WITHDRAWN Prediction of 30-day Readmission for ICU Patients with Heart Failure

Music City Center 

Intensive Care Unit (ICU) readmissions among patients with heart failure (HF) impose a substantial economic burden on both patients and healthcare systems. While previous studies have identified various predictors of readmission, consensus on their relative importance and optimal predictive models remains limited. This study aims to evaluate key predictors and assess the performance of different modeling approaches in forecasting 30-day ICU readmissions using the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. This study applied logistic regression, classification trees, and random forest models to develop predictive frameworks. Although overall model performance did not surpass findings from prior studies, hemoglobin emerged as a significant predictor of 30-day readmission, reinforcing its clinical relevance in HF patient management. These findings highlight the challenges and potential of predictive modeling in ICU readmission risk assessment.

Heart Failure

ICU Readmission

Electronic Health Record

Machine Learning

Variable Importance 

Biometrics Section