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Abstract

Existing prediction algorithms for the identification of patients with heart failure (HF) at high risk of readmission or death after hospital discharge are only modestly effective. We sought to validate a recently developed predictive model of 30-day readmission or death in HF using an Australia-wide sample of patients. This study used data from 1,046 patients with HF at teaching hospitals in 5 Australian capital cities to validate a predictive model of 30-day readmission or death in HF. Besides standard clinical and administrative data, we collected data on individual sociodemographic and socioeconomic status, mental health (Patient Health Questionnaire [PHQ]-9 and Generalized Anxiety Disorder [GAD]-7 scale score), cognitive function (Montreal Cognitive Assessment [MoCA] score), and 2-dimensional echocardiograms. The original sample used to develop the predictive model and the validation sample had similar proportions of patients with an adverse event within 30 days (30% vs 29%, p = 0.35) and 90 days (52% vs 49%, p = 0.36). Applying the predicted risk score to the validation sample provided very good discriminatory power (C-statistic = 0.77) in the prediction of 30-day readmission or death. This discrimination was greater for predicting 30-day death (C-statistic = 0.85) than for predicting 30-day readmission (C-statistic = 0.73). There was a small difference in the performance of the predictive model among patients with either a left ventricular ejection fraction of <40% or a left ventricular ejection fraction of ≥40%, but an attenuation in discrimination when used to predict longer-term adverse outcomes. In conclusion, our findings confirm the generalizability of the predictive model that may be a powerful tool for targeting high-risk patients with HF for intensive management.

Item Type:	Article
Authors/Creators:	Huynh, Q and Negishi, K and De Pasquale, CG and Hare, JL and Leung, D and Stanton, T and Marwick, TH
Journal or Publication Title:	American Journal of Cardiology
Publisher:	Excerpta Medica Inc
ISSN:	0002-9149
DOI / ID Number:	https://doi.org/10.1016/j.amjcard.2017.10.031
Copyright Information:	© 2017 Elsevier Inc. All rights reserved.
Related URLs:	UTAS Profile: Huynh, Q Google Scholar: Negishi, K UTAS Profile: Negishi, K
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