Predictive Modeling for Hospital Readmissions for Patients with Heart Disease: An updated review from 2012-2023.

Abstract

Hospital readmissions are a major concern for healthcare leaders, policy makers, and patients, resulting in adverse health outcomes and imposing an increased burden on hospital resources. This review aims to synthesize existing literature on predictive models focused on patients diagnosed with heart disease, which is known for its high readmission rates. Seven databases (i.e., Web of Science, Scopus, PubMed, ProQuest, Ovid, Cochrane Library and EBSCO) were consulted resulting in the inclusion of 56 eligible studies. Among these, 44 focused on model development, 7 on model validation, 4 on model improvement, and 1 on model implementation. Data were extracted on readmission types, data sources, modeling methods, and predictors, while assessments were conducted to analyze the quality of the studies. Findings showed that readmission types were significantly influenced by policy decisions, data predominantly originated from hospitals, and the prevalent modeling methods used were regression and single-layer machine learning techniques. The most important clinical predictors were related to comorbidities and complications, while the key demographic predictors were age and race. The study found that, despite advancements during the last decade, several limitations exist in current research, particularly in addressing attrition bias and handling missing data. Future research should, therefore, focus on optimizing readmission types, enhancing model generalization, using interpretable models, and emphasizing model implementation.