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      Real world heart failure epidemiology and outcome: A population-based analysis of 88,195 patients

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          Abstract

          Background

          Heart failure (HF) is frequent and its prevalence is increasing. We aimed to evaluate the epidemiologic features of HF patients, the 1-year follow-up outcomes and the independent predictors of those outcomes at a population level.

          Methods and results

          Population-based longitudinal study including all prevalent HF cases in Catalonia (Spain) on December 31st, 2012. Patients were divided in 3 groups: patients without a previous HF hospitalization, patients with a remote (>1 year) HF hospitalization and patients with a recent (<1 year) HF admission. We analyzed 1year all-cause and HF hospitalizations, and all-cause mortality. Logistic regression was used to identify the independent predictors of each of those outcomes. A total of 88,195 patients were included. Mean age was 77 years, 55% were women. Comorbidities were frequent. Fourteen percent of patients had never been hospitalized, 71% had a remote HF hospitalization and 15% a recent hospitalization. At 1-year follow-up, all-cause and HF hospitalization were 53% and 8.8%, respectively. One-year all-cause mortality rate was 14%, and was higher in patients with a recent HF hospitalization (24%). The presence of diabetes mellitus, atrial fibrillation or chronic kidney disease was independently associated with all-cause and HF hospitalization and all-cause mortality. Hospital admissions and emergency department visits the previous year were also found to be independently associated with the three study outcomes.

          Conclusions

          Outcomes are different depending on the HF population studied. Some comorbidity, an all-cause hospitalization or emergency department visit the previous year were associated with a worse outcome.

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          Most cited references40

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          The meaning and use of the area under a receiver operating characteristic (ROC) curve.

          A representation and interpretation of the area under a receiver operating characteristic (ROC) curve obtained by the "rating" method, or by mathematical predictions based on patient characteristics, is presented. It is shown that in such a setting the area represents the probability that a randomly chosen diseased subject is (correctly) rated or ranked with greater suspicion than a randomly chosen non-diseased subject. Moreover, this probability of a correct ranking is the same quantity that is estimated by the already well-studied nonparametric Wilcoxon statistic. These two relationships are exploited to (a) provide rapid closed-form expressions for the approximate magnitude of the sampling variability, i.e., standard error that one uses to accompany the area under a smoothed ROC curve, (b) guide in determining the size of the sample required to provide a sufficiently reliable estimate of this area, and (c) determine how large sample sizes should be to ensure that one can statistically detect differences in the accuracy of diagnostic techniques.
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            Comorbidity measures for use with administrative data.

            This study attempts to develop a comprehensive set of comorbidity measures for use with large administrative inpatient datasets. The study involved clinical and empirical review of comorbidity measures, development of a framework that attempts to segregate comorbidities from other aspects of the patient's condition, development of a comorbidity algorithm, and testing on heterogeneous and homogeneous patient groups. Data were drawn from all adult, nonmaternal inpatients from 438 acute care hospitals in California in 1992 (n = 1,779,167). Outcome measures were those commonly available in administrative data: length of stay, hospital charges, and in-hospital death. A comprehensive set of 30 comorbidity measures was developed. The comorbidities were associated with substantial increases in length of stay, hospital charges, and mortality both for heterogeneous and homogeneous disease groups. Several comorbidities are described that are important predictors of outcomes, yet commonly are not measured. These include mental disorders, drug and alcohol abuse, obesity, coagulopathy, weight loss, and fluid and electrolyte disorders. The comorbidities had independent effects on outcomes and probably should not be simplified as an index because they affect outcomes differently among different patient groups. The present method addresses some of the limitations of previous measures. It is based on a comprehensive approach to identifying comorbidities and separates them from the primary reason for hospitalization, resulting in an expanded set of comorbidities that easily is applied without further refinement to administrative data for a wide range of diseases.
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              The global health and economic burden of hospitalizations for heart failure: lessons learned from hospitalized heart failure registries.

              Heart failure is a global pandemic affecting an estimated 26 million people worldwide and resulting in more than 1 million hospitalizations annually in both the United States and Europe. Although the outcomes for ambulatory HF patients with a reduced ejection fraction (EF) have improved with the discovery of multiple evidence-based drug and device therapies, hospitalized heart failure (HHF) patients continue to experience unacceptably high post-discharge mortality and readmission rates that have not changed in the last 2 decades. In addition, the proportion of HHF patients classified as having a preserved EF continues to grow and may overtake HF with a reduced EF in the near future. However, the prognosis for HF with a preserved EF is similar and there are currently no available disease-modifying therapies. HHF registries have significantly improved our understanding of this clinical entity and remain an important source of data shaping both public policy and research efforts. The authors review global HHF registries to describe the patient characteristics, management, outcomes and their predictors, quality improvement initiatives, regional differences, and limitations of the available data. Moreover, based on the lessons learned, they also propose a roadmap for the design and conduct of future HHF registries. Copyright © 2014 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                24 February 2017
                2017
                : 12
                : 2
                : e0172745
                Affiliations
                [1 ]Heart Failure Programme, Department of Cardiology, Hospital del Mar, Barcelona, Spain
                [2 ]Heart Diseases Biomedical Research Group, IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
                [3 ]School of Medicine, Universitat Autònoma de Barcelona, Barcelona, Spain
                [4 ]Analysis on Demand and Activity Division, Catalan Health Service, Barcelona, Spain
                [5 ]Ciccarone Center for the Prevention of Heart Disease, Department of Cardiology, Johns Hopkins Medical Institutions, Baltimore, Maryland, United States of America
                [6 ]Welch Center for Prevention, Epidemiology and Clinical Research, Johns Hopkins University, Baltimore, Maryland, United States of America
                [7 ]Jordi Gol Primary Care Research Institute, Catalan Institute of Heath, Barcelona, Spain
                [8 ]Heart Failure Program, Cardiology Department, University Hospital Bellvitge, Hospitalet de Llobregat, Barcelona, Spain
                [9 ]School of Medicine, Department of Clinical Science, University of Barcelona, Hospitalet de Llobregat, Barcelona, Spain
                [10 ]IDIBELL (Bellvitge Biomedical Research Institute), Hospitalet de Llobregat, Barcelona, Spain
                Azienda Ospedaliero Universitaria Careggi, ITALY
                Author notes

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

                • Conceptualization: NF JCC EV MC JMVR MCA.

                • Data curation: EV MC MB.

                • Formal analysis: EV MC MB.

                • Investigation: EV MC MB.

                • Methodology: EV MC MB NF JCC.

                • Project administration: NF JCC EV MC.

                • Supervision: JCC MB.

                • Visualization: EV MC SR PM.

                • Writing – original draft: NF JCC EV MB CE.

                • Writing – review & editing: NF EV MC MB MCA PM SR JMVR JCC CE.

                Author information
                http://orcid.org/0000-0003-3110-6572
                Article
                PONE-D-16-47754
                10.1371/journal.pone.0172745
                5325273
                28235067
                9cb08bff-7326-4537-b545-d6968563ed7a
                © 2017 Farré et al

                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
                : 2 December 2016
                : 8 February 2017
                Page count
                Figures: 1, Tables: 3, Pages: 13
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/100009664, Sociedad Española de Cardiología;
                Award ID: Research grant
                Award Recipient :
                Miguel Cainzos-Achirica was funded by a research grant from the Spanish Society of Cardiology.
                Categories
                Research Article
                Medicine and Health Sciences
                Cardiology
                Heart Failure
                Medicine and Health Sciences
                Health Care
                Health Care Facilities
                Hospitals
                Hospitalizations
                People and Places
                Demography
                Death Rates
                Medicine and Health Sciences
                Critical Care and Emergency Medicine
                Medicine and Health Sciences
                Nephrology
                Chronic Kidney Disease
                Medicine and Health Sciences
                Endocrinology
                Endocrine Disorders
                Diabetes Mellitus
                Medicine and Health Sciences
                Metabolic Disorders
                Diabetes Mellitus
                Medicine and Health Sciences
                Pulmonology
                Chronic Obstructive Pulmonary Disease
                Medicine and Health Sciences
                Cardiology
                Arrhythmia
                Atrial Fibrillation
                Custom metadata
                Data are not included in this submission due to legal and privacy stipulations from the CatSalut (Catalan Health Department). Data are available to those completing the request for research identifiable files from the CatSalut upon acceptance by the Catalan Health Department. For contact, please reach the Analysis on Demand and Activity Division, Catalan Health Service (Mr. Emili Vela, evela@ 123456catsalut.cat ).

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                Uncategorized

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