Yang et al. (2222. Yang Y, Chen L, Yam Y, Achenbach S, Al-Mallah M, Berman DS, et al. A clinical model to identify patients with high-risk coronary artery disease. JACC Cardiovasc Imaging. 2015;8(4):427-34, http://dx.doi.org/10.1016/j.jcmg.2014.11.015. http://dx.doi.org/10.1016/j.jcmg.2014.11...
) |
An International Multicenter |
Patients referred to CTA for suspected CAD |
High-risk CAD was defined as left main diameter stenosis>50%, 3-vessel disease with diameter stenosis>70%, or 2-vessel disease involving the proximal left anterior descending artery |
CTA |
24,251 |
7,333 |
9 variables: age, sex, diabetes, hypertension, current smoking, hyperlipidemia, family history of CAD, history of peripheral vascular disease, and chest pain symptoms |
877(3.6%) |
349(4.8%) |
multivariate logistic regression analysis |
Fujimoto et al. (2121. Fujimoto S, Kondo T, Yamamoto H, Yokoyama N, Tarutani Y, Takamura K, 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):563-71, http://dx.doi.org/10.1007/s00380-014-0515-6. http://dx.doi.org/10.1007/s00380-014-051...
) |
Japanese population |
Patients referred to CTA for suspected CAD |
Obstructive CAD was defined as lesions with stenosis of 75% or more |
CTA |
4,137 |
319 |
8 variables: age, gender, hypertension, diabetes mellitus, dyslipidemia, smoking, history of cerebral infarction, and chest symptoms. |
764(18.5%). |
123(34.1%) |
multivariate logistic regression analysis |
Caselli et al. (2020. Caselli C, Rovai D, Lorenzoni V, Carpeggiani C, Teresinska A, Aguade S, 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):709-16, http://dx.doi.org/10.1016/j.cjca.2015.01.035. http://dx.doi.org/10.1016/j.cjca.2015.01...
) |
14 European centers |
Patients with stable chest pain or equivalent symptoms and an intermediate probability of CAD |
Functionally significant CAD was defined as an ICA causing myocardial ischemia at stress imaging or associated with reduced FFR<0.8, or both |
CTA+ at least 1 coronary artery functional imaging test |
527 |
186 |
6 variables:AST, hs-CRP levels, HDL cholesterol, symptom characteristics, age, sex |
80(15.2%) |
75(40%) |
logistic regression analysis |
Chen et al. (1919. Chen ZW, Chen YH, Qian JY, Ma JY, Ge JB. Validation of a novel clinical prediction score for severe coronary artery diseases before elective coronary angiography. PLoS One. 2014;9(4):e94493, http://dx.doi.org/10.1371/journal.pone.0094493. http://dx.doi.org/10.1371/journal.pone.0...
) |
China |
Patients referred to ICA for suspected CAD |
Severe CAD was defined as Gensini scores ≥20, which was approximately equal to onestenosed lesion ≥70% in the proximal left anterior descending artery |
ICA |
347 |
204 |
8 variables: age, sex, AVC, abnormal ECG, diabetes, hyperlipidemia, HDL, LDL |
202(58.2%) |
NR |
logistic regression analysis |
Genders et al. (1818. Genders TS, Steyerberg EW, Hunink MG, 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, http://dx.doi.org/10.1136/bmj.e3485. http://dx.doi.org/10.1136/bmj.e3485...
) |
18 hospitals from Europe and the US |
Patients were referred for catheter-based or CT-based coronary angiography |
Obstructive CAD was defined as at least one vessel with at least 50% diameter stenosis on ICA |
CTA+ICA |
5,677 |
- |
The basic model included 4 variables: age, sex, symptoms, and setting. Theclinical model included 8 variables: age, sex, symptoms, setting, diabetes, hypertension, dyslipidemia, and smoking, The extended model included 9 variables: age, sex, symptoms, setting, diabetes, hypertension, dyslipidemia, smoking, and coronary calcium score. |
- |
- |
logistic regression analysis |
Genders et al. (1717. Genders TS, 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(11):1316-30, http://dx.doi.org/10.1093/eurheartj/ehr014. http://dx.doi.org/10.1093/eurheartj/ehr0...
) |
14 hospitals from Europe and US |
Patients presented with stable chest pain and an ICA was performed |
Obstructive CAD was defined as ≥50% stenosis in one or more vessels on ICA |
ICA |
2,260 |
- |
3 risk factors: sex, age, and symptoms |
1,319(58.4%) |
- |
logistic regression analysis |
Rosenberg et al. (1616. Rosenberg S, Elashoff MR, Beineke P, Daniels SE, Wingrove JA, Tingley WG, 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):425-34, http://dx.doi.org/10.7326/0003-4819-153-7-201010050-00005. http://dx.doi.org/10.7326/0003-4819-153-...
) |
39 center prospective study |
Non-diabetic patients who were referred for diagnostic ICA or had a high risk of CAD |
Obstructive CAD was defined as ≥50% stenosis in ≥1 major coronary artery by quantitative coronary angiography |
ICA |
640 |
526 |
23 genes grouped in the 6 terms, 4 sex-independent and 2sex-specific |
230(35.9%) |
192(36.5%) |
logistic regression analysis |
Morise et al. (1515. 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):350-6, http://dx.doi.org/10.1016/S0002-9343(97)00086-7. http://dx.doi.org/10.1016/S0002-9343(97)...
) |
US |
Inpatients or outpatients were referred for exercise testing because of symptoms that raised the suspicion of coronary disease |
Significant coronary artery disease was defined as the presence of ≥1 vessel with ≥50% luminal diameter narrowing |
ICA |
915 |
348 |
10 risk factors: sex, age, symptoms (chest pain), estrogen status, diabetes, hypertension, smoking, hyperlipidemia, family history, and obesity. |
373 (41%) |
157 (45%) |
multivariate logistic regression analysis |
Pryor et al. (1414. Pryor DB, Harrell FE Jr, Lee KL, Califf RM, Rosati RA. Estimating the likelihood of significant coronary artery disease. Am J Med. 1983;75(5):771-80, http://dx.doi.org/10.1016/0002-9343(83)90406-0. http://dx.doi.org/10.1016/0002-9343(83)9...
) |
US |
Patients with chest pain were referred for ICA |
Significant coronary artery disease was defined as >75% luminal diameter narrowing of at least one major coronary artery |
ICA |
3,627 |
1,811 |
8 risk factors: sex, age, symptoms(chest pain), diabetes, smoking, hyperlipidemia, history of MI, and electrocardiogram (Q waves, ST-T wave changes) |
2,379(65.6%) |
1,266(69.9%) |
multivariate logistic regression analysis |
Diamond and Forrester(1313. 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):1350-8, http://dx.doi.org/10.1056/NEJM197906143002402. http://dx.doi.org/10.1056/NEJM1979061430...
) |
US |
Pooled analysis from 18 other studies |
Obstructive CAD was defined as ≥50% stenosis in one or more vessels on CCA |
coronary angiography |
- |
- |
3 risk factors: sex, age, and symptoms |
- |
- |
Bayesian-based algorithms |