The accuracy of various models in predicting coronary artery disease in the world: A systematic review_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

LI Fangzhou ¹ , SU Xiaoting ¹ , SUN Runchen ¹ , LIN Shen ¹ ,  ZHENG Zhe ^1,2,3

1. National Clinical Research Center of Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100037, P.R.China;
2. Department of Cardiovascular Surgery, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100037, P.R.China;
3. National Health Commission Key Laboratory of Cardiovascular Regenerative Medicine, Fuwai Central-China Hospital, Central-China Branch of National Center for Cardiovascular Diseases, Zhengzhou, 451464, P.R.China;

Corresponding?author：

ZHENG Zhe, Email: zhengzhe@fuwai.com

Keywords：

Coronary artery disease; model; predictive efficacy; external validation; systematic review

DOI：

10.7507/1007-4848.202011080

Video：

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Objective To systematically review the models for predicting coronary artery disease (CAD) and demonstrate their predictive efficacy. Methods PubMed, EMbase and China National Knowledge Internet were searched comprehensively by computer. We included studies which were designed to develop and validate predictive models of CAD. The studies published from inception to September 30, 2020 were searched. Two reviewers independently evaluated the studies according to the inclusion and exclusion criteria and extracted the baseline characteristics and metrics of model performance.Results A total of 30 studies were identified, and 19 diagnostic predictive models were for CAD. Seventeen models had external validation group with area under curve (AUC)>0.7. The AUC for the external validation of the traditional models, including Diamond-Forrester model, updated Diamond-Forrester model, Duke Clinical Score, CAD consortium clinical score, ranged from 0.49 to 0.87.Conclusion Most models have modest discriminative ability. The predictive efficacy of traditional models varies greatly among different populations.

Citation： LI Fangzhou, SU Xiaoting, SUN Runchen, LIN Shen, ZHENG Zhe. The accuracy of various models in predicting coronary artery disease in the world: A systematic review. Chinese Journal of Clinical Thoracic and Cardiovascular Surgery, 2021, 28(3): 288-298. doi: 10.7507/1007-4848.202011080 Copy

1.	中國心血管健康與疾病報告編寫組. 中國心血管健康與疾病報告 2019 概要. 中國循環雜志, 2020, 35(9): 833-854.
2.	Patel MR, Peterson ED, Dai D, et al. Low diagnostic yield of elective coronary angiography. N Engl J Med, 2010, 362(10): 886-895.
3.	Collins GS, Moons KG. Comparing risk prediction models. BMJ, 2012, 344: e3186.
4.	Diamond GA, Forrester JS. Analysis of probability as an aid in the clinical diagnosis of coronary-artery disease. N Engl J Med, 1979, 300(24): 1350-1358.
5.	Pryor DB, Harrell FE Jr, Lee KL, et al. Estimating the likelihood of significant coronary artery disease. Am J Med, 1983, 75(5): 771-780.
6.	Morise AP, Haddad WJ, Beckner D. Development and validation of a clinical score to estimate the probability of coronary artery disease in men and women presenting with suspected coronary disease. Am J Med, 1997, 102(4): 350-356.
7.	Taylor CM, Humphries KH, Pu A, et al. A proposed clinical model for efficient utilization of invasive coronary angiography. Am J Cardiol, 2010, 106(4): 457-462.
8.	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): 1316-1330.
9.	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): 557-562.
10.	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.
11.	Fujimoto S, Kondo T, Yamamoto H, et al. Development of new risk score for pre-test probability of obstructive coronary artery disease based on coronary CT angiography. Heart Vessels, 2015, 30(5): 563-571.
12.	Min JK, Dunning A, Gransar H, et al. Medical history for prognostic risk assessment and diagnosis of stable patients with suspected coronary artery disease. Am J Med, 2015, 128(8): 871-878.
13.	Caselli C, Rovai D, Lorenzoni V, et al. A new integrated clinical-biohumoral model to predict functionally significant coronary artery disease in patients with chronic chest pain. Can J Cardiol, 2015, 31(6): 709-716.
14.	Yang Y, Chen L, Yam Y, et al. A clinical model to identify patients with high-risk coronary artery disease. JACC Cardiovasc Imaging, 2015, 8(4): 427-434.
15.	Liu Y, Li Q, Chen S, et al. A simple modified framingham scoring system to predict obstructive coronary artery disease. J Cardiovasc Transl Res, 2018, 11(6): 495-502.
16.	Adamson PD, Hunter A, Madsen DM, et al. High-sensitivity cardiac troponin I and the diagnosis of coronary artery disease in patients with suspected angina pectoris. Circ Cardiovasc Qual Outcomes, 2018, 11(2): e004227.
17.	Zhou LY, Yin WJ, Wang JL, et al. A novel laboratory-based model to predict the presence of obstructive coronary artery disease. Int Heart J, 2020, 61(3): 437-446.
18.	Zhou J, Chen Y, Zhang Y, et al. Epicardial fat volume improves the prediction of obstructive coronary artery disease above traditional risk factors and coronary calcium score. Circ Cardiovasc Imaging, 2019, 12(1): e008002.
19.	Lin S, Li Z, Fu B, et al. Feasibility of using deep learning to detect coronary artery disease based on facial photo. Eur Heart J, 2020: ehaa640.
20.	McCarthy CP, Neumann JT, Michelhaugh SA, et al. Derivation and external validation of a high-sensitivity cardiac troponin-based proteomic model to predict the presence of obstructive coronary artery disease. J Am Heart Assoc, 2020, 9(16): e017221.
21.	Rosenberg S, Elashoff MR, Beineke P, et al. Multicenter validation of the diagnostic accuracy of a blood-based gene expression test for assessing obstructive coronary artery disease in nondiabetic patients. Ann Intern Med, 2010, 153(7): 425-434.
22.	Chen ZW, Chen YH, Qian JY, et al. Validation of a novel clinical prediction score for severe coronary artery diseases before elective coronary angiography. PLoS One, 2014, 9(4): e94493.
23.	Isma'eel HA, Serhan M, Sakr GE, et al. Diamond-Forrester and Morise risk models perform poorly in predicting obstructive coronary disease in Middle Eastern Cohort. Int J Cardiol, 2016, 203(2016): 803-805.
24.	Pryor DB, Shaw L, McCants CB, et al. Value of the history and physical in identifying patients at increased risk for coronary artery disease. Ann Intern Med, 1993, 118(2): 81-90.
25.	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): 223-228.
26.	周伽, 楊俊杰, 周迎, 等. 驗前概率聯合冠脈 CT 造影對于穩定型冠心病的診斷價值. 解放軍醫學院學報, 2015, 36(4): 313-317.
27.	賀婷, 劉星, 李瑩, 等. 更新的 Diamond-Forrester 法和 Duke 臨床評分預測模型對可疑冠心病患者的診斷價值. 中國全科醫學, 2016, 19(20): 2440-2444.
28.	馮靜, 鄒愛春, 潘黎明, 等. 不同臨床評分結合耳褶征對于預測冠心病診斷價值的初步探討. 中國實驗診斷學, 2017, 21(6): 997-1000.
29.	王岳, 劉玉潔, 張穎, 等. 在中國穩定性胸痛患者中利用冠脈 CTA 對冠心病驗前概率模型進行驗證和比較. 天津醫藥, 2019, 47(2): 145-150.
30.	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, 2020. [Epub ahead of print].
31.	Edlinger M, Wanitschek M, D?rler J, et al. External validation and extension of a diagnostic model for obstructive coronary artery disease: a cross-sectional predictive evaluation in 4888 patients of the Austrian Coronary Artery disease Risk Determination In Innsbruck by diaGnostic ANgiography (CARDIIGAN) cohort. BMJ Open, 2017, 7(4): e014467.
32.	Genders TSS, Coles A, Hoffmann U, et al. The external validity of prediction models for the diagnosis of obstructive coronary artery disease in patients with stable chest pain: insights from the PROMISE Trial. JACC Cardiovasc Imaging, 2018, 11(3): 437-446.
33.	Teressa G, Bhasin V, Noack P, et al. Comparing the modified history, electrocardiogram, age, risk factors, and troponin score and coronary artery disease consortium model for predicting obstructive coronary artery disease and cardiovascular events in patients with acute chest pain. Crit Pathw Cardiol, 2019, 18(3): 125-129.
34.	Damen JA, Hooft L, Schuit E, et al. Prediction models for cardiovascular disease risk in the general population: systematic review. BMJ, 2016, 353: i2416.
35.	He T, Liu X, Xu N, et al. Diagnostic models of the pre-test probability of stable coronary artery disease: A systematic review. Clinics (Sao Paulo), 2017, 72(3): 188-196.
36.	Task Force Members, Montalescot G, Sechtem U, et al. 2013 ESC guidelines on the management of stable coronary artery disease: the Task Force on the management of stable coronary artery disease of the European Society of Cardiology. Eur Heart J, 2013, 34(38): 2949-3003.
37.	Fihn SD, Gardin JM, Abrams J, et al. 2012 ACCF/AHA/ACP/AATS/PCNA/SCAI/STS guideline for the diagnosis and management of patients with stable ischemic heart disease: 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. J Am Coll Cardiol, 2012, 60(24): e44-e164.

1. 中國心血管健康與疾病報告編寫組. 中國心血管健康與疾病報告 2019 概要. 中國循環雜志, 2020, 35(9): 833-854.
2. Patel MR, Peterson ED, Dai D, et al. Low diagnostic yield of elective coronary angiography. N Engl J Med, 2010, 362(10): 886-895.
3. Collins GS, Moons KG. Comparing risk prediction models. BMJ, 2012, 344: e3186.
4. Diamond GA, Forrester JS. Analysis of probability as an aid in the clinical diagnosis of coronary-artery disease. N Engl J Med, 1979, 300(24): 1350-1358.
5. Pryor DB, Harrell FE Jr, Lee KL, et al. Estimating the likelihood of significant coronary artery disease. Am J Med, 1983, 75(5): 771-780.
6. Morise AP, Haddad WJ, Beckner D. Development and validation of a clinical score to estimate the probability of coronary artery disease in men and women presenting with suspected coronary disease. Am J Med, 1997, 102(4): 350-356.
7. Taylor CM, Humphries KH, Pu A, et al. A proposed clinical model for efficient utilization of invasive coronary angiography. Am J Cardiol, 2010, 106(4): 457-462.
8. 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): 1316-1330.
9. 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): 557-562.
10. 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.
11. Fujimoto S, Kondo T, Yamamoto H, et al. Development of new risk score for pre-test probability of obstructive coronary artery disease based on coronary CT angiography. Heart Vessels, 2015, 30(5): 563-571.
12. Min JK, Dunning A, Gransar H, et al. Medical history for prognostic risk assessment and diagnosis of stable patients with suspected coronary artery disease. Am J Med, 2015, 128(8): 871-878.
13. Caselli C, Rovai D, Lorenzoni V, et al. A new integrated clinical-biohumoral model to predict functionally significant coronary artery disease in patients with chronic chest pain. Can J Cardiol, 2015, 31(6): 709-716.
14. Yang Y, Chen L, Yam Y, et al. A clinical model to identify patients with high-risk coronary artery disease. JACC Cardiovasc Imaging, 2015, 8(4): 427-434.
15. Liu Y, Li Q, Chen S, et al. A simple modified framingham scoring system to predict obstructive coronary artery disease. J Cardiovasc Transl Res, 2018, 11(6): 495-502.
16. Adamson PD, Hunter A, Madsen DM, et al. High-sensitivity cardiac troponin I and the diagnosis of coronary artery disease in patients with suspected angina pectoris. Circ Cardiovasc Qual Outcomes, 2018, 11(2): e004227.
17. Zhou LY, Yin WJ, Wang JL, et al. A novel laboratory-based model to predict the presence of obstructive coronary artery disease. Int Heart J, 2020, 61(3): 437-446.
18. Zhou J, Chen Y, Zhang Y, et al. Epicardial fat volume improves the prediction of obstructive coronary artery disease above traditional risk factors and coronary calcium score. Circ Cardiovasc Imaging, 2019, 12(1): e008002.
19. Lin S, Li Z, Fu B, et al. Feasibility of using deep learning to detect coronary artery disease based on facial photo. Eur Heart J, 2020: ehaa640.
20. McCarthy CP, Neumann JT, Michelhaugh SA, et al. Derivation and external validation of a high-sensitivity cardiac troponin-based proteomic model to predict the presence of obstructive coronary artery disease. J Am Heart Assoc, 2020, 9(16): e017221.
21. Rosenberg S, Elashoff MR, Beineke P, et al. Multicenter validation of the diagnostic accuracy of a blood-based gene expression test for assessing obstructive coronary artery disease in nondiabetic patients. Ann Intern Med, 2010, 153(7): 425-434.
22. Chen ZW, Chen YH, Qian JY, et al. Validation of a novel clinical prediction score for severe coronary artery diseases before elective coronary angiography. PLoS One, 2014, 9(4): e94493.
23. Isma'eel HA, Serhan M, Sakr GE, et al. Diamond-Forrester and Morise risk models perform poorly in predicting obstructive coronary disease in Middle Eastern Cohort. Int J Cardiol, 2016, 203(2016): 803-805.
24. Pryor DB, Shaw L, McCants CB, et al. Value of the history and physical in identifying patients at increased risk for coronary artery disease. Ann Intern Med, 1993, 118(2): 81-90.
25. 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): 223-228.
26. 周伽, 楊俊杰, 周迎, 等. 驗前概率聯合冠脈 CT 造影對于穩定型冠心病的診斷價值. 解放軍醫學院學報, 2015, 36(4): 313-317.
27. 賀婷, 劉星, 李瑩, 等. 更新的 Diamond-Forrester 法和 Duke 臨床評分預測模型對可疑冠心病患者的診斷價值. 中國全科醫學, 2016, 19(20): 2440-2444.
28. 馮靜, 鄒愛春, 潘黎明, 等. 不同臨床評分結合耳褶征對于預測冠心病診斷價值的初步探討. 中國實驗診斷學, 2017, 21(6): 997-1000.
29. 王岳, 劉玉潔, 張穎, 等. 在中國穩定性胸痛患者中利用冠脈 CTA 對冠心病驗前概率模型進行驗證和比較. 天津醫藥, 2019, 47(2): 145-150.
30. 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, 2020. [Epub ahead of print].
31. Edlinger M, Wanitschek M, D?rler J, et al. External validation and extension of a diagnostic model for obstructive coronary artery disease: a cross-sectional predictive evaluation in 4888 patients of the Austrian Coronary Artery disease Risk Determination In Innsbruck by diaGnostic ANgiography (CARDIIGAN) cohort. BMJ Open, 2017, 7(4): e014467.
32. Genders TSS, Coles A, Hoffmann U, et al. The external validity of prediction models for the diagnosis of obstructive coronary artery disease in patients with stable chest pain: insights from the PROMISE Trial. JACC Cardiovasc Imaging, 2018, 11(3): 437-446.
33. Teressa G, Bhasin V, Noack P, et al. Comparing the modified history, electrocardiogram, age, risk factors, and troponin score and coronary artery disease consortium model for predicting obstructive coronary artery disease and cardiovascular events in patients with acute chest pain. Crit Pathw Cardiol, 2019, 18(3): 125-129.
34. Damen JA, Hooft L, Schuit E, et al. Prediction models for cardiovascular disease risk in the general population: systematic review. BMJ, 2016, 353: i2416.
35. He T, Liu X, Xu N, et al. Diagnostic models of the pre-test probability of stable coronary artery disease: A systematic review. Clinics (Sao Paulo), 2017, 72(3): 188-196.
36. Task Force Members, Montalescot G, Sechtem U, et al. 2013 ESC guidelines on the management of stable coronary artery disease: the Task Force on the management of stable coronary artery disease of the European Society of Cardiology. Eur Heart J, 2013, 34(38): 2949-3003.
37. Fihn SD, Gardin JM, Abrams J, et al. 2012 ACCF/AHA/ACP/AATS/PCNA/SCAI/STS guideline for the diagnosis and management of patients with stable ischemic heart disease: 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. J Am Coll Cardiol, 2012, 60(24): e44-e164.

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The accuracy of various models in predicting coronary artery disease in the world: A systematic review

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