Prediction models for deep vein thrombosis after knee/hip arthroplasty: A systematic review and network meta-analysis

Abstract

Deep vein thrombosis (DVT) is one of the common complications after joint replacement, which seriously affects the quality of life of patients. We systematically searched nine databases, a total of eleven studies on prediction models to predict DVT after knee/hip arthroplasty were included, eight prediction models for DVT after knee/hip arthroplasty were chosen and compared. The results of network meta-analysis showed the XGBoost model (SUCRA 100.0%), LASSO (SUCRA 84.8%), ANN (SUCRA 72.1%), SVM (SUCRA 53.0%), ensemble model (SUCRA 40.8%), RF (SUCRA 25.6%), LR (SUCRA 21.8%), GBT (SUCRA 1.1%), and best prediction performance is XGB (SUCRA 100%). Results show that the XGBoost model has the best predictive performance. Our study provides suggestions and directions for future research on the DVT prediction model. In the future, well-designed studies are still needed to validate this model.