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Updated: Jul 8, 2026

Method and Instrumented Fixture for Femoral Fracture Testing in a Sideways Fall-on-the-Hip Position
Published on: August 17, 2017
Risk Prediction Models for Preoperative Deep Vein Thrombosis in Older Patients with Hip Fracture: A Systematic Review
Huali Guo1, Kuankuan Xu2, Fangfang Deng1
1Department of Orthopaedics, The First College of Clinical Medical Science, China Three Gorges University/Yichang Central People's Hospital, Yichang, Hubei, China.
Objective:
To systematically assess the risk prediction models for preoperative deep vein thrombosis in older patients with hip fractures.
Method:
We searched four databases for literature through November 17, 2023. We included patients aged ≥60 with hip fractures and considered English-language case-control or cohort studies that focused on developing and/or validating risk prediction models for DVT in this population. Excluded were studies that solely analyzed risk factors without constructing a prediction model, had fewer than 2 predictive variables, or were not available in full-text or were duplicate publications. The Predictive Model Bias Risk Assessment tool was utilized to evaluate risk of bias. The area under the curve (AUC) values were meta-analyzed using R Studio software. The I2 index and Cochrane q test were employed to assess heterogeneity. Additionally, sensitivity analysis was performed by systematically removing individual studies to explore the sources of heterogeneity.
Results:
A total of 1880 studies were gathered. Out of these, seven studies were included, encompassing 8 models. The most commonly utilized factors in the models were D-dimer and the time from injury to admission. The pooled AUC value for the validation of 8 models was 0.84 (95% confidence interval: 0.80-0.87), indicating robust model performance.
Conclusion:
Current risk prediction models for preoperative DVT in elderly hip fracture patients are still in the developmental phase. Future research should focus on developing new models with larger sample sizes, robust study designs, and multicenter external validation.
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