The accuracy of various models in predicting coronary artery disease in the world: A systematic review
- VernacularTitle:全球冠心病预测模型准确性的系统评价
- Author:
Fangzhou LI
1
;
Xiaoting SU
1
;
Runchen SUN
1
;
Shen LIN
1
;
Zhe ZHENG
2
,
3
,
4
Author Information
1. National Clinical Research Center of Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100037, P.R.China
2. 1.National Clinical Research Center of Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100037, P.R.China
3. 2. Department of Cardiovascular Surgery, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100037, P.R.China
4. 3.National Health Commission Key Laboratory of Cardiovascular Regenerative Medicine, Fuwai Central-China Hospital, Central-China Branch of National Center for Cardiovascular Diseases, Zhengzhou, 451464, P.R.China
- Publication Type:Journal Article
- Keywords:
Coronary artery disease;
model;
predictive efficacy;
external validation;
systematic review
- From:
Chinese Journal of Clinical Thoracic and Cardiovascular Surgery
2021;28(03):288-298
- CountryChina
- Language:Chinese
-
Abstract:
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.
