New Algorithm for the Prediction of Cardiovascular Risk in Symptomatic Adults with Stable Chest Pain
Purpose of Review: To review the landmark studies in predicting obstructive coronary artery disease (CAD) in symptomatic patients with stable chest pain and identify better prediction tools and propose a simplified algorithm to guide the health care providers in identifying low risk patients to defer further testing. Recent Findings: There are a few risk prediction models described for stable chest pain patients including Diamond-Forrester (DF), Duke Clinical Score (DCS), CAD Consortium Basic, Clinical, and Extended models. The CAD Consortium models demonstrated that DF and DCS models overestimate the probability of CAD. All CAD Consortium models performed well in the contemporary population. PROMISE trial secondary data results showed that a clinical tool using readily available ten very low-risk pre-test variables could discriminate low-risk patients to defer further testing safely. Summary: In the contemporary population, CAD Consortium Basic or Clinical model could be used with more confidence. Our proposed simple algorithm would guide the physicians in selecting low risk patients who can be managed conservatively with deferred testing strategy. Future research is needed to validate our proposed algorithm to identify the low-risk patients with stable chest pain for whom further testing may not be warranted.
Papireddy, Muralidhar R.; Lavie, Carl J.; Deoker, Abhizith; Mamudu, Hadii; and Paul, Timir K.. 2018. New Algorithm for the Prediction of Cardiovascular Risk in Symptomatic Adults with Stable Chest Pain. Current Cardiology Reports. Vol.20(5). https://doi.org/10.1007/s11886-018-0973-z PMID: 29574581 ISSN: 1523-3782