Key result
Models predicting perioperative DVT in lower limb fractures achieve a moderate 0.82 AUC.
Why the study?
The quality of model development and applicability of predictive models for perioperative deep venous thrombosis in lower limb fractures remained unknown despite numerous existing models.
Do risk prediction models accurately estimate the risk of perioperative deep venous thrombosis in patients with lower limb fractures?
Meta-Analysis
Do risk prediction models accurately estimate the risk of perioperative deep venous thrombosis in patients with lower limb fractures?
Existing DVT risk prediction models for lower limb fracture patients show good overall predictive performance but suffer from high risk of bias and lack of external validation.
Models show moderate DVT discrimination in lower limb fractures; high bias and absent external validation leave routine use premature.
The quality of model development and its applicability are still unknown, even though numerous studies have developed or validated predictive models to estimate the risk of perioperative deep venous thrombosis (DVT) in lower limb fractures. The aim of the study was to systematically assess the DVT risk prediction model in lower limb fracture patients in the perioperative phase. PubMed, EMBASE, Web of Science, and China National Knowledge Infrastructure (CNKI) were assessed regarding studies on risk prediction models for perioperative DVT in limb fractures. The search period spanned from the database establishment to December 20, 2024. Two investigators independently screened the articles and extracted the data, and the quality of the included articles was evaluated using the PROBAST assessment tool. A total of 21 studies describing 25 predictive models were included, and the acquired data indicated that the incidence of DVT ranged from 1.2% to 72.5% in the perioperative period of lower limb fractures. The most commonly used predictors were D-dimer, age, time from injury to admission, and time from injury to surgery. The areas under the curve (AUCs) for all models ranged from 0.676 to 0.979. While twenty-three models demonstrated good predictive performance, many exhibited biases, often stemming from unreported methods for handling missing data, some models lacked external validation. The combined AUC value for the training models was 0.82 (95% confidence interval: 0.78–0.87), suggesting a moderate level of discrimination. The existing model exhibits good overall prediction performance and a low applicability risk, however the bias risk is high. To enhance the robustness of these findings, further research should encompass prospective studies, external validation of the current model, and refinement of the statistical analysis aspects.
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He et al. (2025) conducted a meta-analysis in Deep venous thrombosis in perioperative patients with lower limb fractures. DVT risk prediction models was evaluated on Combined Area Under the Curve (AUC) for the training models (AUC 0.82, 95% CI 0.78-0.87, p=<0.001). The combined AUC value for the training models predicting perioperative deep venous thrombosis in lower limb fractures was 0.82, suggesting a moderate level of discrimination.
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