ArticleWorld journal of clinical cases2026
Machine learning model for predicting hospital-acquired functional decline in older patients with postoperative cardiovascular surgery.
Article in World journal of clinical cases, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundHospital-acquired functional decline (HAFD) is a poor prognostic factor in older patients who have undergone cardiovascular surgery.
aimTo develop a model to predict HAFD and to identify its associated factors.
methodsThis 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.
resultsAmong 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.
conclusionWe 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.
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