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

Huynh, Q ORCID: 0000-0003-1368-5160, Negishi, K ORCID: 0000-0002-9086-2565, De Pasquale, CG, Hare, JL, Leung, D, Stanton, T and Marwick, TH 2018 , 'Validation of predictive score of 30-day hospital readmission or death in patients with heart failure' , American Journal of Cardiology, vol. 121, no. 3 , pp. 322-329 , doi: 10.1016/j.amjcard.2017.10.031.

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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: 10.1016/j.amjcard.2017.10.031
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© 2017 Elsevier Inc. All rights reserved.

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