Discrimination capability of pretest probability of stable coronary artery disease: a systematic review and meta-analysis suggesting how to improve validation procedures

OBJECTIVE: Externally validated pretest probability models for risk stratification of subjects with chest pain and suspected stable coronary artery disease (CAD), determined through invasive coronary angiography or coronary CT angiography, are analysed to characterise the best validation procedures in terms of discriminatory ability, predictive variables and method completeness. DESIGN: Systematic review and meta-analysis. DATA SOURCES: Global Health (Ovid), Healthstar (Ovid) and MEDLINE (Ovid) searched on 22 April 2020. ELIGIBILITY CRITERIA: We included studies validating pretest models for the first-line assessment of patients with chest pain and suspected stable CAD. Reasons for exclusion: acute coronary syndrome, unstable chest pain, a history of myocardial infarction or previous revascularisation; models referring to diagnostic procedures different from the usual practices of the first-line assessment; univariable models; lack of quantitative discrimination capability. METHODS: Eligibility screening and review were performed independently by all the authors. Disagreements were resolved by consensus among all the authors. The quality assessment of studies conforms to the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2). A random effects meta-analysis of area under the receiver operating characteristic curve (AUC) values for each validated model was performed. RESULTS: 27 studies were included for a total of 15 models. Besides age, sex and symptom typicality, other risk factors are smoking, hypertension, diabetes mellitus and dyslipidaemia. Only one model considers genetic profile. AUC values range from 0.51 to 0.81. Significant heterogeneity (p<0.003) was found in all but two cases (p>0.12). Values of I(2) >90% for most analyses and not significant meta-regression results undermined relevant interpretations. A detailed discussion of individual results was then carried out. CONCLUSIONS: We recommend a clearer statement of endpoints, their consistent measurement both in the derivation and validation phases, more comprehensive validation analyses and the enhancement of threshold validations to assess the effects of pretest models on clinical management. PROSPERO REGISTRATION NUMBER: CRD42019139388.