aged ≥60 years, at high risk for hospitalization, and discharged from an SNF. METHODS Program enrollees were matched through propensity score to nonenrollees on the basis of age, sex, comorbid health burden, and mortality risk score. Conditional logistic regression analysis examined 30-day hospitalization and emergency department (ED) use; Cox proportional hazards analyses examined 180-day hospital stay and ED use. RESULTS Each group comprised 160 patients [mean (standard deviation) age, 85.4 (7.4) years]. Thirty-day hospitalization and ED rates were 4.4% and 10.0% in the program group and 3.8% and 10.0% in the group with usual care (P = .76 for hospitalization; P > .99 for ED). At 180 days, hospitalization and ED rates were 30.6% and 46.3% for program patients compared with 11.3% and 25.0% in the comparison group (P < .001). CONCLUSIONS AND IMPLICATIONS We found no evidence of reduced hospitalization or ED visits by program patients vs the comparison group. Such findings are crucial because they illustrate how aggressive stabilization care within the SNF may mitigate the program role. Furthermore, we found higher ED and hospitalization rates at 180 days in program patients than the comparison group.
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Affiliation(s)
- Paul Y Takahashi
- Division of Community Internal Medicine, Mayo Clinic, Rochester, MN, USA; Division of Geriatric Medicine and Gerontology, Mayo Clinic, Rochester, MN, USA.
| | - Anupam Chandra
- Division of Community Internal Medicine, Mayo Clinic, Rochester, MN, USA; Division of Geriatric Medicine and Gerontology, Mayo Clinic, Rochester, MN, USA
| | - Rozalina G McCoy
- Division of Community Internal Medicine, Mayo Clinic, Rochester, MN, USA; Division of Geriatric Medicine and Gerontology, Mayo Clinic, Rochester, MN, USA; Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Lynn S Borkenhagen
- Division of Community Internal Medicine, Mayo Clinic, Rochester, MN, USA
| | - Mary E Larson
- Employee and Community Health, Mayo Clinic, Rochester, MN, USA
| | | | - Joel A Hickman
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Kristi M Swanson
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Gregory J Hanson
- Division of Community Internal Medicine, Mayo Clinic, Rochester, MN, USA; Division of Geriatric Medicine and Gerontology, Mayo Clinic, Rochester, MN, USA; Division of Community Palliative Medicine, Mayo Clinic, Rochester, MN, USA
| | - James M Naessens
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA; Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, USA
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Takahashi PY, Ryu E, Cerhan JR, Bielinski SJ, Olson JE. Pathway to Ascertain the Role of Pharmacogenomics in Healthcare Utilization Outcomes [Response to Letter]. Pharmgenomics Pers Med 2021; 14:545-546. [PMID: 33986611 PMCID: PMC8111333 DOI: 10.2147/pgpm.s316851] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Accepted: 04/21/2021] [Indexed: 11/23/2022]
Affiliation(s)
- Paul Y Takahashi
- Division of Community Internal Medicine, Department of Internal Medicine, Mayo Clinic, Rochester, MN, USA
| | - Euijung Ryu
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - James R Cerhan
- Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Suzette J Bielinski
- Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Janet E Olson
- Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
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Liu PY, Takahashi PY, Yang RJ, Iranmanesh A, Veldhuis JD. Age and time-of-day differences in the hypothalamo-pituitary-testicular, and adrenal, response to total overnight sleep deprivation. Sleep 2021; 43:5717179. [PMID: 31993665 DOI: 10.1093/sleep/zsaa008] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2019] [Revised: 01/08/2020] [Indexed: 11/14/2022] Open
Abstract
STUDY OBJECTIVES In young men, sleep restriction decreases testosterone (Te) and increases afternoon cortisol (F), leading to anabolic-catabolic imbalance, insulin resistance, and other andrological health consequences. Age-related differences in the hypothalamo-pituitary-testicular/adrenal response to sleep restriction could expose older individuals to greater or lesser risk. We aimed to evaluate and compare the 24-h and time-of-day effect of sleep restriction on F, luteinizing hormone (LH), and Te in young and older men. METHODS Thirty-five healthy men, aged 18-30 (n = 17) and 60-80 (n =18) years, underwent overnight sleep deprivation (complete nighttime wakefulness) or nighttime sleep (10 pm to 6 am) with concurrent 10-min blood sampling in a prospectively randomized crossover study. F, LH, and Te secretion were calculated by deconvolution analysis. RESULTS Sleep deprivation had multiple effects on 24-h Te secretion with significant reductions in mean concentrations, basal, total and pulsatile secretion, and pulse frequency (each p < 0.05), in the absence of detectable changes in LH. These effects were most apparent in older men and differed according to age for some parameters: pulsatile Te secretion (p = 0.03) and Te pulse frequency (p = 0.02). Time-of-day analyses revealed that sleep restriction significantly reduced Te in the morning and afternoon, reduced LH in the morning in both age groups, and increased F in the afternoon in older men. CONCLUSIONS These data suggest a time-of-day dependent uncoupling of the regulatory control of the testicular axis and of F secretion. Future studies will need to directly verify these regulatory possibilities specifically and separately in young and older men. CLINICAL TRIAL Not applicable.
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Affiliation(s)
- Peter Y Liu
- Department of Medicine, Division of Endocrinology, Harbor-UCLA Medical Center and Los Angeles Biomedical Research Institute, David Geffen School of Medicine at UCLA, Los Angeles, CA
| | - Paul Y Takahashi
- Department of Primary Care Internal Medicine, Mayo Clinic, Rochester, MN
| | - Rebecca J Yang
- Endocrine Research Unit, Mayo School of Graduate Medical Education, Center for Translational Science Activities, Mayo Clinic, Rochester, MN
| | - Ali Iranmanesh
- Endocrine Service, Salem Veterans Affairs Medical Center, Salem, VA
| | - Johannes D Veldhuis
- Endocrine Research Unit, Mayo School of Graduate Medical Education, Center for Translational Science Activities, Mayo Clinic, Rochester, MN
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Juhn YJ, Wi CI, Ryu E, Sampathkumar P, Takahashi PY, Yao JD, Binnicker MJ, Natoli TL, Evans TK, King KS, Volpe S, Pirçon JY, Silvia Damaso, Pignolo RJ. Adherence to Public Health Measures Mitigates the Risk of COVID-19 Infection in Older Adults: A Community-Based Study. Mayo Clin Proc 2021; 96:912-920. [PMID: 33714601 PMCID: PMC7768210 DOI: 10.1016/j.mayocp.2020.12.016] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Accepted: 12/21/2020] [Indexed: 11/29/2022]
Abstract
OBJECTIVE To assess the prevalence and characteristics of coronavirus disease 2019 (COVID-19) cases during the reopening period in older adults, given that little is known about the prevalence of COVID-19 after the stay-at-home order was lifted in the United States, nor the actual effects of adherence to recommended public health measures (RPHM) on the risk of COVID-19. PATIENTS AND METHODS This was a cross-sectional study nested in a parent prospective cohort study, which followed a population-based sample of 2325 adults 50 years and older residing in southeast Minnesota to assess the incidence of viral infections. Participants were instructed to self-collect both nasal and oropharyngeal swabs, which were tested by reverse transcription polymerase chain reaction-based severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) assay between May 8, 2020, and June, 30, 2020. We assessed the prevalence of COVID-19 cases and characteristics of study subjects. RESULTS A total of 1505 eligible subjects participated in the study whose mean age was 68 years, with 885 (59%) women, 32 (2%) racial/ethnic minorities, and 906 (60%) with high-risk conditions for influenza. The prevalence of other Coronaviridae (human coronavirus [HCoV]-229E, HCoV-NL63, and HCoV-OC43) during the 2019 to 2020 flu season was 109 (7%), and none tested positive for SARS-CoV-2. Almost all participants reported adhering to the RPHM (1,488 [99%] for social distancing, 1,438 [96%] for wearing mask in a public space, 1,476 [98%] for hand hygiene, and 1,441 (96%) for staying home mostly). Eighty-six percent of participants resided in a single-family home. CONCLUSION We did not identify SARS-COV-2 infection in our study cohort. The combination of participants' behavior in following the RPHM and their living environment may considerably mitigate the risk of COVID-19.
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Affiliation(s)
- Young J Juhn
- Department of Pediatric and Internal Medicine, Mayo Clinic, Rochester, MN.
| | - Chung-Il Wi
- Department of Pediatric and Adolescent Medicine, Mayo Clinic, Rochester, MN
| | - Euijung Ryu
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN
| | | | - Paul Y Takahashi
- Division of Community Internal Medicine, Mayo Clinic, Rochester, MN
| | - Joseph D Yao
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN
| | | | - Traci L Natoli
- Department of Medicine Research, Mayo Clinic, Rochester, MN
| | - Tamara K Evans
- Department of Medicine Research, Mayo Clinic, Rochester, MN
| | - Katherine S King
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN
| | | | | | | | - Robert J Pignolo
- Division of Geriatric Medicine and Gerontology, Mayo Clinic, Rochester, MN.
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26
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Takahashi PY, Ryu E, Bielinski SJ, Hathcock M, Jenkins GD, Cerhan JR, Olson JE. No Association Between Pharmacogenomics Variants and Hospital and Emergency Department Utilization: A Mayo Clinic Biobank Retrospective Study. Pharmgenomics Pers Med 2021; 14:229-237. [PMID: 33603442 PMCID: PMC7886254 DOI: 10.2147/pgpm.s281645] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 09/12/2020] [Accepted: 12/29/2020] [Indexed: 02/06/2023]
Abstract
Background The use of pharmacogenomics data is increasing in clinical practice. However, it is unknown if pharmacogenomics data can be used more broadly to predict outcomes like hospitalization or emergency department (ED) visit. We aim to determine the association between selected pharmacogenomics phenotypes and hospital utilization outcomes (hospitalization and ED visits). Methods This cohort study utilized 10,078 patients from the Mayo Clinic Biobank in the RIGHT protocol with sequence and interpreted phenotypes for 10 selected pharmacogenes including CYP2D6, CYP2C9, CYP2C19, CYP3A5, HLA B 5701, HLA B 5702, HLA B 5801, TPMT, SLCO1B1, and DPYD. The primary outcome was hospitalization with ED visits as a secondary outcome. We used Cox proportional hazards model to test the association between each pharmacogenomics phenotype and the risk of the outcomes. Results During the follow-up period (median [in years] = 7.3), 13% (n=1354) and 8% (n=813) of the subjects experienced hospitalization and ED visits, respectively. Compared to subjects who did not experience hospitalization, hospitalized patients were older (median age [in years]: 67 vs 65), poorer self-rated health (15% vs 4.7% for fair/poor), and higher disease burden (median number of chronic conditions: 7 vs 4) at baseline. There was no association of hospitalization with any of the pharmacogenomics phenotypes. The pharmacogenomics phenotypes were not associated with disease burden, a well-established risk factor for hospital utilization outcomes. Similar findings were observed for patients with ED visits during the follow-up period. Conclusion We found no association of 10 well-established pharmacogenomics phenotypes with either hospitalization or ED visits in this relatively large biobank population and outside the context of specific drug use related to these genes. Traditional risk factors for hospitalization like age and self-rated health were much more likely to predict hospitalization and/or ED visits than this pharmacogenomics information.
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Affiliation(s)
- Paul Y Takahashi
- Division of Community Internal Medicine, Department of Internal Medicine, Mayo Clinic, Rochester, MN, USA
| | - Euijung Ryu
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Suzette J Bielinski
- Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Matthew Hathcock
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Gregory D Jenkins
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - James R Cerhan
- Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Janet E Olson
- Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
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27
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Thiyagalingam S, Kulinski AE, Thorsteinsdottir B, Shindelar KL, Takahashi PY. Dysphagia in Older Adults. Mayo Clin Proc 2021; 96:488-497. [PMID: 33549267 DOI: 10.1016/j.mayocp.2020.08.001] [Citation(s) in RCA: 57] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/07/2020] [Revised: 07/07/2020] [Accepted: 08/04/2020] [Indexed: 12/19/2022]
Abstract
Dysphagia, which is a geriatric syndrome affecting 10% to 33% of older adults, is commonly seen in older adults who have experienced a stroke or neurodegenerative diseases such as Alzheimer or Parkinson disease. Patients diagnosed as having dysphagia can experience malnutrition, pneumonia, and dehydration. Patients can also experience increased rates of mortality and long-term care admission. Providers can identify the specific type of dysphagia for treatment in approximately 80% of patients by asking 5 questions in the patient's history: What happens when you try to swallow? Do you have trouble chewing? Do you have difficulty swallowing solids, liquids, or both? Describe the symptom onset, duration, and frequency? What are the associated symptoms? Providers can then request a videofluoroscopic swallow study or a fiberoptic endoscopic evaluation of swallowing for further evaluation of oropharyngeal dysphagia. If providers are diagnosing esophageal dysphagia, barium esophagraphy or esophagogastroduodenoscopy (EGD) can be used as part of the assessment. Patients can be treated for oropharyngeal dysphagia by using compensatory interventions, including behavioral changes, oral care, dietary modification, or rehabilitative interventions such as exercises and therapeutic oral trials. Providers often address treatment of esophageal dysphagia by managing the underlying etiology, which could include removal of caustic medications or using EGD as a therapeutic modality for esophageal rings. High-quality, large research studies are necessary to further manage the diagnosis and appropriate treatment of this growing geriatric syndrome.
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Affiliation(s)
- Shanojan Thiyagalingam
- Division of Geriatric Medicine and Gerontology, Department of Medicine, Mayo Clinic School of Graduate Medical Education, Mayo Clinic College of Medicine and Science, Rochester, MN
| | - Anne E Kulinski
- Department of Neurology-Speech-Language Pathology, Mayo Clinic College of Medicine and Science, Rochester, MN
| | - Bjorg Thorsteinsdottir
- Division of Community Internal Medicine, Mayo Clinic College of Medicine and Science, Rochester, MN
| | - Katrina L Shindelar
- Division of Occupational Therapy, Department of Physical Medicine and Rehabilitation, Mayo Clinic College of Medicine and Science, Rochester, MN
| | - Paul Y Takahashi
- Division of Geriatric Medicine and Gerontology, Department of Medicine, Mayo Clinic School of Graduate Medical Education, Mayo Clinic College of Medicine and Science, Rochester, MN; Division of Community Internal Medicine, Mayo Clinic College of Medicine and Science, Rochester, MN.
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28
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Chandra A, Visscher SL, Lackore KA, Chaudhry R, Takahashi PY, Hanson GJ, Borah BJ. Health Care Costs and Utilization Predictions by the Skilled Nursing Facility Readmission Risk Instrument. J Am Med Dir Assoc 2021; 22:2154-2159.e1. [PMID: 33444563 DOI: 10.1016/j.jamda.2020.12.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2020] [Revised: 12/03/2020] [Accepted: 12/06/2020] [Indexed: 10/22/2022]
Abstract
OBJECTIVES Health care providers at hospitals and skilled nursing facilities (SNFs) are increasingly expected to optimize care of post-acute patients to reduce hospital readmissions and contain costs. To achieve these goals, providers need to understand their patients' risk of hospital readmission and how this risk is associated with health care costs. A previously developed risk prediction model identifies patients' probability of 30-day hospital readmission at the time of discharge to an SNF. With a computerized algorithm, we translated this model as the Skilled Nursing Facility Readmission Risk (SNFRR) instrument. Our objective was to evaluate the relationship between 30-day health care costs and hospital readmissions according to the level of risk calculated by this model. DESIGN This retrospective cohort study used SNFRR scores to evaluate patient data. SETTING AND PARTICIPANTS The patients were discharged from Mayo Clinic Rochester hospitals to 11 area SNFs. METHODS We compared the outcomes of all-cause 30-day standardized direct medical costs and hospital readmissions between risk quartiles based on the distribution of SNFRR scores for patients discharged to SNFs for post-acute care from April 1 through November 30, 2017. RESULTS Mean 30-day all-cause standardized costs were positively associated with SNFRR score quartiles and ranged from $9199 in the fourth quartile (probability of readmission, 0.27-0.66) to $2679 in the first quartile (probability of readmission, 0.07-0.13) (P ≤ .05). Patients in the fourth SNFRR score quartile had 5.68 times the odds of 30-day hospital readmission compared with those in the first quartile. CONCLUSIONS AND IMPLICATIONS The SNFRR instrument accurately predicted standardized direct health care costs for patients on discharge to an SNF and their risk for 30-day hospital readmission. Therefore, it could be used to help categorize patients for preemptive interventions. Further studies are needed to confirm its validity in other institutions and geographic areas.
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Affiliation(s)
- Anupam Chandra
- Division of Community Internal Medicine, Mayo Clinic, Rochester, MN, USA; Division of Geriatric Medicine and Gerontology, Mayo Clinic, Rochester, MN, USA.
| | - Sue L Visscher
- Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, USA
| | - Kandace A Lackore
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Rajeev Chaudhry
- Division of Community Internal Medicine, Mayo Clinic, Rochester, MN, USA; Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Paul Y Takahashi
- Division of Community Internal Medicine, Mayo Clinic, Rochester, MN, USA; Division of Geriatric Medicine and Gerontology, Mayo Clinic, Rochester, MN, USA
| | - Gregory J Hanson
- Division of Community Internal Medicine, Mayo Clinic, Rochester, MN, USA; Division of Geriatric Medicine and Gerontology, Mayo Clinic, Rochester, MN, USA
| | - Bijan J Borah
- Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, USA; Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
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Abstract
Medications to treat disease and extend life in our patients often amass in quantities, resulting in what has been termed "polypharmacy." This imprecise label usually describes the accumulation of 5, and often more, medications. Polypharmacy in advancing age frequently results in drug therapy problems related to interactions, drug toxicity, falls with injury, delirium, and nonadherence. Polypharmacy is associated with resulting increased hospitalizations and higher costs of care for individuals and health care systems. To reduce polypharmacy, we delineate a systematic, consultative approach to identify highest-risk medications and drug-therapy problems. We address strategic reductions (deprescribing) of medications in palliative care, long-term care, and ambulatory older adults. Best practices for reducing opioids, benzodiazepines, and other high-risk medications include education about risk and agreement by patients and their families, advocates, and care teams. Addressing deprescribing should be within the framework of patients' health status as their care and goals transition from longevity to a plan of maintaining alertness, comfort, and satisfaction of quality of life. A team approach to address polypharmacy and avoidance of high-risk therapy is optimal within long-term care. Patients with terminal illnesses or those moving toward a comfort-care emphasis benefit from medication adjustments that are recognized beneficially within each patient's care goals. In caring for older adults, the acknowledgement that complicated regimens and high-risk medications requires a care plan to reduce or prevent medication-related problems and costs that are associated with polypharmacy.
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Affiliation(s)
- Robert William Hoel
- Division of Medication Therapy Management, Pharmacy Services, Mayo Clinic, Rochester, MN.
| | | | - Paul Y Takahashi
- Division of Community Internal Medicine, Mayo Clinic, Rochester, MN
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Chandra A, Takahashi PY, McCoy RG, Hanson GJ, Chaudhry R, Storlie CB, Roellinger DL, Rahman PA, Naessens JM. Use of a Computerized Algorithm to Evaluate the Proportion and Causes of Potentially Preventable Readmissions Among Patients Discharged to Skilled Nursing Facilities. J Am Med Dir Assoc 2020; 22:1060-1066. [PMID: 33243602 DOI: 10.1016/j.jamda.2020.10.007] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2019] [Revised: 07/31/2020] [Accepted: 10/05/2020] [Indexed: 12/19/2022]
Abstract
OBJECTIVES Older patients discharged to skilled nursing facilities (SNFs) for post-acute care are at high risk for hospital readmission. Yet, as in the community setting, some readmissions may be preventable with optimal transitional care. This study examined the proportion of 30-day hospital readmissions from SNFs that could be considered potentially preventable readmissions (PPRs) and evaluated the reasons for these readmissions. DESIGN Retrospective cohort study. SETTING AND PARTICIPANTS Post-acute practice of an integrated health care delivery system serving 11 SNFs in the US Midwest. Patients discharged from the hospital to an SNF and subsequently readmitted to the hospital within 30 days from January 1, 2009, through November 31, 2016. METHODS A computerized algorithm evaluated the relationship between initial and repeat hospitalizations to determine whether the repeat hospitalization was a PPR. We assessed for changes in PPR rates across the system over the study period and evaluated the readmission categories to identify the most prevalent PPR categories. RESULTS Of 11,976 discharges to SNFs for post-acute care among 8041 patients over the study period, 16.6% resulted in rehospitalization within 30 days, and 64.8% of these rehospitalizations were considered PPRs. Annual proportion of PPRs ranged from 58.2% to 66.4% [mean (standard deviation) 0.65 (0.03); 95% confidence interval CI 0.63-0.67; P = .36], with no discernable trend. Nearly one-half (46.2%) of all 30-day readmissions were classified as potentially preventable medical readmissions related to recurrence or continuation of the reason for initial admission or to complications from the initial hospitalization. CONCLUSIONS AND IMPLICATIONS For this cohort of patients discharged to SNFs, a computerized algorithm categorized a large proportion of 30-day hospital readmissions as potentially preventable, with nearly one-half of those linked to the reason for the initial hospitalization. These findings indicate the importance of improvement in postdischarge transitional care for patients discharged to SNFs.
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Affiliation(s)
- Anupam Chandra
- Division of Community Internal Medicine, Mayo Clinic, Rochester, MN, USA.
| | - Paul Y Takahashi
- Division of Community Internal Medicine, Mayo Clinic, Rochester, MN, USA
| | - Rozalina G McCoy
- Division of Community Internal Medicine, Mayo Clinic, Rochester, MN, USA; Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Gregory J Hanson
- Division of Community Internal Medicine, Mayo Clinic, Rochester, MN, USA
| | - Rajeev Chaudhry
- Division of Community Internal Medicine, Mayo Clinic, Rochester, MN, USA; The Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, USA
| | - Curtis B Storlie
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | | | - Parvez A Rahman
- The Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, USA
| | - James M Naessens
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
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Oliveira J E Silva L, Jeffery MM, Campbell RL, Mullan AF, Takahashi PY, Bellolio F. Predictors of return visits to the emergency department among different age groups of older adults. Am J Emerg Med 2020; 46:241-246. [PMID: 33071094 DOI: 10.1016/j.ajem.2020.07.042] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2020] [Revised: 06/24/2020] [Accepted: 07/16/2020] [Indexed: 10/23/2022] Open
Abstract
OBJECTIVE To identify predictors of 30-day emergency department (ED) return visits in patients age 65-79 years and age ≥ 80 years. METHODS This was a cohort study of older adults who presented to the ED over a 1-year period. A mixed-effects logistic regression model was used to identify predictors for returning to the ED within 30 days. We stratified the cohort into those aged 65-79 years and aged ≥80 years. Adjusted odds ratios (aORs) with 95% confidence intervals (CI) were reported. This study adhered to the STROBE reporting guidelines. RESULTS A total of 21,460 ED visits representing 14,528 unique patients were included. The overall return rate was 15% (1998/13,300 visits) for age 65-79 years, and 16% (1306/8160 visits) for age ≥ 80 years. A history of congestive heart failure (CHF), dementia, or prior hospitalization within 2 years were associated with increased odds of returning in both age groups (for age 65-79: CHF aOR 1.36 [CI 1.16-1.59], dementia aOR 1.27 [CI 1.07-1.49], prior hospitalization aOR 1.36 [CI 1.19-1.56]; for age ≥ 80: CHF aOR 1.32 [CI 1.13-1.55], dementia aOR 1.22 [CI 1.04-1.42], and prior hospitalization aOR 1.27 [CI 1.09-1.47]). Being admitted from the ED was associated with decreased odds of returning to the ED within 30 days (aOR 0.72 [CI 0.64-0.80] for age 65-79 years and 0.72 [CI 0.63-0.82] for age ≥ 80). CONCLUSION Age alone was not an independent predictor of return visits. Prior hospitalization, dementia and CHF were predictors of 30-day ED return. The identification of predictors of return visits may help to optimize care transition in the ED.
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Affiliation(s)
| | - Molly M Jeffery
- Department of Emergency Medicine, Mayo Clinic, Rochester, MN, USA; Department of Health Science Research, Division of Health Care Policy and Research, Mayo Clinic, Rochester, MN, USA
| | - Ronna L Campbell
- Department of Emergency Medicine, Mayo Clinic, Rochester, MN, USA
| | - Aidan F Mullan
- Department of Biostatistics and Informatics, Mayo Clinic, Rochester, MN, USA
| | - Paul Y Takahashi
- Department of Internal Medicine, Mayo Clinic, Rochester, MN, USA
| | - Fernanda Bellolio
- Department of Emergency Medicine, Mayo Clinic, Rochester, MN, USA; Department of Health Science Research, Division of Health Care Policy and Research, Mayo Clinic, Rochester, MN, USA.
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Takahashi PY, Leppin AL, Hanson GJ. Hospital to Community Transitions for Older Adults: An Update for the Practicing Clinician. Mayo Clin Proc 2020; 95:2253-2262. [PMID: 32736941 DOI: 10.1016/j.mayocp.2020.02.001] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/31/2019] [Revised: 01/16/2020] [Accepted: 02/04/2020] [Indexed: 01/17/2023]
Abstract
Spurred by changes in both population demographics and health care reimbursement, health care providers are responding by using new models to more fully support the posthospital transition. This paper reviews common models for posthospital transition and also describes the Mayo Clinic model for care transition. Models are designed with the intent of managing the cost of health care by reducing 30-day hospital readmissions and improving management of chronic disease. Meta-analyses have proved helpful in identifying the most effective program elements designed to reduce 30-day hospital readmissions. These elements include a bundled and multidisciplinary approach to best meet the needs of patients. Successful care teams also emphasize self-empowerment for both patients and caregivers. There are 2 general types of practice. In 1 model, introduced by Mary Naylor, an advanced-practice provider cares for the patient for a set period of time, which includes home visits. In the second model, introduced by Eric Coleman, a transitions coach, who can be an RN, a social worker, or a trained volunteer, serves as the health care coach, while improving self-efficacy. Both models have been successful. At Mayo Clinic, the Mayo Clinic Care Transitions program has encompassed a 7-year experience, using the services of an advanced practice provider. In previous studies, this model demonstrated a 20.1% (95% confidence interval [CI], 15.8 to 24.1%) decrease in 30-day readmission in controls compared with 12.4% (95% CI, 8.9 to 15.7%) in the control group. Although this model was successful in reducing 30-day readmissions, there was no difference between groups at 180 days. In patients experiencing the highest deciles of cost (8th decile), enrollment in a care transitions program reduced their overall cost by $2700. This cost savings was statistically significant. Both patients and caregivers participating in the program appreciated the home visits and felt more comfortable communicating at home.
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Affiliation(s)
- Paul Y Takahashi
- Division of Community Internal Medicine and Division of Geriatrics and Gerontology, Mayo Clinic, Rochester, MN; Robert and Arlene Kogod Center on Aging, Mayo Clinic, Rochester, MN.
| | - Aaron L Leppin
- Division of Health Care Policy and Research, Kern Center for the Science of Healthcare Delivery, Mayo Clinic, Rochester, MN
| | - Gregory J Hanson
- Division of Community Internal Medicine and Division of Geriatrics and Gerontology, Mayo Clinic, Rochester, MN; Robert and Arlene Kogod Center on Aging, Mayo Clinic, Rochester, MN
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Hoversten KP, Kiemele LJ, Stolp AM, Takahashi PY, Verdoorn BP. Prevention, Diagnosis, and Management of Chronic Wounds in Older Adults. Mayo Clin Proc 2020; 95:2021-2034. [PMID: 32276784 DOI: 10.1016/j.mayocp.2019.10.014] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/14/2019] [Revised: 10/11/2019] [Accepted: 10/17/2019] [Indexed: 11/18/2022]
Abstract
Chronic wounds are common, disproportionately affect older adults, and are likely to be encountered by providers across all specialties and care settings. All providers should be familiar with basic wound prevention, identification, classification, and treatment approach, all of which are outlined in this article.
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Affiliation(s)
| | | | - Anne M Stolp
- Department of Medicine, Division of Community Internal Medicine, Mayo Clinic, Rochester, MN
| | - Paul Y Takahashi
- Department of Medicine, Divisions of Community Internal Medicine and Geriatric Medicine/Gerontology, Mayo Clinic, Rochester, MN
| | - Brandon P Verdoorn
- Department of Medicine, Divisions of Community Internal Medicine and Geriatric Medicine/Gerontology, Mayo Clinic, Rochester, MN.
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Bartley MM, Rahman PA, Storlie CB, Takahashi PY, Chandra A. Associations of Skilled Nursing Facility Quality Ratings With 30-Day Rehospitalizations and Emergency Department Visits. Ann Longterm Care 2020; 28:e11-e17. [PMID: 33833620 PMCID: PMC8025962] [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] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Skilled nursing facilities (SNFs) increasingly provide care to patients after hospitalization. The Centers for Medicare & Medicaid Services reports ratings for SNFs for overall quality, staffing, health inspections, and clinical quality measures. However, the relationship between these ratings and patient outcomes remains unclear. In this retrospective cohort study, we reviewed the electronic health records of 3,923 adult patients discharged from the hospital and admitted to 9 SNFs served by a health care delivery system. We used Cox proportional hazards models to examine associations between the overall quality and individual ratings and our primary outcomes of 30-day rehospitalizations and 30-day emergency department visits. Patients in higher-rated facilities had a 13% lower risk of 30-day rehospitalization than patients in lower-rated facilities (hazard ratio, 0.87; 95% CI, 0.76-0.99). The risk of emergency department visits was also lower for patients in facilities with a higher overall quality rating and a higher quality measures rating. Staffing and health inspection ratings were not associated with our primary outcomes. These findings may help inform providers and nursing home policy makers.
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Affiliation(s)
- Mairead M Bartley
- Division of Community Internal Medicine, Mayo Clinic, Rochester, Minnesota
| | - Parvez A Rahman
- Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota
| | - Curtis B Storlie
- Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota
| | - Paul Y Takahashi
- Division of Community Internal Medicine, Mayo Clinic, Rochester, Minnesota
| | - Anupam Chandra
- Division of Community Internal Medicine, Mayo Clinic, Rochester, Minnesota
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Roelfsema F, Liu PY, Takahashi PY, Yang RJ, Veldhuis JD. Dynamic Interactions Between LH and Testosterone in Healthy Community-Dwelling Men: Impact of Age and Body Composition. J Clin Endocrinol Metab 2020; 105:5650390. [PMID: 31790144 PMCID: PMC7025815 DOI: 10.1210/clinem/dgz246] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/09/2019] [Accepted: 11/30/2019] [Indexed: 11/19/2022]
Abstract
BACKGROUND Aging is associated with diminished testosterone (Te) secretion, which may be attributed to Leydig cell dysfunction, decreased pituitary stimulation, and altered Te feedback. OBJECTIVE To study all regulatory nodes-gonadotropin-releasing hormone (GnRH), luteinizing hormone (LH) and Leydig cell-in the same cohort of healthy men. STUDY DESIGN This was a placebo-controlled, blinded, prospectively randomized cross-over study in 40 men, age range 19 to 73 years, and body mass index (BMI) range 20 to 34.3 kg/m2. A submaximal dose of the GnRH antagonist ganirelix was used to assess outflow of GnRH, by calculating the difference between LH output during the control arm and ganirelix arm. Ketoconazole (a steroidogenic inhibitor) was used to estimate feedback, by the difference in LH output during the ketoconazole and control arm. High-dose ganirelix and repeated LH infusions were used to measure testicular responsivity. Blood sampling was performed at 10-minute intervals. RESULTS There were age-related, but not body composition-related decreases in estimated GnRH secretion, the feedback strength of Te on LH, and Leydig cell responsivity to LH, accompanied by changes in approximate entropy. Bioavailable Te levels were negatively related to both age and computed tomography (CT)-estimated abdominal visceral mass (AVF), without interaction between these variables. The LH response to a submaximal dose of GnRH was independent of age and AVF. CONCLUSION Advancing age is associated with (1) attenuated bioavailable Te secretion caused by diminished GnRH outflow and not by decreased GnRH responsivity of the gonadotrope, (2) diminished testicular responsivity to infused LH pulses, and (3) partial compensation by diminished Te feedback on central gonadotropic regulation.
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Affiliation(s)
- Ferdinand Roelfsema
- Department of Internal Medicine, Section Endocrinology and Metabolism, Leiden University Medical Center, Leiden, The Netherlands
| | - Peter Y Liu
- Department of Medicine, David Geffen School of Medicine at UCLA, Harbor-UCLA Medical Center and Los Angeles Biomedical Research Institute, Los Angeles, California
| | - Paul Y Takahashi
- Department of Primary Care Internal Medicine, Mayo Clinic, Rochester, Minnesota
| | - Rebecca J Yang
- Endocrine Research Unit, Mayo School of Graduate Medical Education, Center for Translational Science Activities, Mayo Clinic, Rochester, Minnesota
| | - Johannes D Veldhuis
- Endocrine Research Unit, Mayo School of Graduate Medical Education, Center for Translational Science Activities, Mayo Clinic, Rochester, Minnesota
- Correspondence: Johannes Veldhuis, MD, Endocrine Research Unit, Mayo Clinic, 200 First Street SW, Rochester, MN 55906, USA. Email
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Bielinski SJ, St Sauver JL, Olson JE, Larson NB, Black JL, Scherer SE, Bernard ME, Boerwinkle E, Borah BJ, Caraballo PJ, Curry TB, Doddapaneni H, Formea CM, Freimuth RR, Gibbs RA, Giri J, Hathcock MA, Hu J, Jacobson DJ, Jones LA, Kalla S, Koep TH, Korchina V, Kovar CL, Lee S, Liu H, Matey ET, McGree ME, McAllister TM, Moyer AM, Muzny DM, Nicholson WT, Oyen LJ, Qin X, Raj R, Roger VL, Rohrer Vitek CR, Ross JL, Sharp RR, Takahashi PY, Venner E, Walker K, Wang L, Wang Q, Wright JA, Wu TJ, Wang L, Weinshilboum RM. Cohort Profile: The Right Drug, Right Dose, Right Time: Using Genomic Data to Individualize Treatment Protocol (RIGHT Protocol). Int J Epidemiol 2020; 49:23-24k. [PMID: 31378813 PMCID: PMC7124480 DOI: 10.1093/ije/dyz123] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/31/2019] [Indexed: 12/29/2022] Open
Affiliation(s)
- Suzette J Bielinski
- Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Jennifer L St Sauver
- Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
- Robert D and Patricia E Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, USA
| | - Janet E Olson
- Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
- Center for Individualized Medicine, Mayo Clinic, Rochester, MN, USA
| | - Nicholas B Larson
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - John L Black
- Center for Individualized Medicine, Mayo Clinic, Rochester, MN, USA
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA
| | - Steven E Scherer
- Human Genome Sequencing Center, Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | | | - Eric Boerwinkle
- Human Genome Sequencing Center, Department of Molecular and Human Genetics, Baylor College of Medicine, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Bijan J Borah
- Robert D and Patricia E Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, USA
- Division of Health Care Policy and Research, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Pedro J Caraballo
- Division of General Internal Medicine, Department of Medicine, Mayo Clinic, Rochester, MN, USA
| | - Timothy B Curry
- Center for Individualized Medicine, Mayo Clinic, Rochester, MN, USA
- Department of Anesthesia and Perioperative Medicine, Mayo Clinic, Rochester, MN, USA
| | | | | | - Robert R Freimuth
- Division of Digital Health Sciences, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Richard A Gibbs
- Human Genome Sequencing Center, Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Jyothsna Giri
- Center for Individualized Medicine, Mayo Clinic, Rochester, MN, USA
| | - Matthew A Hathcock
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Jianhong Hu
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Debra J Jacobson
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Leila A Jones
- Center for Individualized Medicine, Mayo Clinic, Rochester, MN, USA
| | - Sara Kalla
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | | | - Viktoriya Korchina
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Christie L Kovar
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Sandra Lee
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Hongfang Liu
- Division of Digital Health Sciences, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Eric T Matey
- Center for Individualized Medicine, Mayo Clinic, Rochester, MN, USA
- Department of Pharmacy, Mayo Clinic, Rochester, MN, USA
| | - Michaela E McGree
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | | | - Ann M Moyer
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA
| | - Donna M Muzny
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Wayne T Nicholson
- Department of Anesthesia and Perioperative Medicine, Mayo Clinic, Rochester, MN, USA
| | - Lance J Oyen
- Department of Pharmacy, Mayo Clinic, Rochester, MN, USA
| | - Xiang Qin
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Ritika Raj
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Véronique L Roger
- Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
- Division of Cardiovascular Diseases, Department of Internal Medicine, Mayo Clinic, Rochester, MN, USA
| | | | | | - Richard R Sharp
- Center for Individualized Medicine, Mayo Clinic, Rochester, MN, USA
- Division of Health Care Policy and Research, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Paul Y Takahashi
- Division of Community Internal Medicine, Department of Medicine, Mayo Clinic, Rochester, MN, USA
| | - Eric Venner
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Kimberly Walker
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Liwei Wang
- Division of Digital Health Sciences, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Qiaoyan Wang
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Jessica A Wright
- Center for Individualized Medicine, Mayo Clinic, Rochester, MN, USA
- Department of Pharmacy, Mayo Clinic, Rochester, MN, USA
| | - Tsung-Jung Wu
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Liewei Wang
- Center for Individualized Medicine, Mayo Clinic, Rochester, MN, USA
- Division of Clinical Pharmacology, Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, USA
| | - Richard M Weinshilboum
- Center for Individualized Medicine, Mayo Clinic, Rochester, MN, USA
- Division of Clinical Pharmacology, Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, USA
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Espinoza Suarez NR, Walker LE, Jeffery MM, Stanich JA, Campbell RL, Lohse CM, Takahashi PY, Bellolio F. Validation of the Elderly Risk Assessment Index in the Emergency Department. Am J Emerg Med 2019; 38:1441-1445. [PMID: 31839521 DOI: 10.1016/j.ajem.2019.11.048] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2019] [Revised: 11/27/2019] [Accepted: 11/30/2019] [Indexed: 01/04/2023] Open
Abstract
OBJECTIVES The Elderly Risk Assessment (ERA) score is a validated index for primary care patients that predict hospitalizations, mortality, and Emergency Department (ED) visits. The score incorporates age, prior hospital days, marital status, and comorbidities. Our aim was to validate the ERA score in ED patients. METHODS Observational cohort study of patients age ≥ 60 presenting to an academic ED over a 1-year period. Regression analyses were performed for associations with outcomes (hospitalization, return visits and death). Medians, interquartile range (IQR), odds ratios (OR) and 95% confidence intervals (CI) were calculated. RESULTS The cohort included 27,397 visits among 18,607 patients. Median age 74 years (66-82), 48% were female and 59% were married. Patients from 54% of visits were admitted to the hospital, 16% returned to the ED within 30 days, and 18% died within one year. Higher ERA scores were associated with: hospital admission (score 10 [4-16] vs 5 [1-11], p < 0.0001), return visits (11 [5-17] vs 7 [2-13], p < 0.0001); and death within one year (14 [7-20] vs 6 [2-13], p < 0.0001). Patients with ERA score ≥ 16 were more likely to be admitted to the hospital, OR 2.14 (2.02-2.28, p < 0.0001), return within 30 days OR 1.99 (1.85-2.14), and to die within a year, OR 2.69 (2.54-2.85). CONCLUSION The ERA score can be automatically calculated within the electronic health record and helps identify patients at increased risk of death, hospitalization and return ED visits. The ERA score can be applied to ED patients, and may help prognosticate the need for advanced care planning.
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Affiliation(s)
| | - Laura E Walker
- Department of Emergency Medicine, Mayo Clinic, Rochester, MN, USA
| | - Molly M Jeffery
- Department of Emergency Medicine, Mayo Clinic, Rochester, MN, USA; Department Health Science Research, Division of Health Care Policy and Research, Mayo Clinic, Rochester, MN, USA
| | | | - Ronna L Campbell
- Department of Emergency Medicine, Mayo Clinic, Rochester, MN, USA
| | - Christine M Lohse
- Department of Biostatistics and Informatics, Mayo Clinic, Rochester, MN, USA
| | - Paul Y Takahashi
- Department of Internal Medicine, Mayo Clinic, Rochester, MN, USA
| | - Fernanda Bellolio
- Department of Emergency Medicine, Mayo Clinic, Rochester, MN, USA; Department Health Science Research, Division of Health Care Policy and Research, Mayo Clinic, Rochester, MN, USA.
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Espinoza Suarez NR, Walker LE, Jeffery MM, Stanich JA, Campbell RL, Lohse CM, Takahashi PY, Bellolio F. Validation of the Elderly Risk Assessment Index in the Emergency Department. Am J Emerg Med 2019. [PMID: 31839521 DOI: 10.1016/j.ajem.2019.11.048.] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022] Open
Abstract
OBJECTIVES The Elderly Risk Assessment (ERA) score is a validated index for primary care patients that predict hospitalizations, mortality, and Emergency Department (ED) visits. The score incorporates age, prior hospital days, marital status, and comorbidities. Our aim was to validate the ERA score in ED patients. METHODS Observational cohort study of patients age ≥ 60 presenting to an academic ED over a 1-year period. Regression analyses were performed for associations with outcomes (hospitalization, return visits and death). Medians, interquartile range (IQR), odds ratios (OR) and 95% confidence intervals (CI) were calculated. RESULTS The cohort included 27,397 visits among 18,607 patients. Median age 74 years (66-82), 48% were female and 59% were married. Patients from 54% of visits were admitted to the hospital, 16% returned to the ED within 30 days, and 18% died within one year. Higher ERA scores were associated with: hospital admission (score 10 [4-16] vs 5 [1-11], p < 0.0001), return visits (11 [5-17] vs 7 [2-13], p < 0.0001); and death within one year (14 [7-20] vs 6 [2-13], p < 0.0001). Patients with ERA score ≥ 16 were more likely to be admitted to the hospital, OR 2.14 (2.02-2.28, p < 0.0001), return within 30 days OR 1.99 (1.85-2.14), and to die within a year, OR 2.69 (2.54-2.85). CONCLUSION The ERA score can be automatically calculated within the electronic health record and helps identify patients at increased risk of death, hospitalization and return ED visits. The ERA score can be applied to ED patients, and may help prognosticate the need for advanced care planning.
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Affiliation(s)
| | - Laura E Walker
- Department of Emergency Medicine, Mayo Clinic, Rochester, MN, USA
| | - Molly M Jeffery
- Department of Emergency Medicine, Mayo Clinic, Rochester, MN, USA; Department Health Science Research, Division of Health Care Policy and Research, Mayo Clinic, Rochester, MN, USA
| | | | - Ronna L Campbell
- Department of Emergency Medicine, Mayo Clinic, Rochester, MN, USA
| | - Christine M Lohse
- Department of Biostatistics and Informatics, Mayo Clinic, Rochester, MN, USA
| | - Paul Y Takahashi
- Department of Internal Medicine, Mayo Clinic, Rochester, MN, USA
| | - Fernanda Bellolio
- Department of Emergency Medicine, Mayo Clinic, Rochester, MN, USA; Department Health Science Research, Division of Health Care Policy and Research, Mayo Clinic, Rochester, MN, USA.
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Yousufuddin M, Takahashi PY, Major B, Ahmmad E, Al-Zubi H, Peters J, Doyle T, Jensen K, Al Ward RY, Sharma U, Seshadri A, Wang Z, Simha V, Murad MH. Association between hyperlipidemia and mortality after incident acute myocardial infarction or acute decompensated heart failure: a propensity score matched cohort study and a meta-analysis. BMJ Open 2019; 9:e028638. [PMID: 31843818 PMCID: PMC6924840 DOI: 10.1136/bmjopen-2018-028638] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [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] [Indexed: 12/22/2022] Open
Abstract
OBJECTIVE To examine the effect of HLP, defined as having a pre-existing or a new in-hospital diagnosis based on low density lipoprotein cholesterol (LDL-C) level ≥100 mg/dL during index hospitalisation or within the preceding 6 months, on all-cause mortality after hospitalisation for acute myocardial infarction (AMI) or acute decompensated heart failure (ADHF) and to determine whether HLP modifies mortality associations of other competing comorbidities. A systematic review and meta-analysis to place the current findings in the context of published literature. DESIGN Retrospective study, 1:1 propensity-score matching cohorts; a meta-analysis. SETTING Large academic centre, 1996-2015. PARTICIPANTS Hospitalised patients with AMI or ADHF. MAIN OUTCOMES AND MEASURES All-cause mortality and meta-analysis of relative risks (RR). RESULTS Unmatched cohorts: 13 680 patients with AMI (age (mean) 68.5 ± (SD) 13.7 years; 7894 (58%) with HLP) and 9717 patients with ADHF (age, 73.1±13.7 years; 3668 (38%) with HLP). In matched cohorts, the mortality was lower in AMI patients (n=4348 pairs) with HLP versus no HLP, 5.9 versus 8.6/100 person-years of follow-up, respectively (HR 0.76, 95% CI 0.72 to 0.80). A similar mortality reduction occurred in matched ADHF patients (n=2879 pairs) with or without HLP (12.4 vs 16.3 deaths/100 person-years; HR 0.80, 95% CI 0.75 to 0.86). HRs showed modest reductions when HLP occurred concurrently with other comorbidities. Meta-analyses of nine observational studies showed that HLP was associated with a lower mortality at ≥2 years after incident AMI or ADHF (AMI: RR 0.72, 95% CI 0.69 to 0.76; heart failure (HF): RR 0.67, 95% CI 0.55 to 0.81). CONCLUSIONS Among matched AMI and ADHF cohorts, concurrent HLP, compared with no HLP, was associated with a lower mortality and attenuation of mortality associations with other competing comorbidities. These findings were supported by a systematic review and meta-analysis.
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Affiliation(s)
| | - Paul Y Takahashi
- Internal Medicine, Mayo Clinic Minnesota, Rochester, Minnesota, USA
| | - Brittny Major
- Internal Medicine, Mayo Clinic Minnesota, Rochester, Minnesota, USA
| | - Eimad Ahmmad
- Internal Medicine, Mayo Clinic Health System in Albert Lea, Albert Lea, Minnesota, USA
| | - Hossam Al-Zubi
- Internal Medicine, Mayo Clinic Health System in Albert Lea, Albert Lea, Minnesota, USA
| | - Jessica Peters
- Internal Medicine, Mayo Clinic Minnesota, Rochester, Minnesota, USA
| | - Taylor Doyle
- Internal Medicine, Mayo Clinic Minnesota, Rochester, Minnesota, USA
| | - Kelsey Jensen
- Internal Medicine, Mayo Clinic Health System in Albert Lea, Albert Lea, Minnesota, USA
| | - Ruaa Y Al Ward
- Internal Medicine, Mayo Clinic Minnesota, Rochester, Minnesota, USA
| | - Umesh Sharma
- Internal Medicine, Mayo Clinic Health System in Albert Lea, Albert Lea, Minnesota, USA
| | - Ashok Seshadri
- Internal Medicine, Mayo Clinic Minnesota, Rochester, Minnesota, USA
| | - Zhen Wang
- Internal Medicine, Mayo Clinic Minnesota, Rochester, Minnesota, USA
| | - Vinaya Simha
- Internal Medicine, Mayo Clinic Minnesota, Rochester, Minnesota, USA
| | - M Hassan Murad
- Internal Medicine, Mayo Clinic Minnesota, Rochester, Minnesota, USA
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Olson JE, Ryu E, Hathcock MA, Gupta R, Bublitz JT, Takahashi PY, Bielinski SJ, St Sauver JL, Meagher K, Sharp RR, Thibodeau SN, Cicek M, Cerhan JR. Characteristics and utilisation of the Mayo Clinic Biobank, a clinic-based prospective collection in the USA: cohort profile. BMJ Open 2019; 9:e032707. [PMID: 31699749 PMCID: PMC6858142 DOI: 10.1136/bmjopen-2019-032707] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
PURPOSE The Mayo Clinic Biobank was established to provide a large group of patients from which comparison groups (ie, controls) could be selected for case-control studies, to create a prospective cohort with sufficient power for common outcomes and to support electronic health record (EHR) studies. PARTICIPANTS A total of 56 862 participants enrolled (21% response rate) into the Mayo Clinic Biobank from Rochester, Minnesota (77%, n=43 836), Jacksonville, Florida (18%, n=10 368) and La Crosse, Wisconsin (5%, n=2658). Participants were all Mayo Clinic patients, 18 years of age or older and US residents. FINDINGS TO DATE Overall, 43% of participants were 65 years of age or older and female participants were more frequent (59%) than males at all sites. Most participants resided in the Upper Midwest regions of the USA (Minnesota, Iowa, Illinois or Wisconsin), Florida or Georgia. Self-reported race among Biobank participants was 90% white. Here we provide examples of the types of studies that have successfully utilised the resource, including (1) investigations of the population itself, (2) provision of controls for case-control studies, (3) genotype-driven research, (4) EHR-based research and (5) prospective recruitment to other studies. Over 270 projects have been approved to date to access Biobank data and/or samples; over 200 000 sample aliquots have been approved for distribution. FUTURE PLANS The data and samples in the Mayo Clinic Biobank can be used for various types of epidemiological and clinical studies, especially in the setting of case-control studies for which the Biobank samples serve as control samples. We are planning cohort studies with additional follow-up and acquisition of genetic information on a large scale.
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Affiliation(s)
- Janet E Olson
- Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA
| | - Euijung Ryu
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA
| | - Matthew A Hathcock
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA
| | - Ruchi Gupta
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA
| | - Joshua T Bublitz
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA
| | - Paul Y Takahashi
- Division of Primary Care Internal Medicine, Department of Internal Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Suzette J Bielinski
- Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA
| | - Jennifer L St Sauver
- Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA
| | - Karen Meagher
- Biomedical Ethics Research Program, Mayo Clinic, Rochester, Minnesota, USA
| | - Richard R Sharp
- Biomedical Ethics Research Program, Mayo Clinic, Rochester, Minnesota, USA
| | - Stephen N Thibodeau
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA
| | - Mine Cicek
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA
| | - James R Cerhan
- Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA
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Goudarzvand S, St Sauver J, Mielke MM, Takahashi PY, Lee Y, Sohn S. Early temporal characteristics of elderly patient cognitive impairment in electronic health records. BMC Med Inform Decis Mak 2019; 19:149. [PMID: 31391041 PMCID: PMC6686236 DOI: 10.1186/s12911-019-0858-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
Background The aging population has led to an increase in cognitive impairment (CI) resulting in significant costs to patients, their families, and society. A research endeavor on a large cohort to better understand the frequency and severity of CI is urgent to respond to the health needs of this population. However, little is known about temporal trends of patient health functions (i.e., activity of daily living [ADL]) and how these trends are associated with the onset of CI in elderly patients. Also, the use of a rich source of clinical free text in electronic health records (EHRs) to facilitate CI research has not been well explored. The aim of this study is to characterize and better understand early signals of elderly patient CI by examining temporal trends of patient ADL and analyzing topics of patient medical conditions in clinical free text using topic models. Methods The study cohort consists of physician-diagnosed CI patients (n = 1,435) and cognitively unimpaired (CU) patients (n = 1,435) matched by age and sex, selected from patients 65 years of age or older at the time of enrollment in the Mayo Clinic Biobank. A corpus analysis was performed to examine the basic statistics of event types and practice settings where the physician first diagnosed CI. We analyzed the distribution of ADL in three different age groups over time before the development of CI. Furthermore, we applied three different topic modeling approaches on clinical free text to examine how patients’ medical conditions change over time when they were close to CI diagnosis. Results The trajectories of ADL deterioration became steeper in CI patients than CU patients approximately 1 to 1.5 year(s) before the actual physician diagnosis of CI. The topic modeling showed that the topic terms were mostly correlated and captured the underlying semantics relevant to CI when approaching to CI diagnosis. Conclusions There exist notable differences in temporal trends of basic and instrumental ADL between CI and CU patients. The trajectories of certain individual ADL, such as bathing and responsibility of own medication, were closely associated with CI development. The topic terms obtained by topic modeling methods from clinical free text have a potential to show how CI patients’ conditions evolve and reveal overlooked conditions when they close to CI diagnosis.
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Affiliation(s)
- Somaieh Goudarzvand
- School of Computing and Engineering, University of Missouri, Kansas City, MO, USA
| | | | | | | | - Yugyung Lee
- School of Computing and Engineering, University of Missouri, Kansas City, MO, USA
| | - Sunghwan Sohn
- Division of Digital Health Sciences, Mayo Clinic, Rochester, MN, USA.
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Takahashi PY, Quigg SM, Croghan IT, Schroeder DR, Ebbert JO. SMART goals setting and biometric changes in obese adults with multimorbidity: Secondary analysis of a randomized controlled trial. SAGE Open Med 2019; 7:2050312119858042. [PMID: 31258905 PMCID: PMC6591663 DOI: 10.1177/2050312119858042] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2018] [Accepted: 05/22/2019] [Indexed: 01/22/2023] Open
Abstract
Objectives: Clinicians recommend diet and exercise for overweight/obese patients. We
conducted a secondary analysis of a randomized controlled clinical trial
evaluating goal setting and pedometer use versus usual care on weight, waist
circumference, and blood pressure of patients with multiple chronic
conditions. Methods: In this trial, we recruited and randomized patients over 18 years with
multiple chronic conditions. There were two groups with an immediate
intervention group who received behavioral coaching and a pedometer versus a
delayed control who received the intervention after 2 months. We evaluated
body weight, waist circumference, and blood pressure as outcomes. We used
analysis of covariance to evaluate differences between the intervention and
the control groups. Results: Of 130 patients, mean age was 63.4 years (SD, 17.3). At 2 months,
intervention participants lost 0.2 kg versus a 0.1-kg gain in the control
participants (P = .44). The immediate intervention group
had significantly smaller waist circumference change at 2-month follow-up
compared to control at −1.6 cm (95% confidence interval = −3.1 to −0.1),
which was driven by an increase in waist circumference in the delayed
control group. No difference in systolic blood pressure was observed. Discussion: We observed no difference in weight or blood pressure between the groups with
obesity and multiple chronic conditions.
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Affiliation(s)
- Paul Y Takahashi
- Division of Primary Care Internal Medicine, Mayo Clinic, Rochester, MN, USA.,Robert and Arlene Kogod Center on Aging, Mayo Clinic, Rochester, MN, USA
| | - Stephanie M Quigg
- Division of Primary Care Internal Medicine, Mayo Clinic, Rochester, MN, USA
| | - Ivana T Croghan
- Division of Primary Care Internal Medicine, Mayo Clinic, Rochester, MN, USA.,Nicotine Dependence Center, Mayo Clinic, Rochester, MN, USA
| | - Darrell R Schroeder
- Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, USA
| | - Jon O Ebbert
- Division of Primary Care Internal Medicine, Mayo Clinic, Rochester, MN, USA.,Nicotine Dependence Center, Mayo Clinic, Rochester, MN, USA
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Thorsteinsdottir B, Peterson SM, Naessens JM, McCoy RG, Hanson GJ, Hickson LJ, Chen CYY, Rahman PA, Shah ND, Borkenhagen L, Chandra A, Havyer R, Leppin A, Takahashi PY. Care Transitions Program for High-Risk Frail Older Adults is Most Beneficial for Patients with Cognitive Impairment. J Hosp Med 2019; 14:329-335. [PMID: 30794142 PMCID: PMC6546541 DOI: 10.12788/jhm.3112] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/19/2018] [Accepted: 10/21/2018] [Indexed: 02/06/2023]
Abstract
BACKGROUND Although posthospitalization care transitions programs (CTP) are highly diverse, their overall program thoroughness is most predictive of their success. OBJECTIVE To identify components of a successful homebased CTP and patient characteristics that are most predictive of reduced 30-day readmissions. DESIGN Retrospective cohort. PATIENTS A total of 315 community-dwelling, hospitalized, older adults (≥60 years) at high risk for readmission (Elder Risk Assessment score ≥16), discharged home over the period of January 1, 2011 to June 30, 2013. SETTING Midwest primary care practice in an integrated health system. INTERVENTION Enrollment in a CTP during acute hospitalization. MEASUREMENTS The primary outcome was all-cause readmission within 30 days of the first CTP evaluation. Logistic regression was used to examine independent variables, including patient demographics, comorbidities, number of medications, completion, and timing of program fidelity measures, and prior utilization of healthcare. RESULTS The overall 30-day readmission rate was 17.1%. The intensity of follow-up varied among patients, with 17.1% and 50.8% of the patients requiring one and ≥3 home visits, respectively, within 30 days. More than half (54.6%) required visits beyond 30 days. Compared with patients who were not readmitted, readmitted patients were less likely to exhibit cognitive impairment (29.6% vs 46.0%; P = .03) and were more likely to have high medication use (59.3% vs 44.4%; P = .047), more emergency department (ED; 0.8 vs 0.4; P = .03) and primary care visits (4.0 vs 3.0; P = .018), and longer cumulative time in the hospital (4.6 vs 2.5 days; P = .03) within 180 days of the index hospitalization. Multivariable analysis indicated that only cognitive impairment and previous ED visits were important predictors of readmission. CONCLUSIONS No single CTP component reliably predicted reduced readmission risk. Patients with cognitive impairment and polypharmacy derived the most benefit from the program.
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Affiliation(s)
- Bjorg Thorsteinsdottir
- Division of Primary Care Internal Medicine, Mayo Clinic, Rochester, Minnesota
- Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, Minnesota
- Knowledge and Evaluation Research Unit, Mayo Clinic, Rochester, Minnesota
- Biomedical Ethics Research Program, Mayo Clinic, Rochester, Minnesota
- Corresponding Author: Bjorg Thorsteinsdottir, MD: E-mail: thorsteinsdottir. ; Telephone: 507-774-5944
| | - Stephanie M Peterson
- Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota
| | - James M Naessens
- Division of Health Care Policy and Research, Mayo Clinic, Rochester, Minnesota
| | - Rozalina G McCoy
- Division of Primary Care Internal Medicine, Mayo Clinic, Rochester, Minnesota
- Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, Minnesota
| | - Gregory J Hanson
- Division of Primary Care Internal Medicine, Mayo Clinic, Rochester, Minnesota
| | - LaTonya J Hickson
- Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, Minnesota
- Division of Nephrology and Hypertension, Mayo Clinic, Rochester, Minnesota
| | - Christina YY Chen
- Division of Primary Care Internal Medicine, Mayo Clinic, Rochester, Minnesota
| | - Parvez A Rahman
- Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, Minnesota
| | - Nilay D Shah
- Knowledge and Evaluation Research Unit, Mayo Clinic, Rochester, Minnesota
| | - Lynn Borkenhagen
- Division of Primary Care Internal Medicine, Mayo Clinic, Rochester, Minnesota
| | - Anupam Chandra
- Division of Primary Care Internal Medicine, Mayo Clinic, Rochester, Minnesota
| | - Rachel Havyer
- Division of Primary Care Internal Medicine, Mayo Clinic, Rochester, Minnesota
- Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, Minnesota
| | - Aaron Leppin
- Knowledge and Evaluation Research Unit, Mayo Clinic, Rochester, Minnesota
| | - Paul Y Takahashi
- Division of Primary Care Internal Medicine, Mayo Clinic, Rochester, Minnesota
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Chandra A, Rahman PA, Sneve A, McCoy RG, Thorsteinsdottir B, Chaudhry R, Storlie CB, Murphree DH, Hanson GJ, Takahashi PY. Risk of 30-Day Hospital Readmission Among Patients Discharged to Skilled Nursing Facilities: Development and Validation of a Risk-Prediction Model. J Am Med Dir Assoc 2019; 20:444-450.e2. [PMID: 30852170 DOI: 10.1016/j.jamda.2019.01.137] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2018] [Revised: 01/14/2019] [Accepted: 01/16/2019] [Indexed: 12/22/2022]
Abstract
OBJECTIVES Patients discharged to a skilled nursing facility (SNF) for post-acute care have a high risk of hospital readmission. We aimed to develop and validate a risk-prediction model to prospectively quantify the risk of 30-day hospital readmission at the time of discharge to a SNF. DESIGN Retrospective cohort study. SETTING Ten independent SNFs affiliated with the post-acute care practice of an integrated health care delivery system. PARTICIPANTS We evaluated 6032 patients who were discharged to SNFs for post-acute care after hospitalization. MEASUREMENTS The primary outcome was all-cause 30-day hospital readmission. Patient demographics, medical comorbidity, prior use of health care, and clinical parameters during the index hospitalization were analyzed by using gradient boosting machine multivariable analysis to build a predictive model for 30-day hospital readmission. Area under the receiver operating characteristic curve (AUC) was assessed on out-of-sample observations under 10-fold cross-validation. RESULTS Among 8616 discharges to SNFs from January 1, 2009, through June 30, 2014, a total of 1568 (18.2%) were readmitted to the hospital within 30 days. The 30-day hospital readmission prediction model had an AUC of 0.69, a 16% improvement over risk assessment using the Charlson Comorbidity Index alone. The final model included length of stay, abnormal laboratory parameters, and need for intensive care during the index hospitalization; comorbid status; and number of emergency department and hospital visits within the preceding 6 months. CONCLUSIONS AND IMPLICATIONS We developed and validated a risk-prediction model for 30-day hospital readmission in patients discharged to a SNF for post-acute care. This prediction tool can be used to risk stratify the complex population of hospitalized patients who are discharged to SNFs to prioritize interventions and potentially improve the quality, safety, and cost-effectiveness of care.
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Affiliation(s)
- Anupam Chandra
- Division of Primary Care Internal Medicine, Mayo Clinic, Rochester, MN.
| | - Parvez A Rahman
- Robert D. and Patricia E. Kern Center for Science of Health Care Delivery, Mayo Clinic, Rochester, MN
| | - Amelia Sneve
- Division of Primary Care Internal Medicine, Mayo Clinic, Rochester, MN
| | - Rozalina G McCoy
- Division of Primary Care Internal Medicine, Mayo Clinic, Rochester, MN; Division of Health Care Policy and Research, Mayo Clinic, Rochester, MN
| | | | - Rajeev Chaudhry
- Division of Primary Care Internal Medicine, Mayo Clinic, Rochester, MN
| | - Curtis B Storlie
- Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN
| | - Dennis H Murphree
- Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN
| | - Gregory J Hanson
- Division of Primary Care Internal Medicine, Mayo Clinic, Rochester, MN
| | - Paul Y Takahashi
- Division of Primary Care Internal Medicine, Mayo Clinic, Rochester, MN
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Bobo WV, Grossardt BR, Lapid MI, Leung JG, Stoppel C, Takahashi PY, Hoel RW, Chang Z, Lachner C, Chauhan M, Flowers L, Brue SM, Frye MA, St. Sauver J, Rocca WA, Sutor B. Frequency and predictors of the potential overprescribing of antidepressants in elderly residents of a geographically defined U.S. population. Pharmacol Res Perspect 2019; 7:e00461. [PMID: 30693088 PMCID: PMC6344796 DOI: 10.1002/prp2.461] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2018] [Revised: 12/07/2018] [Accepted: 12/18/2018] [Indexed: 12/20/2022] Open
Abstract
The purpose of this study was to estimate the extent of potential antidepressant overprescribing in a geographically defined U.S. population, and to determine the indications and factors that account for it. We conducted a cohort study of new antidepressant prescriptions for elderly residents of Olmsted County, Minnesota, 2005-2012, using the Rochester Epidemiology Project medical records-linkage system. Indications for antidepressants were abstracted from health records for all cohort members. Potential antidepressant overprescribing was defined based on regulatory approval, the level of evidence identified from a standardized drug information database, and multidisciplinary expert review. Predictors of potential antidepressant overprescribing were investigated using logistic regression models, stratified by general antidepressant indication (general medical indication, specific psychiatric diagnosis, and non-specific psychiatric symptoms). Potential antidepressant overprescribing occurred in 24% of 3199 incident antidepressant prescriptions during the study period, and involved primarily newer antidepressants that were prescribed for non-specific psychiatric symptoms and subthreshold diagnoses. Potential antidepressant overprescribing was associated with nursing home residence, having a higher number of comorbid medical conditions and outpatient prescribers, taking more concomitant medications, having greater use of urgent or acute care services in the year preceding the index antidepressant prescription, and being prescribed antidepressants via telephone, e-mail, or patient portal. In conclusion, potential antidepressant overprescribing occurred in elderly persons and involved mainly newer antidepressants used for non-specific psychiatric symptoms and subthreshold diagnoses, and was associated with indicators of higher clinical complexity or severity and with prescribing without face-to-face patient contact.
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Affiliation(s)
- William V. Bobo
- Department of Psychiatry & PsychologyMayo ClinicJacksonvilleFlorida
| | - Brandon R. Grossardt
- Division of Biomedical Statistics and InformaticsDepartment of Health Sciences ResearchMayo ClinicRochesterMinnesota
| | - Maria I. Lapid
- Department of Psychiatry & PsychologyMayo ClinicRochesterMinnesota
| | | | - Cynthia Stoppel
- Department of Psychiatry & PsychologyMayo ClinicRochesterMinnesota
| | - Paul Y. Takahashi
- Department of Primary Care Internal MedicineMayo ClinicRochesterMinnesota
| | - Robert W. Hoel
- Department of Pharmacy ServicesMayo ClinicRochesterMinnesota
| | - Zheng Chang
- Department of Medical Epidemiology and BiostatisticsKarolinska InstitutetStockholmSweden
| | | | - Mohit Chauhan
- Department of Psychiatry & PsychologyMayo ClinicJacksonvilleFlorida
| | - Lee Flowers
- Department of Psychiatry & PsychologyMayo ClinicRochesterMinnesota
| | - Scott M. Brue
- Biomedical Informatics Support SystemMayo ClinicRochesterMinnesota
| | - Mark A. Frye
- Department of Psychiatry & PsychologyMayo ClinicRochesterMinnesota
| | - Jennifer St. Sauver
- Division of EpidemiologyDepartment of Health Sciences ResearchMayo ClinicRochesterMinnesota
| | - Walter A. Rocca
- Division of EpidemiologyDepartment of Health Sciences ResearchMayo ClinicRochesterMinnesota
- Department of NeurologyMayo ClinicRochesterMinnesota
| | - Bruce Sutor
- Department of Psychiatry & PsychologyMayo ClinicRochesterMinnesota
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Takahashi PY, Finnie DM, Quigg SM, Borkenhagen LS, Kumbamu A, Kimeu AK, Griffin JM. Understanding experiences of patients and family caregivers in the Mayo Clinic Care Transitions program: a qualitative study. Clin Interv Aging 2018; 14:17-25. [PMID: 30587950 PMCID: PMC6304078 DOI: 10.2147/cia.s183893] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [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] [Indexed: 11/23/2022] Open
Abstract
Background Care transitions programs are increasingly used to improve care and reduce re-admission of patients after hospitalization. To learn from the experience of patients who have participated in the Mayo Clinic Care Transitions (MCCT) program and to understand the patient experience, we sought perspectives of patients, caregivers, and providers who worked with participants of the MCCT program. Methods Investigators interviewed 17 patients and nine of their caregivers about their experience with the MCCT program. Eight health care providers described provider experiences with the MCCT program. Data from semistructured interviews were audio recorded, transcribed, and evaluated through content analysis. Inductive coding methods were used to elicit themes about patient experience with the MCCT program. Results Patients, caregivers, and providers emphasized that the MCCT program prevented hospitalizations and contributed to the health and quality of life of participants. All three stakeholder groups emphasized the value of the home visit and provision of the visit on a patient’s “home turf” as central to the program. Patients appreciated speaking to a provider without the stress and exertion of a trip to the clinic. Caregivers appreciated improved communication provided in the home visit and felt that home visits gave them peace of mind. Patients, caregivers, and providers also identified the need for improved phone triage and communication. Conclusion Patients, caregivers, and providers acknowledged the care transitions problem and emphasized the benefits of seeing patients on their home turf rather than in an office visit. This qualitative study of patient, caregiver, and provider experiences further validates the importance of the MCCT program.
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Affiliation(s)
- Paul Y Takahashi
- Department of Internal Medicine, Division of Primary Care Internal Medicine, Mayo Clinic, Rochester, MN, USA,
| | - Dawn M Finnie
- Robert D and Patricia E Kern Center for Science of Health Care Delivery, Mayo Clinic, Rochester, MN, USA
| | - Stephanie M Quigg
- Department of Internal Medicine, Division of Primary Care Internal Medicine, Mayo Clinic, Rochester, MN, USA,
| | - Lynn S Borkenhagen
- Department of Internal Medicine, Division of Primary Care Internal Medicine, Mayo Clinic, Rochester, MN, USA,
| | - Ashok Kumbamu
- Robert D and Patricia E Kern Center for Science of Health Care Delivery, Mayo Clinic, Rochester, MN, USA
| | - Ashley K Kimeu
- Department of Internal Medicine, Division of Primary Care Internal Medicine, Mayo Clinic, Rochester, MN, USA,
| | - Joan M Griffin
- Robert D and Patricia E Kern Center for Science of Health Care Delivery, Mayo Clinic, Rochester, MN, USA.,Department of Health Science Research, Division of Health Care Policy and Research, Mayo Clinic, Rochester, MN, USA
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Roelfsema F, Yang RJ, Takahashi PY, Erickson D, Bowers CY, Veldhuis JD. Aromatized Estrogens Amplify Nocturnal Growth Hormone Secretion in Testosterone-Replaced Older Hypogonadal Men. J Clin Endocrinol Metab 2018; 103:4419-4427. [PMID: 30032193 PMCID: PMC6212797 DOI: 10.1210/jc.2018-00755] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/06/2018] [Accepted: 06/20/2018] [Indexed: 11/19/2022]
Abstract
CONTEXT Testosterone (T) increases GH secretion in older men with a relative lack of T, in hypogonadal men of all ages, and in patients undergoing sex reassignment. The role of estradiol (E2) in men is less well defined. OBJECTIVE To assess the contribution of aromatization of T to spontaneous nocturnal and stimulated GH secretion. PARTICIPANTS Four groups of healthy older men (N = 74, age range 57 to 77 years) were studied. The gonadotropic axis was clamped with the gonadotropin-releasing hormone antagonist degarelix. Three groups received T and one group placebo addback. Two T-replaced groups were treated with anastrozole (an aromatase inhibitor) and either placebo or E2 addback. MAIN OUTCOME MEASURES Ten-minute GH concentration profiles were quantified by deconvolution analysis, after overnight (2200 to 0800 hours) sampling, and after combined IV injection of GHRH (0.3 µg/kg) and GHRH-2 (0.3 µg/kg) and withdrawal of a 2-hour somatostatin infusion (1 µg/kg/h). RESULTS E2 addback during aromatase inhibition increased basal (P = 0.046), pulsatile (P = 0.020), and total (P = 0.018) GH secretion by 60% to 70%. E2 did not potentiate GH secretory stimuli. Logarithmically transformed pulsatile GH secretion correlated strongly and positively with concurrent E2 concentrations overall (P = 0.028) and under anastrozole treatment (P = 0.005). CONCLUSION E2 administration in older men transdermally stimulates overnight pulsatile GH secretion. The exact site of E2 action cannot be ascertained from these experiments but may include hypothalamic loci involved in GH regulation, especially because GH secretagogue effects on somatotrope pituitary cells were not affected.
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Affiliation(s)
- Ferdinand Roelfsema
- Department of Internal Medicine, Section of Endocrinology and Metabolism, Leiden University Medical Center, Leiden, Netherlands
| | - Rebecca J Yang
- Endocrine Research Unit, Mayo School of Graduate Medical Education, Center for Translational Science Activities, Mayo Clinic, Rochester, Minnesota
| | - Paul Y Takahashi
- Department of Primary Care Internal Medicine, Mayo Clinic, Rochester, Minnesota
| | - Dana Erickson
- Division of Endocrinology, Mayo Clinic, Rochester, Minnesota
| | - Cyril Y Bowers
- Department of Internal Medicine, Tulane University Health Sciences Center, New Orleans, Louisiana
| | - Johannes D Veldhuis
- Endocrine Research Unit, Mayo School of Graduate Medical Education, Center for Translational Science Activities, Mayo Clinic, Rochester, Minnesota
- Correspondence and Reprint Requests: Johannes D. Veldhuis, MD, Endocrine Research Unit, Mayo Clinic, 200 First Street SW, Rochester, Minnesota 55905. E-mail:
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Chen CY, Naessens JM, Takahashi PY, McCoy RG, Borah BJ, Borkenhagen LS, Kimeu AK, Rojas RL, Johnson MG, Visscher SL, Cha SS, Thorsteinsdottir B, Hanson GJ. Improving Value of Care for Older Adults With Advanced Medical Illness and Functional Decline: Cost Analyses of a Home-Based Palliative Care Program. J Pain Symptom Manage 2018; 56:928-935. [PMID: 30165123 DOI: 10.1016/j.jpainsymman.2018.08.015] [Citation(s) in RCA: 8] [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] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/06/2018] [Revised: 08/16/2018] [Accepted: 08/16/2018] [Indexed: 01/07/2023]
Abstract
CONTEXT Identifying high-value health care delivery for patients with clinically complex and high-cost conditions is important for future reimbursement models. OBJECTIVES The objective of this study was to assess the Medicare reimbursement savings of an established palliative care homebound program. METHODS This is a retrospective cohort study involving 50 participants enrolled in a palliative care homebound program and 95 propensity-matched control patients at Mayo Clinic in Rochester, Minnesota, between September 1, 2012, and March 31, 2013. Total Medicare reimbursement was compared in the year before enrollment with the year after enrollment for participants and controls. RESULTS No significant differences were observed in demographic characteristics or prognostic indices between the two groups. Total Medicare reimbursement per program participant the year before program enrollment was $16,429 compared with $14,427 per control patient, resulting in $2004 higher charges per program patient. In 12 months after program enrollment, mean annual payment was $5783 per patient among participants and $22,031 per patient among the matched controls. In the second year, the intervention group had a decrease of $10,646 per patient; the control group had an increase of $7604 per patient. The difference between the participant group and control group was statistically significant (P < 0.001) and favored the palliative care homebound program enrollees by $18,251 (95% CI, $11,268-$25,234). CONCLUSION The Mayo Clinic Palliative Care Homebound Program reduced annual Medicare expenditures by $18,251 per program participant compared with matched control patients. This supports the role of home-based palliative medicine in delivering high-value care to high-risk older adults.
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Affiliation(s)
- Christina Y Chen
- Division of Community Internal Medicine, Mayo Clinic, Rochester, Minnesota, USA.
| | - James M Naessens
- Division of Health Care Policy and Research, Mayo Clinic, Rochester, Minnesota, USA; Robert D. and Patricia E. Kern Center for the Science of Healthcare Delivery, Mayo Clinic, Rochester, Minnesota, USA
| | - Paul Y Takahashi
- Division of Community Internal Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Rozalina G McCoy
- Division of Community Internal Medicine, Mayo Clinic, Rochester, Minnesota, USA; Division of Health Care Policy and Research, Mayo Clinic, Rochester, Minnesota, USA
| | - Bijan J Borah
- Division of Health Care Policy and Research, Mayo Clinic, Rochester, Minnesota, USA; Robert D. and Patricia E. Kern Center for the Science of Healthcare Delivery, Mayo Clinic, Rochester, Minnesota, USA
| | | | - Ashley K Kimeu
- Certified Nurse Practitioners, Mayo Clinic, Rochester, Minnesota, USA
| | - Ricardo L Rojas
- Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota, USA
| | - Matt G Johnson
- Robert D. and Patricia E. Kern Center for the Science of Healthcare Delivery, Mayo Clinic, Rochester, Minnesota, USA
| | - Sue L Visscher
- Robert D. and Patricia E. Kern Center for the Science of Healthcare Delivery, Mayo Clinic, Rochester, Minnesota, USA
| | - Stephen S Cha
- Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota, USA
| | | | - Gregory J Hanson
- Division of Community Internal Medicine, Mayo Clinic, Rochester, Minnesota, USA; Mayo Center for Palliative Medicine, Mayo Clinic, Rochester, Minnesota, USA
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Roelfsema F, Yang RJ, Liu PY, Takahashi PY, Veldhuis JD. Feedback on LH in Testosterone-Clamped Men Depends on the Mode of Testosterone Administration and Body Composition. J Endocr Soc 2018; 3:235-249. [PMID: 30623162 PMCID: PMC6320245 DOI: 10.1210/js.2018-00317] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/28/2018] [Accepted: 11/20/2018] [Indexed: 11/19/2022] Open
Abstract
Context Quantitative studies of the short-term feedback of testosterone (T) on luteinizing hormone (LH) secretion in healthy men are relatively rare. Such studies require the shutting down of endogenous T secretion and the imposition of experimentally controlled IV T addback. Objective To evaluate whether pulsatile and continuous T delivery confers equivalent negative feedback on LH secretion. Design This was a placebo-controlled, blinded, and prospectively randomized crossover study comprising 16 healthy men [age range 23 to 54 years and a body mass index (BMI) between 22.3 and 34.2 kg/m2]. Subjects received ketoconazole to block endogenous T secretion and received continuous or 90-minute pulses of IV T addback. Setting The study was performed in a Clinical Translational Research Unit. Interventions Subjects underwent 14 hours of blood sampling at 10-minute intervals, with a bolus IV injection of 33 ng/kg gonadotropin-releasing hormone (GnRH). Main Outcome Measures Log-transformed LH and T concentration ratios before and after GnRH administration. Results Despite higher T concentrations during pulsatile T feedback, LH concentrations and secretion rates, whether driven by endogenous or exogenous GnRH, were similar to those during continuous T infusion, indicating diminished pulsatile T feedback. Feedback correlated negatively with BMI. Under controlled T feedback, basal but not pulsatile LH secretion correlated negatively with CT-estimated visceral fat mass. Conclusion Feedback by pulsatile T delivery has diminished inhibitory strength compared with continuous infusion. Feedback is negatively correlated with BMI.
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Affiliation(s)
- Ferdinand Roelfsema
- Department of Internal Medicine, Section Endocrinology and Metabolism, Leiden University Medical Center, Leiden, Netherlands
| | - Rebecca J Yang
- Endocrine Research Unit, Mayo School of Graduate Medical Education, Center for Translational Science Activities, Mayo Clinic, Rochester, Minnesota
| | - Peter Y Liu
- Endocrine Research Unit, Mayo School of Graduate Medical Education, Center for Translational Science Activities, Mayo Clinic, Rochester, Minnesota.,Division of Endocrinology, Department of Medicine, David Geffen School of Medicine at University of California Los Angeles, Harbor-University of California Los Angeles Medical Center, and Los Angeles Biomedical Research Institute, Los Angeles, California
| | - Paul Y Takahashi
- Department of Primary Care Internal Medicine, Mayo Clinic, Rochester, Minnesota
| | - Johannes D Veldhuis
- Endocrine Research Unit, Mayo School of Graduate Medical Education, Center for Translational Science Activities, Mayo Clinic, Rochester, Minnesota
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Takahashi PY, Jenkins GD, Welkie BP, McDonnell SK, Evans JM, Cerhan JR, Olson JE, Thibodeau SN, Cicek MS, Ryu E. Association of mitochondrial DNA copy number with self-rated health status. Appl Clin Genet 2018; 11:121-127. [PMID: 30498369 PMCID: PMC6207265 DOI: 10.2147/tacg.s167640] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Purpose In aging adults, mitochondrial dysfunction may be an important contributor. We evaluated the association between mitochondrial DNA (mtDNA) copy number, which is a biomarker for mitochondrial function, and self-rated health status. Patients and methods We conducted a cross-sectional study of patients enrolled within the Mayo Clinic Biobank. We utilized the questionnaire and sequence data from 944 patients. We examined the association between mtDNA copy number and self-rated health status with 3 collapsed categories for the latter variable (excellent/very good, good, and fair/poor). For analysis, we used proportional odds models after log-transforming mtDNA copy number, and we adjusted for age and sex. Results We found the median age at enrollment was 61 years (25th–75th percentile: 51–71), and 64% reported excellent or very good health, 31% reported good health, and 6% reported fair/poor health. Overall, the median mtDNA copy number was 88.9 (25th–75th percentile: 77.6–101.1). Higher mtDNA copy number was found for subjects reporting better self-rated health status after adjusting for age, sex, and comorbidity burden (OR =2.3 [95% CI: 1.2–4.5] for having better self-rated health for a one-unit increase in log-transformed mtDNA copy number). Conclusion We found that a higher mtDNA copy number is associated with better self-rated health status after adjustment for age, sex, and comorbidity burden. The current study implies that mtDNA copy number may serve as a biomarker for self-reported health. Further studies, potentially including cohort studies, may be required.
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Affiliation(s)
| | | | | | | | | | | | | | - Stephen N Thibodeau
- Department of laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA
| | - Mine S Cicek
- Department of laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA
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