目的 系统评价膝骨关节炎(knee osteoarthritis,KOA)的发病风险预测模型.方法 计算机检索CNKI、WanFang Data、VIP、PubMed、Embase、Web of Science 和 Cochrane Library 数据库,搜集 KOA 风险预测模型的相关研究,检索时限均为建库至2024年4月.由2名研究员独立筛选文献和提取数据后,采用PROBAST偏倚风险评估工具对风险预测模型进行偏倚风险和适用性评价.结果 共纳入12篇文献,包含21个KOA发病风险预测模型.预测因子数量在3～12个之间,最常见的预测因子为年龄、性别、BMI.纳入模型的建模受试者工作特征曲线下面积(area under curve,AUC)范围为0.554～0.948,验模AUC范围为0.6～0.94.模型总体预测性能一般,且整体偏倚风险高,超过一半的模型未进行外部验证.结论 目前KOA发病风险预测模型整体质量和适用性还有较大提升空间,未来建模可遵循CHARMS和PROBAST降低偏倚风险,探索多种建模方法的组合,并加强对模型的外部验证.
Predictive model for the risk of knee osteoarthritis:a systematic review
Objective To systematically evaluate the risk prediction model of knee osteoarthritis(KOA).Methods The CNKI,WanFang Data,VIP,PubMed,Embase,Web of Science and Cochrane Library databases were electronically searched to collect relevant studies on KOA's risk prediction model from inception to April,2024.After study screening and data extraction by two independent researchers,the PROBAST bias risk assessment tool was used to evaluate the bias risk and applicability of the risk prediction model.Results A total of 12 studies involving 21 risk prediction models for KOA were included.The number of predictors ranged from 3 to 12,and the most common predictors were age,sex,and BMI.The range of modeling AUC included in the model was 0.554-0.948,and the range of testing AUC was 0.6-0.94.The overall predictive performance of the models was mediocre and the risk of overall bias was high,and more than half of the models were not externally verified.Conclusion At present,the overall quality and applicability of the KOA morbidity risk prediction model still have great room for improvement.Future modeling should follow the CHARMS and PROBAST to reduce the risk of bias,explore the combination of multiple modeling methods,and strengthen the external verification of the model.
万方数据
维普
