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Interpretable LightGBM model for predicting postoperative gastrointestinal hemorrhage in elderly hip fracture
Huan Sai1, Jiachen Ren1, Xu Ma1
1Department of Orthopaedic Surgery, Hebei Medical University Third Hospital, No 139 Ziqiang Road, Shijiazhuang, Hebei, 050051, P. R. China.
Background:
Postoperative gastrointestinal (GI) bleeding is a serious complication after hip fracture surgery in older adults, yet perioperative risk stratification remains limited because commonly used GI-bleeding scores are not tailored to orthopedic settings. This study aimed to develop and internally validate an interpretable model to predict postoperative GI bleeding risk in elderly hip fracture patients, using data routinely available during the perioperative period.
Methods:
We retrospectively included 342 elderly patients who underwent hip fracture surgery at the Third Hospital of Hebei Medical University from January to December 2023. The outcome was GI bleeding within 1 month after surgery, confirmed by medical records and/or telephone follow-up. Patients were randomly split into a training set (n = 242) and a validation set (n = 100). Predictors were screened using LASSO with 10-fold cross-validation, followed by multivariable logistic regression to identify independent risk factors. Ten prediction algorithms were trained and compared. Model performance was assessed by AUC, calibration, and decision curve analysis, and interpretability was evaluated using SHAP.
Results:
GI bleeding occurred in 38 patients (11.1%). Multivariable analysis identified four independent predictors: alcohol consumption history (OR 8.109, 95% CI 2.463-26.69), glucocorticoid use (OR 4.922, 95% CI 1.055-22.97), NSAID use (OR 6.851, 95% CI 1.811-25.915), and higher systemic immune-inflammation index (SII) (OR 1.001, 95% CI 1.000-1.002). Among the tested models, LightGBM showed the best overall performance, with AUCs of 0.843 (training) and 0.817 (validation), good calibration, and the highest net benefit on decision curve analysis. SHAP results ranked feature importance as SII, NSAID use, alcohol consumption history, and glucocorticoid use, consistent with regression findings.
Conclusions:
We developed and validated an interpretable LightGBM model that predicts postoperative GI bleeding risk in elderly hip fracture patients using routinely available clinical data. The final model incorporates only preoperative variables, systemic inflammation, NSAID use, alcohol history, and glucocorticoid use, supporting its application for early risk stratification prior to surgery.
