Elucidating predictors of preoperative acute heart failure in older people with hip fractures through machine learning and SHAP analysis: a retrospective cohort study.

BACKGROUND

Acute heart failure (AHF) has become a significant challenge in older people with hip fractures. Timely identification and assessment of preoperative AHF have become key factors in reducing surgical risks and improving outcomes.

OBJECTIVE

This study aims to precisely predict the risk of AHF in older people with hip fractures before surgery through machine learning techniques and SHapley Additive exPlanations (SHAP), providing a scientific basis for clinicians to optimize patient management strategies and reduce adverse events.

METHODS

A retrospective study design was employed, selecting patients admitted for hip surgery in the Department of Geriatric Orthopedics at the Third Hospital of Hebei Medical University from January 2018 to December 2022 as research subjects. Data were analyzed using logistic regression, random forests, support vector machines, AdaBoost, XGBoost, and GBM machine learning methods combined with SHAP analysis to interpret relevant factors and assess the risk of AHF.

RESULTS

A total of 2,631 patients were included in the final cohort, with an average age of 79.3 ± 7.7. 33.7% of patients experienced AHF before surgery. A predictive model for preoperative AHF in older people hip fracture patients was established through multivariate logistics regression: Logit(P) = -2.262-0.315 × Sex + 0.673 × Age + 0.556 × Coronary heart disease + 0.908 × Pulmonary infection + 0.839 × Ventricular arrhythmia + 2.058 × Acute myocardial infarction + 0.442 × Anemia + 0.496 × Hypokalemia + 0.588 × Hypoalbuminemia, with a model nomogram established and an AUC of 0.767 (0.723-0.799). Predictive models were also established using five machine learning methods, with GBM performing optimally, achieving an AUC of 0.757 (0.721-0.792). SHAP analysis revealed the importance of all variables, identifying acute myocardial infarction as the most critical predictor and further explaining the interactions between significant variables.

CONCLUSION

This study successfully developed a predictive model based on machine learning that accurately predicts the risk of AHF in older people with hip fractures before surgery. The application of SHAP enhanced the model's interpretability, providing a powerful tool for clinicians to identify high-risk patients and take appropriate preventive and therapeutic measures in preoperative management.