在膝盖/关节整形术后深静脉血栓的预测模型:系统性审查和网络元分析
Qingqing Zeng1,2, Zhuolan Li1,2, Sijie Gui3
1School of Nursing, Hengyang Medical School, University of South China, Hengyang, China.
Journal of orthopaedic surgery (Hong Kong)
|May 6, 2024
概括
关节置换后的深静脉血栓 (DVT) 是一个严重的问题. 在对DVT风险预测进行评估的八种模型中,XGBoost模型显示出最好的预测性能.
科学领域:
- 医疗信息学 医疗信息学
- 生物统计学 生物统计学
- 整形外科手术 整形外科手术
背景情况:
- 深静脉血栓塞 (DVT) 是膝盖和关节整形术后的常见并发症.
- 严重的DVT显著损害了患者在关节置换手术后的生活质量.
研究的目的:
- 系统地比较膝关节/关节整形手术后 DVT 的各种模型的预测性能.
- 在这个患者群体中确定DVT的最佳预测模型.
主要方法:
- 在9个数据库中进行了系统的文献搜索,确定了11项研究.
- 选择了8种不同的DVT术后预测模型,并使用网络元分析进行了比较.
- 模型的性能使用Sugumar 基于实用性的复合评估 (SUCRA) 度量来评估.
主要成果:
- XGBoost模型实现了最高的预测性能 (SUCRA 100.0%).
- 其他评估的模型包括LASSO (84.8%),ANN (72.1%),SVM (53.0%),ensemble (40.8%),RF (25.6%),LR (21.8%) 和GBT (1.1%).这些模型中,LASSO是最常见的模型.
- 在预测DVT方面,XGBoost显著超过了所有其他模型.
结论:
- 推XGBoost模型作为在膝盖/关节整形术后预测DVT的优越选择.
- 需要进一步的精心设计的研究来验证XGBoost模型在临床实践中的性能.
- 这项研究为开发和完善DVT预测策略提供了宝贵的见解.
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