Key result
XGBoost machine learning model predicts post-THA VTE with ~0.98 AUC.
Why the study?
Existing risk scores like Caprini and Geneva are not specifically designed for THA and may not accurately predict postoperative DVT or PE despite prophylactic anticoagulation.
Does an XGBoost machine learning model improve the prediction of deep vein thrombosis and pulmonary embolism in patients undergoing elective total hip arthroplasty compared to traditional risk scores?
Observational (n=1,481)
No
Does an XGBoost machine learning model improve the prediction of deep vein thrombosis and pulmonary embolism in patients undergoing elective total hip arthroplasty compared to traditional risk scores?
An XGBoost machine learning model utilizing standard preoperative variables—especially bilirubin—highly accurately predicts DVT and PE risk after elective total hip arthroplasty, outperforming traditional risk scores.
Should not yet change VTE risk stratification after hip arthroplasty; leaves open need for prospective validation against traditional scores.
Prophylactic anticoagulation is a standard strategy for patients undergoing total hip arthroplasty (THA) to prevent deep venous thromboembolism (DVT) and pulmonary embolism (PE). Nevertheless, some patients still experience these complications during their hospital stay. Current risk assessment methods like the Caprini and Geneva scores are not specifically designed for THA and may not accurately predict DVT or PE postoperatively. This study used machine learning techniques to establish models for early diagnosis of DVT and PE in patients undergoing THA. Data were collected from 1481 patients who received perioperative prophylactic anticoagulation. Model establishment and parameter tuning were performed using a training set and evaluated using a test set. Among the models, extreme gradient boosting (XGBoost) performed the best, with an area under the receiver operating characteristic curve (AUC) of 0.982, sensitivity of 0.913, and specificity of 0.998. The main features used in the XGBoost model were direct and indirect bilirubin, partial activation prothrombin time, prealbumin, creatinine, D-dimer, and C-reactive protein. Shapley Additive Explanations analysis was conducted to further analyze these features. This study presents a model for early diagnosis DVT or PE after THA and demonstrates bilirubin could be a potential predictor in the assessment of DVT or PE. Compared to traditional risk assessment, XGBoost has a high sensitivity and specificity to predict DVT and PE in the clinical setting. Furthermore, the results of this study were converted into a web calculator that can be used in clinical practice.
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Ding et al. (2023) conducted an observational in Deep vein thrombosis and pulmonary embolism after total hip arthroplasty (n=1,481). XGBoost machine learning prediction model vs. Traditional risk scores (PLOS, Rapt, Geneva) was evaluated on Prediction of DVT and PE at discharge (measured by AUC) (AUC 0.982, 95% CI 0.954-1.000). An XGBoost machine learning model based on preoperative clinical features effectively predicted deep vein thrombosis and pulmonary embolism after total hip arthroplasty with an AUC of 0.982.
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