Roles of nonclinical and clinical data in prediction of 30-day rehospitalization or death among heart failure patients

Journal of Cardiac Failure
Quan L HuynhMARATHON Investigators

Abstract

Selecting heart failure (HF) patients for intensive management to reduce readmissions requires effective targeting. However, available prediction scores are only modestly effective. We sought to develop a prediction score for 30-day all-cause rehospitalization or death in HF with the use of nonclinical and clinical data. This statewide data linkage included all patients who survived their 1st HF admission (with either reduced or preserved ejection fraction) to a Tasmanian public hospital during 2009-2012. Nonclinical data (n = 1,537; 49.5% men, median age 80 y) included administrative, socioeconomic, and geomapping data. Clinical data before discharge were available from 977 patients. Prediction models were developed and internally and externally validated. Within 30 days of discharge, 390 patients (25.4%) died or were rehospitalized. The nonclinical model (length of hospital stay, age, living alone, discharge during winter, remoteness index, comorbidities, and sex) had fair discrimination (C-statistic 0.66 [95% confidence interval (CI) 0.63-0.69]). Clinical data (blood urea nitrogen, New York Heart Association functional class, albumin, heart rate, respiratory rate, diuretic use, angiotensin-converting enzyme inhibitor use, ar...Continue Reading

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Citations

Feb 26, 2016·Journal of the American Medical Informatics Association : JAMIA·R Scott EvansDonald L Lappé
Aug 18, 2016·Western Journal of Nursing Research·Matthew CarazoVictoria Vaughan Dickson
Apr 15, 2017·International Journal of Cardiology·Francesc FormigaXavier Corbella
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Jul 3, 2020·Journal of Multidisciplinary Healthcare·Afsaneh RoshanghalbEmanuele Lettieri
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Mar 18, 2021·Medicine·Mahmoud El IskandaraniMarwan M Refaat
Aug 5, 2021·BMJ Open·Lisa Grossman LiuHojjat Salmasian
Sep 3, 2021·Journal of the American Heart Association·Kentaro JujoYuya Matsue

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