Predicting lower extremity deep vein thrombosis in elderly patients with hip fracture: A machine learning model with emphasis on diabetes as a key risk factor

Lower extremity deep vein thrombosis (DVT) is a serious complication in elderly patients with hip fracture, contributing to increased morbidity and mortality. Diabetes mellitus, with its prothrombotic state, may further elevate this risk. Early identification of high‑risk patients is important for targeted thromboprophylaxis. The objective of the present study was to develop and validate machine learning (ML) models for predicting DVT using clinical variables available at admission in elderly patients with hip fracture, with a specific focus on diabetes as a key predictor. A retrospective cohort study of elderly patients (≥65 years) with hip fracture who were admitted to a tertiary academic medical center (Tianjin Hospital, China) between January 2020 and December 2025, was conducted. A total of seven ML algorithms were developed and validated using a 70‑30 split with 10‑fold cross‑validation. Model interpretability was enhanced using Shapley Additive exPlanations (SHAP) analysis. Subgroup analyses were performed to evaluate model performance across diabetic and non‑diabetic populations. DVT occurred in 16.5% (66/400) of patients during hospitalization. Diabetes was present in 32.5% of the cohort and was significantly associated with DVT (odds ratio=2.64; 95% CI: 1.56‑4.48). The random forest model demonstrated an improved performance [area under the curve (AUC)=0.92; accuracy=0.87; sensitivity=0.85; specificity=0.88]. SHAP analysis identified diabetes‑associated variables (hemoglobin A1c, diabetes duration and fasting glucose) among the top predictors, along with age, albumin, D‑dimer and immobility on admission. Lipid parameters, including low‑density lipoprotein cholesterol and triglycerides, also contributed to prediction. Model performance remained robust in diabetic (AUC=0.94) and non‑diabetic (AUC=0.90) subgroups. Calibration was good (slope=1.02; intercept=‑0.02; Brier score=0.09; Hosmer‑Lemeshow P=0.45). Decision curve analysis determined clinical net benefit across thresholds of 15‑40%. ML models, particularly random forest, accurately predicted DVT in patients with elderly hip fracture using routinely available admission data. Diabetes therefore emerged as a pivotal risk factor and its inclusion enhanced predictive accuracy. The present model holds promise for early risk stratification and individualized thromboprophylaxis, although rigorous external validation is warranted before any clinical application.