Objectives
An overuse of invasive and non-invasive anatomical testing for the diagnosis of coronary artery disease (CAD) affects patients’ and healthcare professionals’ safety, and the sustainability of Healthcare Systems. Pre-test probability (PTP) models can be routinely used as gatekeeper for initial patient management. Several PTP models have been developed after the seminal work of Diamond and Forrester in the late 1970s, however to assess their generalizability to different populations extended validation procedures should be carried out and their results carefully analyzed.
Methods
A systematic review has been carried out to assess the discrimination capabilities of PTP models validated on external populations. The main metric was the area under the ROC curve (AUC). A comprehensive search has been done in MEDLINE®, HealthSTAR, and Global Health databases on 22 April 2020. The review conforms to the PRISMA statement; protocol was registered in PROSPERO (CRD42019139388).
Results
Nearly all the models considered in the 27 analysed papers include age, sex, and chest pain symptoms. Other common risk factors are smoking, hypertension, diabetes mellitus and dyslipidaemia. Only one model considers genetic profile. Reported AUCs range from 0.51 to 0.81. Relevant heterogeneity sources have been highlighted, such as the sample size, the presence of a PTP cut-off and the adoption of different definitions of CAD which can prevent comparisons of results and meta-analysis. Very few papers address a complete validation, making then impossible to understand the reasons why the model does not show a good discrimination capability on a different data set.
Conclusions
We recommend a more clear statement of endpoints, their consistent measurement both in the derivation and validation phases, more comprehensive validation analyses and the enhancement of threshold validations of PTP to assess the effects of PTP on clinical management.
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