New Algorithm for the Prediction of Cardiovascular Risk in Symptomatic Adults with Stable Chest Pain
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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.
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.
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.
KeywordsAlgorithms Cardiovascular risk Stable chest pain Pre-test probability Coronary artery disease
Compliance with Ethical Standards
Conflict of Interest
Muralidhar R. Papireddy, Carl J. Lavie, Abhizith Deoker, Hadii Mamudu, and Timir K. Paul declare that they have no conflict of interest.
Human and Animal Rights and Informed Consent
This article does not contain any studies with human or animal subjects performed by any of the authors.
Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance.
- 5.Goff DC, Lloyd-Jones DM, Bennett G, et al (2013) 2013 ACC/AHA guideline on the assessment of cardiovascular risk: a report of the American college of cardiology/American heart association task force on practice guidelines. Circulation 0:000–000.Google Scholar
- 10.• Almeida J, Fonseca P, Dias T, Ladeiras-Lopes R, Bettencourt N, Ribeiro J, et al. Comparison of coronary artery disease consortium 1 and 2 scores and Duke clinical score to predict obstructive coronary disease by invasive coronary angiography. Clin Cardiol. 2016;39:223–8. This study compared CAD Consortium Basic and Clinical Models with DCS Model in predicting CAD. CrossRefPubMedGoogle Scholar
- 11.• Bittencourt MS, Hulten E, Polonsky TS, Hoffman U, Nasir K, Abbara S, et al. European society of cardiology-recommended coronary artery disease consortium pretest probability scores more accurately predict obstructive coronary disease and cardiovascular events than the diamond and forrester score. Circulation. 2016;134:201–11. This study compared CAD Consortium Basic and Clinical Models with DF Model to predict CAD. CrossRefPubMedGoogle Scholar
- 13.•• Fordyce CB, Douglas PS, Roberts RS, et al. Identification of patients with stable chest pain deriving minimal value from noninvasive testing. JAMA Cardiol. 2017;2:400. This study identified 10 very low risk variables that is associated with lowest rates of abnormal test results and using these variables low-risk patients can be identified and managed with deferred testing strategy. CrossRefPubMedGoogle Scholar
- 14.Sackett DL, Haynes RB. Architecture of diagnostic research. In: Evid. Base Clin. Diagnosis; 2002. p. 19–38.Google Scholar