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Validation of predictive score of 30-day hospital readmission or death in patients with heart failure

Citation

Huynh, Q and Negishi, K and De Pasquale, CG and Hare, JL and Leung, D and Stanton, T and Marwick, TH, Validation of predictive score of 30-day hospital readmission or death in patients with heart failure, American Journal of Cardiology, 121, (3) pp. 322-329. ISSN 0002-9149 (2018) [Refereed Article]

Copyright Statement

2017 Elsevier Inc. All rights reserved.

DOI: doi:10.1016/j.amjcard.2017.10.031

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 Details

Item Type:Refereed Article
Keywords:hospital readmission, death, heart failure, predictive models
Research Division:Medical and Health Sciences
Research Group:Cardiorespiratory Medicine and Haematology
Research Field:Cardiology (incl. Cardiovascular Diseases)
Objective Division:Health
Objective Group:Clinical Health (Organs, Diseases and Abnormal Conditions)
Objective Field:Cardiovascular System and Diseases
UTAS Author:Huynh, Q (Dr Quan Huynh)
UTAS Author:Negishi, K (Dr Kazuaki Negishi)
ID Code:125123
Year Published:2018
Web of Science® Times Cited:4
Deposited By:Menzies Institute for Medical Research
Deposited On:2018-03-28
Last Modified:2019-02-18
Downloads:0

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