An ensemble XGBoost model using PROFUND components achieved modest discrimination for predicting 30-day readmission in multimorbid patients, with an AUC of 0.59 compared to 0.52 for the LACE score.
Cohort (n=485)
Yes
Do machine learning models using PROFUND components improve the prediction of unplanned 30-day readmission in multimorbid patients compared to LACE and HOSPITAL scores?
Incorporating frailty, functional, and social vulnerability domains via machine learning models resulted in only modest, incremental improvements in predicting 30-day readmission compared to standard clinical scores.
Effect estimate: AUC 0.59
Early hospital readmission in multimorbid patients remains a major clinical challenge. Although risk stratification tools are widely used, predictive performance is often limited. The PROFUND index captures frailty, functional dependence, and social vulnerability, but its role in predicting 30-day readmission is unclear. In this prospective multicentre cohort study, multimorbid patients admitted to Internal Medicine and Geriatrics departments were followed after discharge. The primary outcome was unplanned 30-day readmission among patients surviving to 30 days. Models based on PROFUND components were developed using logistic regression and gradient boosting, including a calibrated ensemble model, and compared with LACE and HOSPITAL scores. Performance was assessed in an external validation cohort. Among 435 patients included in the readmission analysis, 14% were readmitted within 30 days. In external validation, discrimination remained modest (AUC 0.52–0.59). The ensemble XGBoost model achieved the highest AUC (0.59), followed by XGBoost (0.58), HOSPITAL (0.54), and LACE (0.52). Differences were incremental. SHAP analysis identified cognitive impairment, anaemia, advanced age, heart failure severity, functional dependence, and limited caregiver support as key contributors. Incorporating frailty, functional, and social vulnerability domains through PROFUND components resulted in only modest improvements in 30-day readmission prediction. Even with machine learning, discrimination remained limited. The observed performance likely reflects both the intrinsic complexity of short-term readmission and the constraints imposed by sample size and available predictors.
Pikatza-Huerga et al. (Wed,) conducted a cohort in Multimorbidity (n=485). Ensemble XGBoost model (PROFUND components) vs. LACE and HOSPITAL scores was evaluated on Unplanned 30-day hospital readmission (AUC 0.59). An ensemble XGBoost model using PROFUND components achieved modest discrimination for predicting 30-day readmission in multimorbid patients, with an AUC of 0.59 compared to 0.52 for the LACE score.