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      Effectiveness of Telemedicine Visits in Reducing 30‐Day Readmissions Among Patients With Heart Failure During the COVID‐19 Pandemic

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          Abstract

          Background

          The COVID‐19 pandemic resulted in a rapid implementation of telemedicine into clinical practice. This study examined whether early outpatient follow‐up via telemedicine is as effective as in‐person visits for reducing 30‐day readmissions in patients with heart failure.

          Methods and Results

          Using electronic health records from a large health system, we included patients with heart failure living in North Carolina (N=6918) who were hospitalized between March 16, 2020 and March 14, 2021. All‐cause readmission within 30 days after discharge was examined using weighted logistic regression models. Overall, 7.6% (N=526) of patients received early telemedicine follow‐up, 38.8% (N=2681) received early in‐person follow‐up, and 53.6% (N=3711) did not receive follow‐up within 14 days of discharge. Compared with patients without early follow‐up, those who received early follow‐up were younger, were more likely to be Medicare beneficiaries, had more comorbidities, and were less likely to live in an disadvantaged neighborhood. Relative to in‐person visits, those with telemedicine follow‐up were of similar age, sex, and race but with generally fewer comorbidities. Overall, the 30‐day readmission rate (19.0%) varied among patients who received telemedicine visits (15.0%), in‐person visits (14.0%), or no follow‐up (23.1%). After covariate adjustment, patients who received either telemedicine (odds ratio [OR], 0.55; 95% CI, 0.44–0.72) or in‐person (OR, 0.52; 95% CI, 0.45–0.60) visits were similarly less likely to be readmitted within 30 days compared with patients with no follow‐up.

          Conclusions

          During the COVID‐19 pandemic, the use of telemedicine visits for early follow‐up increased rapidly. Patients with heart failure who received outpatient follow‐up either via telemedicine or in‐person had better outcomes than those who received no follow‐up.

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          Most cited references47

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          Heart Disease and Stroke Statistics—2021 Update: A Report From the American Heart Association

          The American Heart Association, in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, diet, and weight) and health factors (cholesterol, blood pressure, and glucose control) that contribute to cardiovascular health. The Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, congenital heart disease, rhythm disorders, subclinical atherosclerosis, coronary heart disease, heart failure, valvular disease, venous disease, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs). The American Heart Association, through its Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States to provide the most current information available in the annual Statistical Update. The 2021 Statistical Update is the product of a full year’s worth of effort by dedicated volunteer clinicians and scientists, committed government professionals, and American Heart Association staff members. This year’s edition includes data on the monitoring and benefits of cardiovascular health in the population, an enhanced focus on social determinants of health, adverse pregnancy outcomes, vascular contributions to brain health, the global burden of cardiovascular disease, and further evidence-based approaches to changing behaviors related to cardiovascular disease. Each of the 27 chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics. The Statistical Update represents a critical resource for the lay public, policy makers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.
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            Balance diagnostics for comparing the distribution of baseline covariates between treatment groups in propensity-score matched samples

            The propensity score is a subject's probability of treatment, conditional on observed baseline covariates. Conditional on the true propensity score, treated and untreated subjects have similar distributions of observed baseline covariates. Propensity-score matching is a popular method of using the propensity score in the medical literature. Using this approach, matched sets of treated and untreated subjects with similar values of the propensity score are formed. Inferences about treatment effect made using propensity-score matching are valid only if, in the matched sample, treated and untreated subjects have similar distributions of measured baseline covariates. In this paper we discuss the following methods for assessing whether the propensity score model has been correctly specified: comparing means and prevalences of baseline characteristics using standardized differences; ratios comparing the variance of continuous covariates between treated and untreated subjects; comparison of higher order moments and interactions; five-number summaries; and graphical methods such as quantile–quantile plots, side-by-side boxplots, and non-parametric density plots for comparing the distribution of baseline covariates between treatment groups. We describe methods to determine the sampling distribution of the standardized difference when the true standardized difference is equal to zero, thereby allowing one to determine the range of standardized differences that are plausible with the propensity score model having been correctly specified. We highlight the limitations of some previously used methods for assessing the adequacy of the specification of the propensity-score model. In particular, methods based on comparing the distribution of the estimated propensity score between treated and untreated subjects are uninformative. Copyright © 2009 John Wiley & Sons, Ltd.
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              Moving towards best practice when using inverse probability of treatment weighting (IPTW) using the propensity score to estimate causal treatment effects in observational studies

              The propensity score is defined as a subject's probability of treatment selection, conditional on observed baseline covariates. Weighting subjects by the inverse probability of treatment received creates a synthetic sample in which treatment assignment is independent of measured baseline covariates. Inverse probability of treatment weighting (IPTW) using the propensity score allows one to obtain unbiased estimates of average treatment effects. However, these estimates are only valid if there are no residual systematic differences in observed baseline characteristics between treated and control subjects in the sample weighted by the estimated inverse probability of treatment. We report on a systematic literature review, in which we found that the use of IPTW has increased rapidly in recent years, but that in the most recent year, a majority of studies did not formally examine whether weighting balanced measured covariates between treatment groups. We then proceed to describe a suite of quantitative and qualitative methods that allow one to assess whether measured baseline covariates are balanced between treatment groups in the weighted sample. The quantitative methods use the weighted standardized difference to compare means, prevalences, higher‐order moments, and interactions. The qualitative methods employ graphical methods to compare the distribution of continuous baseline covariates between treated and control subjects in the weighted sample. Finally, we illustrate the application of these methods in an empirical case study. We propose a formal set of balance diagnostics that contribute towards an evolving concept of ‘best practice’ when using IPTW to estimate causal treatment effects using observational data. © 2015 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.
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                Author and article information

                Contributors
                hanzhang.xu@duke.edu
                Journal
                J Am Heart Assoc
                J Am Heart Assoc
                10.1002/(ISSN)2047-9980
                JAH3
                ahaoa
                Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease
                John Wiley and Sons Inc. (Hoboken )
                2047-9980
                01 March 2022
                05 April 2022
                : 11
                : 7 ( doiID: 10.1002/jah3.v11.7 )
                : e023935
                Affiliations
                [ 1 ] Department of Family Medicine and Community Health Duke University Durham NC
                [ 2 ] Duke University School of Nursing Duke University Durham NC
                [ 3 ] Center for the Study of Aging and Human Development Duke University Durham NC
                [ 4 ] Department of Population Health Sciences Duke University Durham NC
                [ 5 ] Office of the Provost University of Texas Southwestern Medical Dallas TX
                [ 6 ] Department of Internal Medicine University of Texas Southwestern Medical Dallas TX
                [ 7 ] Duke Clinical Research Institute Duke University Durham NC
                [ 8 ] Department of Sociology Duke University Durham NC
                Author notes
                [*] [* ] Correspondence to: Hanzhang Xu, PhD, Department of Family Medicine and Community Health, Duke University Medical Center, P.O. Box 104006, Durham, NC 27710. Email: hanzhang.xu@ 123456duke.edu

                Author information
                https://orcid.org/0000-0001-9617-247X
                https://orcid.org/0000-0003-0828-6851
                https://orcid.org/0000-0002-5393-6246
                https://orcid.org/0000-0002-5415-4721
                https://orcid.org/0000-0002-0976-4715
                Article
                JAH37249
                10.1161/JAHA.121.023935
                9075458
                35229656
                6d2a0796-19cf-4ba5-b561-37583f730e20
                © 2022 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.

                History
                : 05 October 2021
                : 01 February 2022
                Page count
                Figures: 3, Tables: 3, Pages: 14, Words: 10227
                Funding
                Funded by: National Institute on Aging , doi 10.13039/100000049;
                Award ID: R21AG061142
                Award ID: R03AG064303
                Funded by: National Institute on Minority Health and Health Disparities , doi 10.13039/100006545;
                Award ID: U54MD012530
                Funded by: National Heart, Lung, and Blood Institute , doi 10.13039/100000050;
                Award ID: K12HL138030
                Categories
                Original Research
                Original Research
                Health Services and Outcomes Research
                Custom metadata
                2.0
                April 5, 2022
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.1.3 mode:remove_FC converted:05.04.2022

                Cardiovascular Medicine
                electronic health records,heart failure,hospitalization,telemedicine,health services,quality and outcomes

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