Development and internal validation of a clinical prediction model to predict independence in daily living at discharge for patients with heart failure: analysis using a Japanese national inpatient database real-world dataset.

Abstract

Purpose: To develop a clinical prediction model (CPM) to predict independence in activities of daily living (ADLs) in patients with heart failure.

Subjects and methods: We collected the data of the individuals who were admitted and rehabilitated for heart failure from January 2017 to June 2022 from Japan's Diagnosis Procedure Combination database. We assessed the subjects' ADLs at discharge using the Barthel Index and classified them into independence, partial-independence, and total-dependence groups based on their ADLs at discharge. Two CPMs (an independence model and a partial-independence model) were developed by a binomial logistic regression analysis. The predictors included subject characteristics, treatment, and post-hospitalization disease onset. The CPMs' accuracy was validated by the area under the curve (AUC). Internal validation was performed using the bootstrap method. The final CPM is presented in a nomogram.

Results: We included 96,753 patients whose ADLs could be traced at discharge. The independence model had a 0.73 mean AUC and a 1.0 slope at bootstrapping. We thus developed a simplified model using nomograms, which also showed adequate predictive accuracy in the independence model. The partial-independence model had a 0.65 AUC and inadequate predictive accuracy.

Conclusions: The independence model of ADLs in patients with heart failure is a useful CPM.