Prediction models for deep vein thrombosis after knee/hip arthroplasty: A systematic review and network meta-analysis
| Autor: | Qingqing Zeng, Zhuolan Li, Sijie Gui, Jingjing Wu, Caijuan Liu, Ting Wang, Dan Peng, Guqing Zeng |
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| Jazyk: | angličtina |
| Rok vydání: | 2024 |
| Předmět: | |
| Zdroj: | Journal of Orthopaedic Surgery, Vol 32 (2024) |
| Druh dokumentu: | article |
| ISSN: | 2309-4990 10225536 |
| DOI: | 10.1177/10225536241249591 |
| Popis: | 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. |
| Databáze: | Directory of Open Access Journals |
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