Why the study?
To characterise the best validation procedures for externally validated pretest probability models for risk stratification in patients with chest pain and suspected stable CAD.
What is the discrimination capability of pretest probability models for risk stratification in patients with suspected stable CAD?
What is the discrimination capability of pretest probability models for risk stratification in patients with suspected stable CAD?
The meta-analysis highlights the need for clearer endpoints and more comprehensive validation analyses for pretest probability models in stable CAD due to high heterogeneity in current studies.
Heterogeneous discrimination of CAD pretest models in external validations; leaves open standardized procedures to guide testing decisions.
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: >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.
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Mincarone et al. (2021) studied this question.
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