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Development and Validation of an Acute Heart Failure-Specific Mortality Predictive Model Based on Administrative Data
Journal article   Open access   Peer reviewed

Development and Validation of an Acute Heart Failure-Specific Mortality Predictive Model Based on Administrative Data

Noriko Sasaki, Jason Lee, Sungchul Park, Takeshi Umegaki, Susumu Kunisawa, Tetsuya Otsubo, Hiroshi Ikai and Yuichi Imanaka
Canadian journal of cardiology, v 29(9), pp 1055-1061
01 Sep 2013
DOI: https://doi.org/10.1016/j.cjca.2012.11.021
PMID: 23395282
Featured in Collection :   UN Sustainable Development Goals @ Drexel
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https://doi.org/10.1016/j.cjca.2012.11.021View
Published, Version of Record (VoR)Open Access (License Unspecified),  Open

Abstract

Cardiac & Cardiovascular Systems Cardiovascular System & Cardiology Life Sciences & Biomedicine Science & Technology
Background: Acute heart failure (AHF) with its high in-hospital mortality is an increasing burden on healthcare systems worldwide, and comparing hospital performance is required for improving hospital management efficiency. However, it is difficult to distinguish patient severity from individual hospital care effects. The aim of this study was to develop a risk adjustment model to predict in-hospital mortality for AHF using routinely available administrative data. Methods: Administrative data were extracted from 86 acute care hospitals in Japan. We identified 8620 hospitalized patients with AHF from April 2010 to March 2011. Multivariable logistic regression analyses were conducted to analyze various patient factors that might affect mortality. Two predictive models (models 1 and 2; without and with New York Heart Association functional class, respectively) were developed and bootstrapping was used for internal validation. Expected mortality rates were then calculated for each hospital by applying model 2. Results: The overall in-hospital mortality rate was 7.1%. Factors independently associated with higher in-hospital mortality included advanced age, New York Heart Association class, and severe respiratory failure. In contrast, comorbid hypertension, ischemic heart disease, and atrial fibrillation/flutter were found to be associated with lower in-hospital mortality. Both model 1 and model 2 demonstrated good discrimination with c-statistics of 0.76 (95% confidence interval, 0.74-0.78) and 0.80 (95% confidence interval, 0.78-0.82), respectively, and good calibration after bootstrap correction, with better results in model 2. Conclusions: Factors identifiable from administrative data were able to accurately predict in-hospital mortality. Application of our model might facilitate risk adjustment for AHF and can contribute to hospital evaluations.

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20 citations in Web of Science
20 citations in Scopus

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Web of Science research areas
Cardiac & Cardiovascular Systems
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