A retrospective cohort study of patients aged ≥65 years who were admitted with hip fracture to a tertiary academic medical center (Tianjin Hospital, China) between January 2020 and December 2025 was conducted. All elderly patients with hip fracture during this period were consecutively screened for eligibility. Inclusion criteria: i) Age ≥65 years old, diagnosed with hip fracture through imaging (hip joint X-ray/CT), classified as femoral neck fracture or intertrochanteric fracture; ii) after admission, standardized and improved lower limb vein color Doppler ultrasound screening was conducted, and the diagnostic data for thrombosis was complete; iii) the clinical medical record data is complete, and all predictive variables such as demographics, underlying diseases, fracture injuries, test indicators, surgical anesthesia, perioperative medication and activity ability could be fully extracted; iv) hospitalization duration ≥5 days, no early automatic discharge or loss to follow-up, with a complete DVT screening imaging report; and v) accepted standard orthopedic surgical treatment: Proximal femoral nail anti-rotation (PFNA) internal fixation, artificial femoral head replacement and total hip replacement. Other exclusion criteria were as follows: i) A clear history of lower limb DVT and pulmonary embolism at the time of admission, and ultrasound findings of old venous thrombosis; ii) multiple severe traumas, pelvic fractures, spinal fractures, traumatic brain injury, hemorrhagic shock and continuous vascular active drug maintenance in ICU; iii) late-stage malignant tumors, maintenance hemodialysis, decompensated cirrhosis, severe coagulation dysfunction and autoimmune vasculitis; iv) long-term oral anticoagulant use (such as warfarin, rivaroxaban and apixaban) and long-term lower limb venous filter placement before admission; v) accepted standard orthopedic surgical treatment: PFNA internal fixation, artificial femoral head replacement or total hip replacement. Patients managed conservatively with long-term bed rest were excluded from the final analysis; vi) missing data for coagulation four items (prothrombin time, activated partial thromboplastin time, thrombin time and fibrinogen), D-dimer, fracture classification, surgical records and bilateral lower limb venous ultrasound reports; vii) long-term bed rest for >2 weeks before admission, paraplegia, lower limb disability and previous lower limb amputation surgery; viii) during hospitalization, due to death or automatic discharge, the standard lower limb venous ultrasound screening was not completed, and there were no reliable DVT judgment results; ix) non-traumatic hip fractures (excluding pathological fractures caused by osteoporosis, and direct exclusion of bone marrow tumors and tumor metastasis fractures); and x) refusing thrombus screening and standardized anticoagulant prophylaxis after admission making it impossible to determine the true risk of DVT. After applying the exclusion criteria, a total of 400 patients (182 males and 218 females) were included in the final analysis. The present study was approved by the Institutional Review Board of Tianjin Hospital (Tianjin, China) Ethics Committee with a waiver for informed consent due to its retrospective nature.

Data were systematically collected from electronic health records using a standardized protocol (26) by two independent reviewers, with disagreements resolved by consensus. The primary outcome was lower extremity DVT diagnosed by duplex ultrasound during hospitalization. DVT ascertainment followed a two-step process: Initial screening using International Classification of Diseases-9/10 codes for DVT [ICD-9 and ICD-10 diagnostic codes applied to ascertain DVT conform to standardized venous thromboembolism diagnostic coding criteria formulated by the World Health Organization (WHO) (27) and domestic hospital information coding specifications. ICD-9 coding inclusion criteria for DVT: Codes 451.11 and 451.19 were adopted, which correspond to phlebitis and thrombophlebitis of lower extremities, encompassing acute DVT of the femoral vein, popliteal vein, tibial vein and calf muscular veins; codes related to superficial phlebitis, chronic post-thrombotic syndrome and pulmonary embolism without concurrent lower-extremity DVT were excluded. ICD-10 coding inclusion criteria for DVT: Codes I82.4, I82.5, I82.6 and I82.8 were utilized, representing acute embolism and thrombosis of distal and proximal deep veins of the lower limb. Exclusion criteria for ICD-10 codes covered superficial venous thrombosis, chronic residual venous thrombotic lesions, thromboembolism of upper extremities, cerebral venous thrombosis and non-venous thrombotic disorders of visceral organs. All index admission diagnosis codes and secondary diagnosis codes meeting the above ICD-9/10 criteria were marked as DVT-positive flagged cases for subsequent standardized medical record abstraction], followed by structured chart review for all code-flagged patients and a 20% random sample of non-flagged patients. Both clinicians, blinded to predictor variables, independently reviewed radiology reports, progress notes and discharge summaries, with disagreements resolved by a third senior vascular specialist. Both screening and clinically indicated ultrasounds were included, with DVT defined as a new non-compressible venous segment or intraluminal filling defect.

A comprehensive set of variables were collected at the time of admission, encompassing demographic characteristics (including age, sex and BMI), comorbidities (hypertension, coronary artery disease, chronic kidney disease, diabetes mellitus with detailed specifications including type, duration and treatment modality, history of stroke or varicose veins), diabetes-specific measures [hemoglobin A1c (HbA1c), fasting glucose, insulin use and oral hypoglycemic agents], laboratory parameters [hemoglobin, platelet count, D-dimer, fibrinogen, albumin, creatinine, CRP and lipid profile including total cholesterol, low-density lipoprotein (LDL) cholesterol, high-density lipoprotein (HDL) cholesterol and triglycerides], functional status (mobility status on admission categorized as independent ambulation, ambulatory with assistance or bedridden; Barthel Index assessed on admission), fracture characteristics (fracture type: Femoral neck vs. intertrochanteric) and a composite measure of comorbidity burden [Charlson Comorbidity Index (28)]. After data collection, preprocessing was performed following a standardized workflow (29) to ensure model development was leakage-free.

A standardi zed ML development workflow was followed to ensure a rigorous and leakage-free evaluation. The dataset was first randomly split into a model development set (70%) and a hold-out internal validation set (30%) using stratified sampling based on the DVT outcome. All subsequent data-dependent steps, including handling of missing values, normalization and hyperparameter tuning, were conducted exclusively within the development set to prevent any information from the validation set influencing model development.

For variables with <20% missing values, multiple imputation by chained equations (MICE) with 10 iterations was applied to the training data. Convergence was assessed by visually inspecting trace plots of the mean and variance of imputed values across iterations; stable patterns without trends or strong autocorrelation were observed by iteration 5, with 10 iterations being conservatively used. The mean imputed values from the training set were then used to impute missing values in the test set. Variables with >20% missingness were excluded from analysis. Continuous variables were standardized using Z-score normalization, with the mean and SD calculated from the training set and applied to the test set. No feature selection was performed prior to modeling based on univariate statistical significance; all aforementioned variables listed were used as input features to allow the ML models to evaluate all potential associations, including complex interactions, thereby preventing information leakage from outcome-driven feature selection. Multicollinearity was assessed using variance inflation factors (VIF) for all predictor variables in the logistic regression model; all VIF values were <3, indicating no significant collinearity among the diabetes-associated variables or other predictors (Table SI). A total of seven ML algorithms were implemented: Logistic regression with L2 regularization as a baseline conventional model, decision tree for comparison as a single-tree architecture, random forest as an ensemble of decision trees with bagging, Extreme Gradient Boosting (XGBoost) as a gradient-boosted ensemble, LightGBM, CatBoost and support vector machine with a radial basis function kernel. For each algorithm, hyperparameter tuning was conducted strictly within the training set via grid search coupled with 10-fold cross-validation, optimizing for the area under the receiver operating characteristic (ROC) curve. The searched hyperparameter grids included: For random forest, number of trees (100, 300, 500), max depth (5, 10, 15) and min samples split (2, 5, 10); for XGBoost, learning rate (0.01, 0.1, 0.2), max depth (3, 6, 9) and n_estimators (100, 300); for LightGBM, learning rate (0.01, 0.05, 0.1), max depth (3, 5, 7), num_leaves (15, 31, 63) and min_child_samples (10, 20, 30); for CatBoost, iterations (100, 300, 500), learning rate (0.01, 0.05, 0.1), depth (3, 5, 7) and l2_leaf_reg (1, 3, 5); for support vector machine, C (0.1, 1, 10) and γ (’scale’, ’auto’, 0.01, 0.1); for logistic regression, C (0.01, 0.1, 1, 10); and for decision tree, max depth (3, 5, 10, 15) and min samples split (2, 5, 10). The model configuration achieving the highest mean cross-validated area under the curve (AUC) was selected for each algorithm. The final model for each algorithm was produced by retraining the optimal hyperparameter configuration on the entire preprocessed training set and then applied only once to the preprocessed hold-out internal validation set for final, unbiased evaluation. The search ranges for hyperparameters were determined based on preliminary experiments [including i) trial runs testing different iteration numbers (5/10/15) for MICE multiple imputation to identify sufficient convergence iterations; ii) testing of varying Z-score normalization strategies to confirm leakage-free scaling logic; iii) pre-screening of hyperparameter value ranges for all seven machine learning algorithms via small subset training samples, eliminating obviously underfitting or overfitting parameter combinations; iv) VIF multicollinearity pre-tests on raw feature sets to exclude highly collinear variable combinations beforehand; and v) pilot training with partial cohort data to compare rough AUC performance boundaries of each algorithm, laying the range basis for formal grid search], common configurations reported (30,31) in the literature and the need to cover a spectrum from simple to complex models for each algorithm. After developing and tuning all seven models, their performance was evaluated on the hold-out validation set using a comprehensive set of metrics.

Model performance was assessed on the internal validation set using the area under the ROC curve with 95% CIs calculated by DeLong's method, along with accuracy, sensitivity, specificity, positive predictive value, negative predictive value and F1-score. DeLong's tests were also performed to compare AUCs between models, particularly random forest vs. the others. Calibration was evaluated using calibration plots, calibration slope and intercept, the Hosmer-Lemeshow goodness-of-fit test and the Brier score. Clinical utility was assessed using decision curve analysis across probability thresholds from 0-0.5 and a clinical impact curve to illustrate the number of patients classified as high-risk and the number of true positives at various risk thresholds.

To enhance interpretability, feature importance was calculated for tree-based models as mean decrease in impurity. In addition, SHAP was applied to the best-performing model to generate summary plots of global feature importance and direction of effect, dependence plots for key variables (including diabetes-associated and lipid parameters) to visualize nonlinear associations and interactions and to quantify the contribution of these variables to overall predictions.

The performance of the random forest model was evaluated in prespecified subgroups, including diabetic vs. non-diabetic patients, age <80 vs. ≥80 years, men vs. women, BMI <25 vs. ≥25 kg/m² and mobility status on admission (independent vs. assisted or bedridden). AUC values with 95% CIs were calculated for each subgroup.

To assess robustness, a number of sensitivity analyses were conducted: Complete-case analysis excluding patients with any missing data, repeating model development with five different random seeds for train-test split, using mean imputation instead of MICE, and comparing the primary random forest model to one trained only on variables with P<0.05 from univariate analysis.

Analysis was conducted and the present study results were reported in accordance with the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis-Artificial Intelligence statement (32).

Data distribution was tested using the Shapiro-Wilk test. Patient characteristics are described using mean ± SD or median with interquartile range for continuous variables and frequency with percentages for categorical variables. Independent t-tests or Mann-Whitney U tests were applied for continuous variables with normal or non-normal distribution, respectively. Categorical variables were analyzed using the Pearson χ² test or Fisher's exact test as appropriate.

Univariate logistic regression analysis was performed to assess the unadjusted associations between individual variables measured at admission and DVT, providing a conventional clinical summary of potential risk factors. All variables, including demographic characteristics, comorbidities, diabetes-specific laboratory markers, routine laboratory parameters, functional status indicators, fracture classification and Charlson Comorbidity Index score (detailed list: age, sex, BMI, hypertension, coronary artery disease, chronic kidney disease, diabetes mellitus, stroke history, varicose veins, HbA1c, fasting glucose, insulin use, oral hypoglycemic agents, hemoglobin, platelet count, D-dimer, fibrinogen, albumin, creatinine, CRP, lipid profile, mobility status, Barthel Index, fracture type, Charlson Comorbidity Index) were first analyzed via univariate logistic regression. All of these variables, regardless of univariate statistical significance, were adopted as input features for subsequent machine learning modeling.

All statistical analyses were conducted using SPSS software (version 26.0; IBM Corp) and Python (version 3.9.16; Python Software Foundation) with libraries including scikit-learn 1.2.2, XGBoost 1.7.5, LightGBM 3.3.5, CatBoost 1.2, SHAP 0.41.0, matplotlib 3.7.1 and pandas 1.5.3. MICE was implemented using the IterativeImputer class from sklearn.impute with max_iter=10, tol=1e-3 and convergence monitored via the change in parameter estimates between successive iterations. VIF calculations were performed using the ‘variance_inflation_factor’ function from ‘statsmodels.stats.outliers_influence’ in Python. Two-tailed P<0.05 was considered to indicate a statistically significant difference for baseline comparisons and univariate analysis.

A total of 400 consecutive elderly patients admitted with hip fracture who met the inclusion criteria were included the final analytical cohort. The overall incidence of lower extremity DVT during hospitalization was 16.5% (66/400 patients). As detailed in Table I, baseline characteristics differed significantly between patients who developed DVT and those who did not. Patients in the DVT group were significantly older (83.1±7.3 vs. 78.4±8.6 years; P<0.001) and had a lower BMI (22.8±3.3 vs. 24.1±3.7 kg/m²; P=0.012). The prevalence of diabetes mellitus was significantly higher in the DVT group (51.5 vs. 28.7%; P<0.001), with longer diabetes duration (11.8±6.3 vs. 7.4±4.9 years; P<0.001) and higher rates of insulin use (22.7 vs. 8.7%; P<0.001). Other comorbidities significantly associated with DVT included coronary artery disease (48.5 vs. 34.7%; P=0.032), chronic kidney disease (27.3 vs. 13.8%; P=0.006), history of stroke (24.2 vs. 12.6%; P=0.012), varicose veins (18.2 vs. 8.4%; P=0.012), heart failure (22.7 vs. 11.7%; P=0.014) and atrial fibrillation (21.2 vs. 10.2%; P=0.010). The Charlson Comorbidity Index was also significantly higher in the DVT group (5.4±2.4 vs. 4.2±2.1; P<0.001).

Laboratory parameters revealed a distinct prothrombotic and metabolic profile among patients who developed DVT. These patients exhibited significantly higher HbA1c levels (7.6±1.5 vs. 6.5±1.2%; P<0.001) and fasting glucose (7.68±2.18 vs. 6.58±1.82 mmol/l; P<0.001). Markers of thrombosis and inflammation were also significantly higher, including D-dimer (8920±5380 vs. 5560±4280 ng/ml; P<0.001) and fibrinogen (4.58±1.32 vs. 3.79±1.12 g/l; P<0.001). In addition, CRP was significantly higher in the DVT group (36.5±22.8 vs. 24.9±18.4 mg/l; P<0.001). Nutritional status was significantly poorer in the DVT group, as evidenced by lower albumin levels (31.8±4.8 vs. 37.4±4.9 g/l; P<0.001) and a higher prevalence of hypoalbuminemia (57.6 vs. 26.9%; P<0.001). Hemoglobin was also significantly lower (113.6±17.5 vs. 125.8±18.0 g/l; P<0.001). Regarding lipid profiles, patients with DVT exhibited significantly higher total cholesterol (5.22±1.22 vs. 4.69±1.12 mmol/l; P<0.001), LDL cholesterol (3.38±0.95 vs. 2.83±0.84 mmol/l; P<0.001) and triglycerides (2.02±0.86 vs. 1.61±0.71 mmol/l; P<0.001), while HDL cholesterol was significantly lower (1.14±0.31 vs. 1.33±0.35 mmol/l; P<0.001).

Functional status on admission was significantly impaired in the DVT group compared with that in the non-DVT group, with a lower Barthel Index (46.5±15.8 vs. 64.1±16.8; P<0.001) and a higher proportion of patients requiring assistance or being bedridden (78.8 vs. 56.9%; P<0.001). Process-associated factors also differed significantly: Patients who developed DVT exhibited a longer time from injury to admission (2.4±1.8 vs. 1.7±1.4 days; P=0.002) and longer time from admission to surgery (4.6±2.6 vs. 3.3±2.1 days; P<0.001). Fracture type was similarly distributed between groups (P=0.142). To further quantify the strength of association for each risk factor, univariate logistic regression analysis was performed.

Univariate logistic regression analysis revealed a wide spectrum of significant predictors spanning demographic, comorbidity, laboratory, functional and process-associated domains, as detailed in Table II. Among the strongest identified risk factors were diabetes mellitus [odds ratio (OR)=2.64; 95% CI: 1.56-4.48; P<0.001], with each additional year of diabetes duration conferring a 10% increase in DVT risk (OR=1.10; 95% CI: 1.05-1.15; P<0.001). Insulin use was associated with a >2-fold elevated risk (OR=2.38; 95% CI: 1.38-4.12; P=0.002). Hypoalbuminemia emerged as one of the most potent predictors, with a nearly 4-fold increased odds of DVT (OR=3.58; 95% CI: 2.08-6.16; P<0.001), reinforced by the continuous protective effect of albumin (OR=0.84 per g/l; 95% CI: 0.79-0.89; P<0.001).

Markers of thrombosis and inflammation demonstrated strong associations: D-dimer (OR=1.28 per 1,000 ng/ml; 95% CI: 1.18-1.39; P<0.001), fibrinogen (OR=1.62 per g/l; 95% CI: 1.32-1.99; P<0.001) and CRP (OR=1.22 per 10 mg/l; 95% CI: 1.08-1.38; P<0.001) were all significantly associated with increased DVT risk. Lipid parameters also contributed notably: LDL cholesterol (OR=1.58 per mmol/l; 95% CI: 1.26-1.98; P<0.001), triglycerides (OR=1.58 per mmol/l; 95% CI: 1.24-2.01; P<0.001) and total cholesterol (OR=1.38 per mmol/l; 95% CI: 1.12-1.70; P<0.001) were significant risk factors, while HDL cholesterol demonstrated a protective effect (OR=0.58 per mmol/l; 95% CI: 0.42-0.80; P<0.001).

Functional status was highly predictive: Compared with independently mobile patients, those requiring assistance exhibited a >2-fold increased risk (OR=2.28; 95% CI: 1.22-4.26; P=0.010), and bedridden patients showed a ~4-fold increased risk (OR=3.92; 95% CI: 1.98-7.76; P<0.001). Each 10-point decrement in Barthel Index was associated with a 38% increase in DVT risk (OR=0.62; 95% CI: 0.51-0.75; P<0.001). Process-associated factors also served key roles: Each additional day from injury to admission increased risk by 25% (OR=1.25; 95% CI: 1.08-1.45; P=0.002) and each additional day from admission to surgery increased risk by 18% (OR=1.18; 95% CI: 1.08-1.29; P<0.001). While univariate analysis identifies individual risk factors, ML models can capture complex interactions among multiple predictors. Therefore seven different algorithms were evaluated for DVT prediction.

Predictive performances of seven distinct ML classifiers for DVT were evaluated on the internal validation set, with results systematically summarized in Table III and graphically presented in Fig. 1. Fig. 1A-G display the individual ROC curves for each model with their corresponding area AUC values. Fig. 1H overlays the ROC curves of all seven models, allowing direct visual comparison. The diagonal dashed line in each panel represents the reference line of no discrimination.

Among all evaluated algorithms, the ensemble-based random forest model demonstrated improved performance, achieving an AUC of 0.92 (95% CI: 0.87-0.96). This high discriminative ability was complemented by balanced and robust performance across all other key metrics, including an accuracy of 0.87, sensitivity of 0.85, specificity of 0.88, positive predictive value of 0.80, negative predictive value of 0.91 and an F1-score of 0.84.

Other gradient boosting methods also exhibited strong predictive capability: LightGBM achieved an AUC of 0.91 (95% CI: 0.86-0.95), while XGBoost and CatBoost both attained AUCs of 0.90 (95% CI: 0.85-0.94). The support vector machine classifier achieved moderate performance with an AUC of 0.87 (95% CI: 0.82-0.92). By contrast, both the conventional logistic regression and the simpler Decision Tree classifiers showed comparatively limited performance, with AUC values of 0.84 (95% CI: 0.78-0.89) and 0.81 (95% CI: 0.75-0.87), respectively.

DeLong's test comparisons revealed that the random forest AUC was significantly higher compared with logistic regression (P=0.006) and Decision Tree (P<0.001), but the differences with XGBoost (P=0.220), LightGBM (P=0.280) and CatBoost (P=0.240) were not statistically significant. The random forest model also demonstrated marked calibration on the internal validation set (Fig. 2), with a calibration slope of 1.02 (95% CI: 0.95-1.09), an intercept of -0.02, a Brier score of 0.09 and a non-significant Hosmer-Lemeshow test (P=0.450), indicating that its predicted risks were reliable across the probability spectrum. Having established the improved performance of the random forest model, the present study further examined which features drove its predictions using SHAP analysis.

Comprehensive feature importance analysis across the seven ML approaches revealed notable consistency in identifying core predictive factors, as quantitatively detailed in Table IV. Random forest feature importance based on mean decrease in impurity identified HbA1c as the most important predictor (normalized score: 100), followed by D-dimer (normalized score: 96), age (normalized score: 90), diabetes duration (normalized score: 88), albumin (normalized score: 86), time from admission to surgery (normalized score: 82), LDL cholesterol (normalized score: 78), immobility on admission (normalized score: 76) and triglycerides (normalized score: 72). SHAP analysis further determined these rankings and provided additional insights into the direction and nature of effects (Fig. 3).

HbA1c showed a strong positive association with DVT risk, with SHAP values increasing sharply at levels above 7.0%, suggesting a potential threshold effect for glycemic control. Diabetes duration exhibited a near-linear positive association with DVT risk. Albumin demonstrated a negative association, with risk increasing notably when albumin fell below 35 g/l. Age exhibited a gradual positive association, with accelerating risk after age 80 years (Fig. 3A).

Among thrombotic markers, D-dimer displayed a nonlinear effect, with risk increasing steeply up to ~8,000 ng/ml before plateauing at high levels. Fibrinogen exhibited a more linear positive association. Lipid parameters revealed important patterns: LDL cholesterol and triglycerides exhibited positive associations with DVT risk, while HDL cholesterol demonstrated a consistent protective effect.

SHAP dependence plots revealed notable interactions among key predictors (Fig. 3B and C). The effect of HbA1c was modified by age, such that the HbA1c-associated risk increase was more notable in patients aged ≥80 years. Diabetes duration interacted with D-dimer, with patients exhibiting a long diabetes duration and elevated D-dimer experiencing a disproportionately high risk. The effect of LDL cholesterol was potentiated in patients with elevated triglycerides. Notably, diabetes-associated variables (HbA1c, diabetes duration, fasting glucose and insulin use) collectively accounted for ~30% of total feature importance in the random forest model, while lipid parameters collectively contributed an additional 18%, underscoring the pivotal role of metabolic factors in DVT pathogenesis.

Random forest models maintained robust and consistent discriminative ability across all prespecified subgroups, as detailed in Table V and visualized in Fig. 4. In diabetic patients, the model achieved an AUC of 0.94 (95% CI: 0.89-0.98) compared with 0.90 (95% CI: 0.85-0.94) in non-diabetic patients. For patients aged ≥80 years, the AUC was 0.92 (95% CI: 0.87-0.96) vs. 0.91 (95% CI: 0.86-0.95) for those <80 years. Performance was similarly robust across sexes, with AUCs of 0.91 for men and 0.92 for women. Of note, the AUC of 0.95 in the HbA1c ≥7.0% subgroup, while promising, should be interpreted with caution given the moderate sample size (n=108; events=28). These findings require demonstration in larger prospective cohorts.

Patients with lower BMI (<25 kg/m²) exhibited a slightly higher model performance (AUC of 0.93) compared with those with a higher BMI (AUC of 0.90). The model demonstrated good discrimination in patients with impaired mobility on admission (AUC of 0.93) compared with independently mobile patients (AUC of 0.88). Notably, among patients with HbA1c ≥7.0%, the model achieved an AUC of 0.95, markedly higher compared with those with improved glycemic control (AUC of 0.89). Similarly, patients with elevated LDL cholesterol (≥3.0 mmol/l) exhibited a higher model performance (AUC of 0.93) compared with those with lower LDL (AUC of 0.90). The model also performed well in patients with prolonged preoperative waiting time (>3 days), achieving an AUC of 0.93 vs. 0.89 in those with shorter waiting times.

Multiple sensitivity analyses demonstrated the robustness of the primary findings, as detailed in Table VI. Complete-case analysis, excluding patients with any missing data (n=352), yielded a random forest AUC of 0.91 (95% CI: 0.86-0.95), nearly identical to the primary analysis using imputed data. A total of five different random seeds for train-test split produced random forest AUCs ranging from 0.90-0.93, with a mean of 0.92 and SD of 0.01, indicating good stability. Using mean imputation instead of MICE resulted in an AUC of 0.91 (95% CI: 0.86-0.95), comparable with the primary analysis. The model trained only on variables with P<0.05 from univariate analysis achieved an AUC of 0.91, confirming no performance degradation caused by retaining all potential predictors in the full model.

To assess the incremental value of specific variable categories, additional sensitivity analyses were performed. Excluding lipid parameters from the model reduced the AUC to 0.88 (95% CI: 0.83-0.92), demonstrating the marked contribution of lipid profile to prediction accuracy. Excluding diabetes-specific variables (HbA1c, diabetes duration, fasting glucose and insulin use) resulted in a more notable reduction to 0.85 (95% CI: 0.80-0.90), underscoring the pivotal role of diabetes-associated factors in DVT prediction. A 10-fold cross-validation on the training set yielded a mean AUC of 0.91 (95% CI: 0.87-0.95), consistent with the hold-out validation results.

Decision curve analysis (Fig. 5) demonstrated that the random forest model provided positive net benefit across a wide range of clinically relevant threshold probabilities from 10-45%, outperforming both the treat-all and treat-none strategies as well as the other ML models. At a risk threshold of 20%, the net benefit was 0.13, equivalent to correctly identifying 13 additional true DVT cases per 100 patients without increasing false positives.

The clinical impact curve (Fig. 6) illustrated the practical implications of model implementation: At a 20% risk threshold, ~28% of patients would be classified as high-risk, and these high-risk patients would account for 86% of all DVT cases (sensitivity). The positive predictive value at this threshold was 50.7%, indicating that among patients identified as high-risk, ~50% would actually develop DVT. This translates to a number needed to treat (NNT) of ~2.0, meaning that for every 2 patients classified as high-risk and receiving targeted prophylaxis, one DVT event could potentially be prevented. This suggested favorable cost-effectiveness for targeted thromboprophylaxis programs, while avoiding unnecessary intervention in 72% of low-risk patients.

Based on the present exploratory findings, a hypothesis-generating clinical decision pathway may be proposed for risk stratification and prevention of DVT in elderly patients with hip fracture, as illustrated in Fig. 7. This conceptual framework requires prospective validation before clinical implementation. Upon admission, key clinical data including diabetes-specific indicators (HbA1c, diabetes duration and insulin use), age, lipid profile, D-dimer, albumin, mobility status and time from injury/admission are collected. These variables are input into the validated random forest model, which calculates the individual DVT risk probability of the patient. Patients are stratified into high-risk (risk ≥20%) and low-risk categories based on a predefined threshold. All standardized baseline admission predictors (including demographic characteristics, comorbidities, diabetes-specific laboratory markers, routine laboratory parameters, admission functional status, fracture classification and Charlson Comorbidity Index) are preprocessed uniformly and input into the internally validated random forest ensemble model. The model generates a continuous individualized predicted probability (range, 0-1) of postoperative DVT for each patient based on the full feature vector. Patients are stratified into two risk tiers using a predefined 20% probability threshold. This cutoff value was determined by maximizing Youden's J statistic on the internal validation ROC curve to balance screening sensitivity and specificity for clinical practice. Patients with predicted DVT risk ≥20% are categorized as high-risk candidates who require enhanced, intensified thromboprophylaxis, comprising strict glycemic control (target HbA1c <7%), extended anticoagulation, lipid management with statins, nutritional support to correct hypoalbuminemia and early mobilization. Patients with predicted risk <20% fall into the low-risk group eligible for standard routine venous thromboembolism prevention strategies, for example,with low-molecular-weight heparin during hospitalization and routine care. All patients are monitored for DVT and bleeding events throughout their hospital stay. This pathway enables early, personalized risk stratification and facilitates timely implementation of targeted preventive measures.