Predicting 1 year readmission for heart failure: A comparative study of machine learning and the LACE index.

ABSTRACT:

Aims

There is a lack of tools for accurately identifying the risk of readmission for heart failure in elderly patients with arrhythmia. The aim of this study was to establish and compare the performance of the LACE [length of stay ('L'), acute (emergent) admission ('A'), Charlson comorbidity index ('C') and visits to the emergency department during the previous 6 months ('E')] index and machine learning in predicting 1 year readmission for heart failure in elderly patients with arrhythmia.

Methods

Elderly patients with arrhythmia who were hospitalized at Sichuan Provincial People's Hospital between 1 June 2018 and 31 May 2020 were enrolled. The LACE index was calculated for each patient, and the area under the receiver operating characteristic curve (AUROC) was calculated. S

SUBMITTER: Song X 

PROVIDER: S-EPMC11424322 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature