Established machine learning models to predict readmission for elderly patients with ischemic heart disease.

Abstract

Background: The contribution of clinical features associated with 30-day or 1-year readmission in elderly patients with ischemic heart disease (IHD) and whether these features can be used to predict the readmission risk of patients has not been studied.

Aims: The study aimed to develop 30-day and 1-year readmission prediction models for elderly IHD patients using combined machine learning features routinely collected at the time of hospital discharge, and to investigate their prognostic impact.

Methods: Eight machine learning algorithms were used to develop prediction models. Area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPRC) were used to assess discrimination. SHapley Additive exPlanations (SHAP) analysis was used to explain the contribution of features.

Results: A total of 6687 patients were enrolled. For 30-day readmissions, the categorical boosting (CB) model had the best predictive performance with the highest AUROC (0.72), and the Brier score was 0.23. For 1-year readmissions, the CB model had the best predictive performance with the highest AUROC (0.66), and the Brier score was 0.14. The age-adjusted Charlson comorbidity index, brain natriuretic peptide, heart failure, cholesterol, free thyroxine, thymidine kinase 1, osmotic pressure, and red blood cell distribution width (standard deviation) were the common important features to predict 30-day and 1-year readmissions of elderly IHD patients.

Conclusions: Elderly IHD patients with high risk of 30-day or 1-year readmission can be identified using machine learning and features collected at the time of discharge.