Developing a Machine Learning Model to Predict 180-day Readmission for Elderly Patients with Angina

TL;DR

This study develops a machine learning model to predict which elderly patients with angina are at high risk of being readmitted to the hospital within 180 days.

Contribution

The novel contribution is the development and validation of a high-performing ML model for predicting 180-day readmission in elderly angina patients.

Findings

The XGB model achieved strong predictive performance with an AUROC of 0.89 and AUPRC of 0.91.

Key predictors of readmission included the number of medications, hematocrit levels, and chronic obstructive pulmonary disease.

Abstract

Readmission of elderly angina patients has become a serious
problem, with a dearth of available prediction tools for readmission assessment.
The objective of this study was to develop a machine learning (ML) model that can
predict 180-day all-cause readmission for elderly angina patients.

The clinical data for elderly angina patients was
retrospectively collected. Five ML algorithms were used to develop prediction
models. Area under the receiver operating characteristic curve (AUROC), area
under the precision recall curve (AUPRC), and the Brier score were applied to
assess predictive performance. Analysis by Shapley additive explanations (SHAP)
was performed to evaluate the contribution of each variable.

A
total of 1502 elderly angina patients (45.74% female) were
enrolled in the study. The extreme gradient boosting (XGB) model showed good
predictive performance for 180-day readmission (AUROC = 0.89; AUPRC = 0.91; Brier
score = 0.21). SHAP analysis revealed that the number of medications, hematocrit,
and chronic obstructive pulmonary disease were important variables associated
with 180-day readmission.

An ML model can accurately
identify elderly angina patients with a high risk of 180-day readmission. The
model used to identify individual risk factors can also serve to remind
clinicians of appropriate interventions that may help to prevent the readmission
of patients.

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