全球冠心病预测模型准确性的系统评价

目的对各种预测冠心病的模型进行系统回顾,展示其预测效能。方法计算机检索PubMed、EMbase和中国知网。纳入研究冠心病预测模型的文章。检索时限为建库至2020年9月30日。由2名评价员独立按照纳入和排除标准筛选文献,提取建模信息、预测效能等资料。结果共纳入30篇文章,报道了19个经过外部验证的预测模型,17个模型具有受试者工作特征曲线下面积(area under curve,AUC)>0.7的外部验证组。经典的Diamond-Forrester模型、更新的Diamond-Forrester模型、杜克临床评分、冠心病联盟临床评分外部验证组的AUC在0.49~0.87之间。结论大部分模型具有适中的预测效能,经典的冠心病诊断预测模型在各人群中预测效能差异大。

The accuracy of various models in predicting coronary artery disease in the world: A systematic review

Objective To systematically review the models for predicting coronary artery disease(CAD)and demonstrate their predictive efficacy.Methods PubMed,EMbase and China National Knowledge Internet were searched comprehensively by computer.We included studies which were designed to develop and validate predictive models of CAD.The studies published from inception to September 30,2020 were searched.Two reviewers independently evaluated the studies according to the inclusion and exclusion criteria and extracted the baseline characteristics and metrics of model performance.Results A total of 30 studies were identified,and 19 diagnostic predictive models were for CAD.Seventeen models had external validation group with area under curve(AUC)>0.7.The AUC for the external validation of the traditional models,including Diamond-Forrester model,updated Diamond-Forrester model,Duke Clinical Score,CAD consortium clinical score,ranged from 0.49 to 0.87.Conclusion Most models have modest discriminative ability.The predictive efficacy of traditional models varies greatly among different populations.