Key result
An ensemble machine learning model based on electronic health records predicted deep vein thrombosis risk after knee/hip arthroplasty with an AUC of 0.9206 (95% CI 0.8956-0.9364).
Why the study?
Deep vein thrombosis is a common complication of knee or hip arthroplasty, leaving a continued need for artificial intelligence-based risk prediction methods.
Do machine learning models based on electronic health records improve the prediction of deep vein thrombosis risk after knee/hip arthroplasty compared to the Caprini score?
Observational (n=6,897)
Do machine learning models based on electronic health records improve the prediction of deep vein thrombosis risk after knee/hip arthroplasty compared to the Caprini score?
Effect estimate: AUC 0.9206 (95% CI 0.8956-0.9364)
Absolute Event Rate: 0.9206% vs 0.5703%
Machine learning models based on electronic health records, particularly an ensemble model, demonstrate high accuracy in predicting deep vein thrombosis risk after knee/hip arthroplasty, significantly outperforming the traditional Caprini score.
ML models may aid DVT risk stratification post-arthroplasty; hypothesis-generating and requires prospective validation before clinical use.
Deep vein thrombosis (DVT) is a common postoperative complication of knee/hip arthroplasty. There is a continued need for artificial intelligence-based methods of predicting lower extremity DVT risk after knee/hip arthroplasty. In this study, we performed a retrospective study to analyse the data from patients who underwent primary knee/hip arthroplasty between January 2017 and December 2021 with postoperative bilateral lower extremity venous ultrasonography. Patients' features were extracted from electronic health records (EHRs) and assigned to the training (80%) and test (20%) datasets using six models: eXtreme gradient boosting, random forest, support vector machines, logistic regression, ensemble, and backpropagation neural network. The Caprini score was calculated according to the Caprini score measurement scale, and the corresponding optimal cut-off Caprini score was calculated according to the largest Youden index. In total, 6897 cases of knee/hip arthroplasty were included (average age, 65.5 ± 8.9 years; 1702 men), among which 1161 (16.8%) were positive and 5736 (83.2%) were negative for deep vein thrombosis. Among the six models, the ensemble model had the highest area under the curve [0.9206 (0.8956, 0.9364)], with a sensitivity, specificity, positive predictive value, negative predictive value, and F1 score of 0.8027, 0.9059, 0.6100, 0.9573 and 0.7003, respectively. The corresponding optimal cut-off Caprini score was 10, with an area under the curve, sensitivity, specificity, positive predictive value, and negative predictive values of 0.5703, 0.8915, 0.2491, 0.1937, 0.9191, and 0.3183, respectively. In conclusion, machine learning models based on EHRs can help predict the risk of deep vein thrombosis after knee/hip arthroplasty.
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Wang et al. (2023) conducted an observational in Deep vein thrombosis after knee/hip arthroplasty (n=6,897). Ensemble machine learning model vs. Caprini score was evaluated on Area under the curve (AUC) for predicting deep vein thrombosis (AUC 0.9206, 95% CI 0.8956-0.9364). An ensemble machine learning model based on electronic health records predicted deep vein thrombosis risk after knee/hip arthroplasty with an AUC of 0.9206 (95% CI 0.8956-0.9364).
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