Predicting 30-day readmission using DRG-based hospitalization data: a large real-world logistic regression model from a tertiary hospital

Using comprehensive DRG-based real-world data, we developed an interpretable prediction model for 30-day readmission with moderate-to-good discrimination and acceptable calibration. Clinical case-mix captured by DRG categories and patient-level complexity reflected by longer length of stay were key determinants of early readmission. The model may support risk stratification, quality improvement, and performance monitoring in DRG payment environments. The findings may also inform policy discussions on aligning DRG efficiency incentives with patient safety outcomes.