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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

  • Authors:
    • Qian Song
    • Ping'an Shi
    • Peng Tian
    • Aijun Chao
    • Yinguang Zhang
    • Qiang Dong
  • View Affiliations / Copyright

    Affiliations: Department of Osteo‑Internal Medicine, Tianjin University Tianjin Hospital, Tianjin 300122, P.R. China, Department of Endocrinology, Tianjin University Tianjin Hospital, Tianjin 300122, P.R. China, Department of Orthopedics, Tianjin University Tianjin Hospital, Tianjin 300122, P.R. China
    Copyright: © Song et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
  • Article Number: 259
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    Published online on: July 29, 2026
       https://doi.org/10.3892/etm.2026.13253
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Abstract

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.
View Figures

Figure 1

ROC curves of seven ML models. ROC
curves for (A) CatBoost, (B) decision tree, (C) LightGBM, (D)
logistic regression, (E) random forest, (F) SVM and (G) XGBoost,
with AUC values as indicated. (H) Overlay of the ROC curves of all
seven models, with corresponding AUC values listed: CatBoost, 0.90;
decision tree, 0.81; LightGBM, 0.91; logistic regression, 0.84;
random forest, 0.92; SVM, 0.87; XGBoost, 0.90. The diagonal dashed
line represents the reference line of no discrimination. ROC,
receiver operating characteristic; ML, machine learning; AUC, area
under curve; SVM, support vector machine; XGBoost, Extreme Gradient
Boosting.

Figure 2

Calibration plot of the random forest
model. Calibration metrics for the random forest model: A
calibration slope of 1.02 (95% CI: 0.95-1.09), intercept of -0.02,
Brier score of 0.09 and a Hosmer-Lemeshow P=0.45, indicating good
calibration. The x-axis represents the predicted probability of DVT
and the y-axis the observed DVT frequency. DVT, deep vein
thrombosis.

Figure 3

SHAP analysis of the random forest
model. (A) SHAP summary plot listing the top 15 features ranked by
mean absolute SHAP value. The color bar indicates feature value
(red indicates high and blue indicates low). Positive SHAP values
correspond to increased DVT risk. The features shown (from top to
bottom) are: TG, time to surgery, LDL cholestrol, immobility,
history of stroke, HDL cholestrol, HbA1c, Fibrinogen, fasting
glucose, diabetes duration, D-dimer, chronic kidney disease, BMI,
albumin and age. (B) SHAP dependence plot for HbA1c. Points are
colored by age group (blue indicates <80 years and red indicates
≥80 years). The vertical dashed line marks the threshold
HbA1c=7.0%. (C) SHAP dependence plot for LDL cholesterol. Points
are colored by TG level (red indicates low TG <1.7 mmol/l and
green indicates high TG ≥1.7 mmol/l). SHAP, Shapley Additive
exPlanations; DVT, deep vein thrombosis; LDL, low-density
lipoprotein; HDL, high-density lipoprotein; TG, triglyceride;
HbA1c, hemoglobin A1c.

Figure 4

Subgroup analysis forest plot. Forest
plot showing the AUC values of the random forest model across
clinically relevant subgroups. The subgroups are: Overall,
diabetic, non-diabetic, age <80 years, age ≥80 years, men,
women, BMI <25 kg/m2, BMI ≥25 kg/m2,
independent mobility, assisted/bedridden, HbA1c <7.0%, HbA1c
≥7.0%, LDL <3.0 mmol/l, LDL ≥3.0 mmol/l, wait ≤3 days and wait
>3 days. The overall AUC (red dot) is 0.92; individual subgroup
estimates are indicated by blue dots. The vertical dashed line
represents the overall AUC. LDL, low-density lipoprotein; AUC, area
under curve; HbA1c, hemoglobin A1c.

Figure 5

Decision curve analysis. Decision
curves depicting the net benefit of seven machine learning models
(CatBoost, Decision Tree, LightGBM, logistic regression, random
forest, SVM and XGBoost) together with the treat-all and treat-none
strategies across threshold probabilities from 0-0.5. The key
identifies each model and strategy by color. SVM, support vector
machine; XGBoost, Extreme Gradient Boosting.

Figure 6

Clinical impact curve for the random
forest model. The clinical impact curve illustrates the proportion
of patients classified as high-risk (red line) and the proportion
of true positives among them (blue line) across a range of risk
thresholds. At a threshold of 0.2, ~28% of patients would be
identified as high-riska nd 14.2% of all patients would be true
positives, corresponding to a sensitivity of 86% (86% of all DVT
cases are captured). The NNT at this threshold is 1.97, indicating
that 1.97 patients need to be classified as high-risk and receive
intervention to prevent one DVT event. The vertical dashed line
marks the 0.2 threshold. DVT, deep vein thrombosis; NNT, needed to
treat.

Figure 7

Proposed clinical decision pathway
for DVT risk stratification and prevention at admission. This
conceptual flowchart outlines a hypothesis-generating framework
based on the present exploratory findings. External and prospective
validation are required before any clinical implementation. The
pathway is intended for research purposes to guide future studies.
Data available on admission, including diabetes-specific variables
(HbA1c, diabetes duration and fasting glucose) and lipid parameters
(LDL cholesterol, HDL cholesterol and triglycerides), are collected
and input into a validated random forest model to predict DVT risk.
Patients are stratified into high-risk and low-risk categories
based on a predefined threshold. High-risk patients trigger an
enhanced thromboprophylaxis protocol encompassing extended
anticoagulation, glycemic optimization targeting HbA1c <7.0%,
lipid management, nutritional support to correct hypoalbuminemia
and early mobilization protocols. Patients at low risk receive
standard thromboprophylaxis according to local guidelines. This
pathway enables early, personalized risk stratification and
facilitates timely implementation of targeted preventive measures.
LDL, low-density lipoprotein; HDL, high-density lipoprotein; DVT,
deep vein thrombosis; HbA1c, hemoglobin A1c.
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Spandidos Publications style
Song Q, Shi P, Tian P, Chao A, Zhang Y and Dong Q: 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. Exp Ther Med 32: 259, 2026.
APA
Song, Q., Shi, P., Tian, P., Chao, A., Zhang, Y., & Dong, Q. (2026). 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. Experimental and Therapeutic Medicine, 32, 259. https://doi.org/10.3892/etm.2026.13253
MLA
Song, Q., Shi, P., Tian, P., Chao, A., Zhang, Y., Dong, Q."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". Experimental and Therapeutic Medicine 32.4 (2026): 259.
Chicago
Song, Q., Shi, P., Tian, P., Chao, A., Zhang, Y., Dong, Q."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". Experimental and Therapeutic Medicine 32, no. 4 (2026): 259. https://doi.org/10.3892/etm.2026.13253
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Spandidos Publications style
Song Q, Shi P, Tian P, Chao A, Zhang Y and Dong Q: 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. Exp Ther Med 32: 259, 2026.
APA
Song, Q., Shi, P., Tian, P., Chao, A., Zhang, Y., & Dong, Q. (2026). 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. Experimental and Therapeutic Medicine, 32, 259. https://doi.org/10.3892/etm.2026.13253
MLA
Song, Q., Shi, P., Tian, P., Chao, A., Zhang, Y., Dong, Q."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". Experimental and Therapeutic Medicine 32.4 (2026): 259.
Chicago
Song, Q., Shi, P., Tian, P., Chao, A., Zhang, Y., Dong, Q."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". Experimental and Therapeutic Medicine 32, no. 4 (2026): 259. https://doi.org/10.3892/etm.2026.13253
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