New Algorithm for the Prediction of Cardiovascular Risk in Symptomatic Adults with Stable Chest Pain

  • Muralidhar R. Papireddy
  • Carl J. Lavie
  • Abhizith Deoker
  • Hadii Mamudu
  • Timir K. Paul
Ischemic Heart Disease (D Mukherjee, Section Editor)
Part of the following topical collections:
  1. Topical Collection on Ischemic Heart Disease


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.


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.


Algorithms 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.

  1. 1.
    Mozaffarian D, Benjamin EJ, Go AS, Arnett DK, Blaha MJ, Cushman M, et al. Heart disease and stroke statistics-2015 update: a report from the American Heart Association. Circulation. 2015;131:e29–39.CrossRefPubMedGoogle Scholar
  2. 2.
    Patel MR, Peterson ED, Dai D, Brennan JM, Redberg RF, Anderson HV, et al. Low diagnostic yield of elective coronary angiography. N Engl J Med. 2010;362:886–95.CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Wolk MJ, Bailey SR, Doherty JU, et al. ACCF/AHA/ASE/ASNC/HFSA/HRS/SCAI/SCCT/SCMR/STS 2013 multimodality appropriate use criteria for the detection and risk assessment of stable ischemic heart disease. J Card Fail. 2014;20:65–90.CrossRefPubMedGoogle Scholar
  4. 4.
    Wilson PW, D’Agostino RB, Levy D, Belanger AM, Silbershatz H, Kannel WB. Prediction of coronary heart disease using risk factor categories. Circulation. 1998;97:1837–47.CrossRefPubMedGoogle Scholar
  5. 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
  6. 6.
    Diamond GA, Forrester JS. Analysis of probability as an aid in the clinical diagnosis of coronary-artery disease. N Engl J Med. 1979;300:1350–8.CrossRefPubMedGoogle Scholar
  7. 7.
    Pryor DB, Harrell FE, Lee KL, Califf RM, R a R. Estimating the likelihood of significant coronary artery disease. Am J Med. 1983;75:771–80.CrossRefPubMedGoogle Scholar
  8. 8.
    Genders TSS, Steyerberg EW, Alkadhi H, Leschka S, Desbiolles L, Nieman K, et al. A clinical prediction rule for the diagnosis of coronary artery disease: validation, updating, and extension. Eur Heart J. 2011;32:1316–30.CrossRefPubMedGoogle Scholar
  9. 9.
    Genders TSS, Steyerberg EW, Hunink MGM, Nieman K, Galema TW, Mollet NR, et al. Prediction model to estimate presence of coronary artery disease: retrospective pooled analysis of existing cohorts. BMJ. 2012;344:e3485.CrossRefPubMedPubMedCentralGoogle Scholar
  10. 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. 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
  12. 12.
    Douglas PS, Hoffmann U, Patel MR, Mark DB, al-Khalidi HR, Cavanaugh B, et al. Outcomes of anatomical versus functional testing for coronary artery disease. N Engl J Med. 2015;372:1291–300.CrossRefPubMedPubMedCentralGoogle Scholar
  13. 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. 14.
    Sackett DL, Haynes RB. Architecture of diagnostic research. In: Evid. Base Clin. Diagnosis; 2002. p. 19–38.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Muralidhar R. Papireddy
    • 1
  • Carl J. Lavie
    • 2
  • Abhizith Deoker
    • 3
  • Hadii Mamudu
    • 4
  • Timir K. Paul
    • 1
  1. 1.Division of Cardiology, Department of Internal Medicine, Quillen College of MedicineEast Tennessee State UniversityJohnson CityUSA
  2. 2.Department of Cardiology, Ochsner Clinical SchoolThe University of Queensland School of MedicineNew OrleansUSA
  3. 3.Division of Cardiology, Department of Internal MedicineTexas Tech UniversityEl PasoUSA
  4. 4.Department of Health Services Management and Policy, College of Public HealthEast Tennessee State UniversityJohnson CityUSA

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