Diagnostic performance of the 2019 ESC pre-test probability and Coronary Artery Disease consortium models in estimating obstructive coronary artery disease
https://doi.org/10.20996/1819-6446-2025-3127
EDN: FLGXNG
Abstract
Aim. To evaluate and compare the diagnostic performance of the 2019 European Society of Cardiology pre-test probability (PTP) model and the coronary artery disease (CAD) consortium basic and clinical models in predicting obstructive CAD in patients with stable angina.
Material and methods. This cross-sectional study included 366 patients (mean age 64.8 years, 62.6% male) with suspected stable angina who underwent coronary computed tomography angiography. Obstructive CAD was defined as the presence of ≥50% stenosis in epicardial coronary artery segments with a diameter of ≥2.5 mm. We assessed clinical characteristics and cardiovascular risk factors. The PTP values from the three models were calculated, and their diagnostic performance was evaluated using area under the receiver operating characteristic curves and the Hosmer-Lemeshow test for calibration. Sensitivity, specificity, and predictive values were also analyzed.
Results. Obstructive CAD was detected in 270 (73.8%) patients. Patients with obstructive CAD had higher rates of male sex, hypertension, dyslipidemia, smoking, and typical and atypical angina (all p<0.05). The CAD consortium clinical model provided the most accurate estimate of obstructive CAD prevalence in high-risk patients (76.6% expected vs 84.4% observed), while the 2019 ESC PTP model was more accurate in low-risk patients (2.5% expected vs 0.4% observed). The CAD consortium clinical model demonstrated the best diagnostic performance with an area under the curve (AUC) of 0.760 and good calibration (Hosmer-Lemeshow test, p=0.823). This was followed by the CAD consortium basic model (AUC=0.755), and the 2019 ESC PTP model, which had the lowest performance (AUC=0.701, poor calibration, p=0.001). The CAD consortium clinical model, with a cut-of value >33%, had a sensitivity of 66.7%, specificity of 79.2%, a positive predictive value of 90%, and a negative predictive value of 45.8% in predicting obstructive CAD.
Conclusion. The CAD consortium clinical model showed superior accuracy in predicting obstructive CAD in stable angina patients, especially in high-risk groups, compared to the 2019 ESC PTP and CAD consortium basic models. Its strong diagnostic performance and reliable calibration make it a better tool for CAD risk assessment.
About the Authors
Huy Truong HoangViet Nam
Ho Chi Minh City
Dan Van Buu Do
Viet Nam
Ho Chi Minh City
Thao Le Phuong Nguyen
Viet Nam
Ho Chi Minh City
V. V. Maiskov
Russian Federation
Victor V. Maiskov
Moscow
Z. D. Kobalava
Russian Federation
Zhanna D. Kobalava
Moscow
References
1. Gandhi S, Garratt KN, Li S, et al. Ten-Year Trends in Patient Characteristics, Treatments, and Outcomes in Myocardial Infarction from National Cardiovascular Data Registry Chest Pain-MI Registry. Circ Cardiovasc Qual Outcomes. 2022;15(1):E008112. DOI:10.1161/CIRCOUTCOMES.121.008112.
2. Safiri S, Karamzad N, Singh K, et al. Burden of ischemic heart disease and its attributable risk factors in 204 countries and territories, 1990-2019. Eur J Prev Cardiol. 2022;29(2):420-31. DOI:10.3389/fcvm.2022.868370.
3. Hoorweg BB, Willemsen RT, Cleef LE, et al. Frequency of chest pain in primary care, diagnostic tests performed and final diagnoses. Heart. 2017;103(21):1727- 32. DOI:10.1136/heartjnl-2016-310905.
4. Knuuti J, Ballo H, Juarez-Orozco LE, et al. The performance of non-invasive tests to rule-in and rule-out significant coronary artery stenosis in patients with stable angina: A meta-analysis focused on post-test disease probability. Eur Heart J. 2018;39(35):3322-30. DOI:10.1093/eurheartj/ehy267.
5. Hecht HS, Shaw L, Chandrashekhar YS, et al. Should NICE guidelines be universally accepted for the evaluation of stable coronary disease? A debate. Eur Heart J. 2019;40(18):1440-53. DOI:10.1093/eurheartj/ehz024.
6. Fihn SD, Gardin JM, Abrams J, et al; American College of Cardiology Foundation. 2012 ACCF/AHA/ACP/AATS/PCNA/SCAI/STS guideline for the diagnosis and management of patients with stable ischemic heart disease: executive summary: a report of the American College of Cardiology Foundation/American Heart Association task force on practice guidelines, and the American College of Physicians, American Association for Thoracic Surgery, Preventive Cardiovascular Nurses Association, Society for Cardiovascular Angiography and Interventions, and Society of Thoracic Surgeons. Circulation. 2012;126(25):3097-137. DOI:10.1161/CIR.0b013e3182776f83.
7. Task Force Members; Montalescot G, Sechtem U, Achenbach S, et al. 2013 ESC guidelines on the management of stable coronary artery disease of the European Society of Cardiology. Eur Heart J. 2013;34(38):2949-3003. DOI:10.1093/eurheartj/eht296.
8. Rademaker AA, Danad I, Groothuis JG, et al. Comparison of different cardiac risk scores for coronary artery disease in symptomatic women: Do female-specific risk factors matter? Eur J Prev Cardiol. 2014;21(11):1443-50. DOI:10.1177/2047487313494571.
9. Pickett CA, Hulten EA, Goyal M, et al. Accuracy of traditional age, gender and symptom based pre-test estimation of angiographically significant coronary artery disease in patients referred for coronary computed tomographic angiography. Am J Cardiol. 2013;112(2):208-11. DOI:10.1016/j.amjcard.2013.03.015.
10. Knuuti J, Wijns W, Saraste A, et al; ESC Scientific Document Group. 2019 ESC Guidelines for the diagnosis and management of chronic coronary syndromes. Eur Heart J. 2020;41(3):407-77. DOI:10.1093/eurheartj/ehz425.
11. Juarez-Orozco LE, Saraste A, Capodanno D, et al. Impact of a decreasing pre-test probability on the performance of diagnostic tests for coronary artery disease. Eur Heart J Cardiovasc Imaging. 2019;20(11):1198-207. DOI:10.1093/ehjci/jez054.
12. Baskaran L, Danad I, Gransar H, et al. A Comparison of the Updated DiamondForrester, CAD Consortium, and CONFIRM History-Based Risk Scores for Predicting Obstructive Coronary Artery Disease in Patients With Stable Chest Pain: The SCOT-HEART Coronary CTA Cohort. JACC Cardiovasc Imaging. 2019;12(7):1392-400. DOI:10.1016/j.jcmg.2018.02.020.
13. Bittencourt MS, Hulten E, Polonsky TS, et al. European Society of CardiologyRecommended Coronary Artery Disease Consortium Pretest Probability Scores More Accurately Predict Obstructive Coronary Disease and Cardiovascular Events Than the Diamond and Forrester Score: The Partners Registry. Circulation. 2016;134(3):201-11. DOI:10.1161/CIRCULATIONAHA.116.023396.
14. Writing Committee Members; Gulati M, Levy PD, Mukherjee D, et al. 2021 AHA/ ACC/ASE/CHEST/SAEM/SCCT/SCMR Guideline for the Evaluation and Diagnosis of Chest Pain: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. J Am Coll Cardiol. 2021;78(22):e187-e285. DOI:10.1016/j.jacc.2021.07.053.
15. Lawton JS, Tamis-Holland JE, Bangalore S, et al. 2021 ACC/AHA/SCAI Guideline for Coronary Artery Revascularization: Executive Summary: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. Circulation. 2022;145(3):e4-e17. DOI:10.1161/CIR.0000000000001039.
16. Williams B, Mancia G, Spiering W, et al; ESC Scientific Document Group. 2018 ESC/ESH Guidelines for the management of arterial hypertension. Eur Heart J. 2018;39(33):3021-104. DOI:10.1093/eurheartj/ehy339.
17. American Diabetes Association. Standards of Medical Care in Diabetes — 2022 Abridged for Primary Care Providers. Clin Diabetes. 2022;50(1):10-38. DOI:10.2337/cd17-0119.
18. Parascandola M, Augustson E, Rose A. Characteristics of current and recent former smokers associated with the use of new potential reduced-exposure tobacco products. Nicotine Tob Res. 2009;11(12):1431-38. DOI:10.1093/ntr/ntp157.
19. National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III). Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) final report. Circulation. 2002;106(25):3143-421.
20. Kavey RE, Daniels SR, Lauer RM, et al. American Heart Association guidelines for primary prevention of atherosclerotic cardiovascular disease beginning in childhood. Circulation. 2003;107(11):1562-6. DOI:10.1161/01.cir.0000061521.15730.6e.
21. Michos ED, Choi AD. Coronary Artery Disease in Young Adults: A Hard Lesson But a Good Teacher. J Am Coll Cardiol. 2019;74(15):1879-82. DOI:10.1016/j.jacc.2019.08.1023.
22. DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics. 1988;44(3):837-45.
23. Foldyna B, Udelson JE, Karády J, et al. Pretest probability for patients with suspected obstructive coronary artery disease: Re-evaluating DiamondForrester for the contemporary era and clinical implications: Insights from the PROMISE trial. Eur Heart J Cardiovasc Imaging. 2019;20(5):574-81. DOI:10.1093/ehjci/jey182.
24. Bing R, Singh T, Dweck MR, et al. Validation of European Society of Cardiology pre-test probabilities for obstructive coronary artery disease in suspected stable angina. Eur Hear J Qual Care Clin Outcomes. 2020;6(4):293-300. DOI:10.1093/ehjqcco/qcaa006.
25. Lee UW, Ahn S, Shin YS, et al. Comparison of the CAD consortium and updated Diamond-Forrester scores for predicting obstructive coronary artery disease. Am J Emerg Med. 2021;43:200-4. DOI:10.1016/j.ajem.2020.02.056.
26. Zheng J, Hou Z, Yin W, et al. Performance of the 2019 ESC pre-test probability model in predicting obstructive coronary artery disease in a Chinese population using coronary computed tomography angiography outcomes. J Cardiovasc Comput Tomogr. 2024;18(4):408-15. DOI:10.1016/j.jcct.2024.04.011.
27. Chen T, Shao D, Zhao J, et al. Comparison of the RF-CL and CACS-CL models to estimate the pretest probability of obstructive coronary artery disease and predict prognosis in patients with stable chest pain and diabetes mellitus. Front Cardiovasc Med. 2024;11:1368743. DOI:10.3389/fcvm.2024.1368743.
28. Vranic I, Stankovic I, Ignjatovic A, et al. Validation of the European Society of Cardiology pretest probability models for obstructive coronary artery disease in high-risk population. Hellenic J Cardiol. 2024:S1109-9666(24)00107-6. DOI:10.1016/j.hjc.2024.05.003.
29. Winther S, Murphy T, Schmidt SE, et al. Performance of the American Heart Association/American College of Cardiology Guideline-Recommended Pretest Probability Model for the Diagnosis of Obstructive Coronary Artery Disease. J Am Heart Assoc. 2022;11(24):e027260. DOI:10.1161/JAHA.122.027260.
30. Genders TS, Steyerberg EW, Alkadhi H, et al. A clinical prediction rule for the diagnosis of coronary artery disease: Validation, updating, and extension. Eur Heart J. 2011;32(11):1316-30. DOI:10.1093/eurheartj/ehr014.
31. Genders TS, Steyerberg EW, Hunink MG, et al. Prediction model to estimate presence of coronary artery disease: Retrospective pooled analysis of existing cohorts. BMJ. 2012;344:e3485. DOI:10.1136/bmj.e3485.
32. Almeida J, Fonseca P, Dias T, 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(4): 223-8. DOI:10.1002/clc.22515.
33. Jensen JM, Voss M, Hansen VB, et al. Risk stratification of patients suspected of coronary artery disease: Comparison of five different models. Atherosclerosis. 2012;220(2):557-62. DOI:10.1016/j.atherosclerosis.2011.11.027.
Review
For citations:
Hoang H.T., Do D.V., Nguyen T.L., Maiskov V.V., Kobalava Z.D. Diagnostic performance of the 2019 ESC pre-test probability and Coronary Artery Disease consortium models in estimating obstructive coronary artery disease. Rational Pharmacotherapy in Cardiology. 2025;21(2):98-107. (In Russ.) https://doi.org/10.20996/1819-6446-2025-3127. EDN: FLGXNG