Machine learning model for predicting hospital-acquired functional decline in older patients with postoperative cardiovascular surgery

BACKGROUND: Hospital-acquired functional decline (HAFD) is a poor prognostic factor in older patients who have undergone cardiovascular surgery. AIM: To develop a model to predict HAFD and to identify its associated factors. METHODS: This retrospective observational study included 144 patients who underwent cardiovascular surgery between May 2019 and December 2023. HAFD was defined as a change in the preoperative and pre-discharge short physical performance battery score. Seven machine learning models were constructed, and their performance was evaluated using the area under the receiver operating characteristic curve (AUC) values. The models were further interpreted using SHapley Additive exPlanations (SHAP) values. RESULTS: Among the 144 participants, 41 (28.5%) experienced HAFD. Of the 7 machine learning models, the extreme gradient boosting model (XGBoost) achieved the best performance, with an AUC of 0.87. SHAP analysis revealed that being female and having a slower preoperative walking speed markedly impacted HAFD occurrence. CONCLUSION: We developed a high-accuracy model to predict HAFD in older patients who have undergone cardiovascular surgery and identified key associated factors, informing preoperative evaluations and interventions in clinical practice.