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      Efficacy of Hospital at Home in Patients with Heart Failure: A Systematic Review and Meta-Analysis

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          Abstract

          Background

          Heart failure (HF) is the commonest cause of hospitalization in older adults. Compared to routine hospitalization (RH), hospital at home (HaH)—substitutive hospital-level care in the patient’s home—improves outcomes and reduces costs in patients with general medical conditions. The efficacy of HaH in HF is unknown.

          Methods and Results

          We searched MEDLINE, Embase, CINAHL, and CENTRAL, for publications from January 1990 to October 2014. We included prospective studies comparing substitutive models of hospitalization to RH in HF. At least 2 reviewers independently selected studies, abstracted data, and assessed quality. We meta-analyzed results from 3 RCTs (n = 203) and narratively synthesized results from 3 observational studies (n = 329). Study quality was modest. In RCTs, HaH increased time to first readmission (mean difference (MD) 14.13 days [95% CI 10.36 to 17.91]), and improved health-related quality of life (HrQOL) at both, 6 months (standardized MD (SMD) -0.31 [-0.45 to -0.18]) and 12 months (SMD -0.17 [-0.31 to -0.02]). In RCTs, HaH demonstrated a trend to decreased readmissions (risk ratio (RR) 0.68 [0.42 to 1.09]), and had no effect on all-cause mortality (RR 0.94 [0.67 to 1.32]). HaH decreased costs of index hospitalization in all RCTs. HaH reduced readmissions and emergency department visits per patient in all 3 observational studies.

          Conclusions

          In the context of a limited number of modest-quality studies, HaH appears to increase time to readmission, reduce index costs, and improve HrQOL among patients requiring hospital-level care for HF. Larger RCTs are necessary to assess the effect of HaH on readmissions, mortality, and long-term costs.

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          Most cited references22

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          What is missing from descriptions of treatment in trials and reviews?

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            A comparison of statistical methods for meta-analysis.

            Meta-analysis may be used to estimate an overall effect across a number of similar studies. A number of statistical techniques are currently used to combine individual study results. The simplest of these is based on a fixed effects model, which assumes the true effect is the same for all studies. A random effects model, however, allows the true effect to vary across studies, with the mean true effect the parameter of interest. We consider three methods currently used for estimation within the framework of a random effects model, and illustrate them by applying each method to a collection of six studies on the effect of aspirin after myocardial infarction. These methods are compared using estimated coverage probabilities of confidence intervals for the overall effect. The techniques considered all generally have coverages below the nominal level, and in particular it is shown that the commonly used DerSimonian and Laird method does not adequately reflect the error associated with parameter estimation, especially when the number of studies is small. Copyright 2001 John Wiley & Sons, Ltd.
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              Random-effects meta-analysis of inconsistent effects: a time for change.

              A primary goal of meta-analysis is to improve the estimation of treatment effects by pooling results of similar studies. This article explains how the most widely used method for pooling heterogeneous studies--the Der Simonian-Laird (DL) estimator--can produce biased estimates with falsely high precision. A classic example is presented to show that use of the DL estimator can lead to erroneous conclusions. Particular problems with the DL estimator are discussed, and several alternative methods for summarizing heterogeneous evidence are presented. The authors support replacing universal use of the DL estimator with analyses based on a critical synthesis that recognizes the uncertainty in the evidence,focuses on describing and explaining the probable sources of variation in the evidence, and uses random-effects estimates that provide more accurate confidence limits than the DL estimator.

                Author and article information

                Contributors
                Role: Academic Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                8 June 2015
                2015
                : 10
                : 6
                : e0129282
                Affiliations
                [1 ]Department of Medicine, Queen’s University, Kingston, Ontario, Canada
                [2 ]Department of Medicine, McMaster University, Hamilton, Ontario, Canada
                [3 ]Dalla Lana School of Public Health, Division of Epidemiology, University of Toronto, Toronto, Ontario, Canada
                [4 ]Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, Ontario, Canada
                [5 ]Population Health Research Institute, McMaster University, Hamilton, Ontario, Canada
                University of Naples Federico II, ITALY
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: HV AQ PYA. Performed the experiments: AQ PYA HV. Analyzed the data: AQ PYA CK HV LT. Contributed reagents/materials/analysis tools: HV CK. Wrote the paper: AQ PYA CK HV RBH SJC.

                Article
                PONE-D-15-01172
                10.1371/journal.pone.0129282
                4460137
                26052944
                33edc522-cda9-4982-b452-3494ab3b2a13
                Copyright @ 2015

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

                History
                : 9 January 2015
                : 6 May 2015
                Page count
                Figures: 4, Tables: 2, Pages: 15
                Funding
                This work was supported by the EJ Moran Campbell Award. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Custom metadata
                All relevant data are within the paper and its Supporting Information files.

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