Simplified Approach to Predicting Obstructive Coronary Disease With Integration of Coronary Calcium: Development and External Validation

Overview

Journal J Am Heart Assoc

Specialty Cardiology & Vascular Diseases

Date 2023 Dec 18

PMID 38108259

Authors

Robert J H Miller

Heidi Gransar

Alan Rozanski

Damini Dey

Mouaz Al-Mallah

Benjamin J W Chow

Philipp A Kaufmann

Filippo Cademartiri

Erica Maffei

DongHee Han

Piotr J Slomka

Daniel S Berman

Affiliations

Soon will be listed here.

Abstract

Background: The Diamond-Forrester model was used extensively to predict obstructive coronary artery disease (CAD) but overestimates probability in current populations. Coronary artery calcium (CAC) is a useful marker of CAD, which is not routinely integrated with other features. We derived simple likelihood tables, integrating CAC with age, sex, and cardiac chest pain to predict obstructive CAD.

Methods And Results: The training population included patients from 3 multinational sites (n=2055), with 2 sites for external testing (n=3321). We determined associations between age, sex, cardiac chest pain, and CAC with the presence of obstructive CAD, defined as any stenosis ≥50% on coronary computed tomography angiography. Prediction performance was assessed using area under the receiver-operating characteristic curves (AUCs) and compared with the CAD Consortium models with and without CAC, which require detailed calculations, and the updated Diamond-Forrester model. In external testing, the proposed likelihood tables had higher AUC (0.875 [95% CI, 0.862-0.889]) than the CAD Consortium clinical+CAC score (AUC, 0.868 [95% CI, 0.855-0.881]; =0.030) and the updated Diamond-Forrester model (AUC, 0.679 [95% CI, 0.658-0.699]; <0.001). The calibration for the likelihood tables was better than the CAD Consortium model (Brier score, 0.116 versus 0.121; =0.005).

Conclusions: We have developed and externally validated simple likelihood tables to integrate CAC with age, sex, and cardiac chest pain, demonstrating improved prediction performance compared with other risk models. Our tool affords physicians with the opportunity to rapidly and easily integrate a small number of important features to estimate a patient's likelihood of obstructive CAD as an aid to clinical management.

References

van Velzen S, Lessmann N, Velthuis B, Bank I, van den Bongard D, Leiner T . Deep Learning for Automatic Calcium Scoring in CT: Validation Using Multiple Cardiac CT and Chest CT Protocols. Radiology. 2020; 295(1):66-79. PMC: 7106943. DOI: 10.1148/radiol.2020191621. View

AlAref S, Maliakal G, Singh G, van Rosendael A, Ma X, Xu Z . Machine learning of clinical variables and coronary artery calcium scoring for the prediction of obstructive coronary artery disease on coronary computed tomography angiography: analysis from the CONFIRM registry. Eur Heart J. 2019; 41(3):359-367. PMC: 7849944. DOI: 10.1093/eurheartj/ehz565. View

Rios R, Miller R, Hu L, Otaki Y, Singh A, Diniz M . Determining a minimum set of variables for machine learning cardiovascular event prediction: results from REFINE SPECT registry. Cardiovasc Res. 2021; 118(9):2152-2164. PMC: 9302886. DOI: 10.1093/cvr/cvab236. View

Rozanski A, Gransar H, Shaw L, Kim J, Miranda-Peats L, Wong N . Impact of coronary artery calcium scanning on coronary risk factors and downstream testing the EISNER (Early Identification of Subclinical Atherosclerosis by Noninvasive Imaging Research) prospective randomized trial. J Am Coll Cardiol. 2011; 57(15):1622-32. PMC: 3104928. DOI: 10.1016/j.jacc.2011.01.019. View

den Dekker M, De Smet K, de Bock G, Tio R, Oudkerk M, Vliegenthart R . Diagnostic performance of coronary CT angiography for stenosis detection according to calcium score: systematic review and meta-analysis. Eur Radiol. 2012; 22(12):2688-98. DOI: 10.1007/s00330-012-2551-x. View

Fihn S, Blankenship J, Alexander K, Bittl J, Byrne J, Fletcher B . 2014 ACC/AHA/AATS/PCNA/SCAI/STS focused update of the guideline for the diagnosis and management of patients with stable ischemic heart disease: a report of the American College of Cardiology/American Heart Association Task Force on Practice.... Circulation. 2014; 130(19):1749-67. DOI: 10.1161/CIR.0000000000000095. View

Miller R, Eisenberg E, Friedman J, Cheng V, Hayes S, Tamarappoo B . Impact of heart rate on coronary computed tomographic angiography interpretability with a third-generation dual-source scanner. Int J Cardiol. 2019; 295:42-47. DOI: 10.1016/j.ijcard.2019.07.098. View

Van Calster B, McLernon D, van Smeden M, Wynants L, Steyerberg E . Calibration: the Achilles heel of predictive analytics. BMC Med. 2019; 17(1):230. PMC: 6912996. DOI: 10.1186/s12916-019-1466-7. View

Rozanski A, Gransar H, Hayes S, Min J, Friedman J, Thomson L . Temporal trends in the frequency of inducible myocardial ischemia during cardiac stress testing: 1991 to 2009. J Am Coll Cardiol. 2013; 61(10):1054-65. DOI: 10.1016/j.jacc.2012.11.056. View

10.

Rios R, Miller R, Manral N, Sharir T, Einstein A, Fish M . Handling missing values in machine learning to predict patient-specific risk of adverse cardiac events: Insights from REFINE SPECT registry. Comput Biol Med. 2022; 145:105449. PMC: 9117456. DOI: 10.1016/j.compbiomed.2022.105449. View

11.

Won K, Chang H, Niinuma H, Sung J, Shin S, Cho I . Evaluation of the predictive value of coronary artery calcium score for obstructive coronary artery disease in asymptomatic Korean patients with type 2 diabetes mellitus. Coron Artery Dis. 2014; 26(2):150-6. PMC: 4323554. DOI: 10.1097/MCA.0000000000000184. View

12.

Genders T, Steyerberg E, Alkadhi H, Leschka S, Desbiolles L, Nieman K . 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. View

13.

Austin P . A Tutorial on Multilevel Survival Analysis: Methods, Models and Applications. Int Stat Rev. 2018; 85(2):185-203. PMC: 5756088. DOI: 10.1111/insr.12214. View

14.

Einstein A, Johnson L, Bokhari S, Son J, Thompson R, Bateman T . Agreement of visual estimation of coronary artery calcium from low-dose CT attenuation correction scans in hybrid PET/CT and SPECT/CT with standard Agatston score. J Am Coll Cardiol. 2010; 56(23):1914-21. PMC: 3040452. DOI: 10.1016/j.jacc.2010.05.057. View

15.

Baskaran L, Danad I, Gransar H, Hartaigh B, Schulman-Marcus J, Lin F . A Comparison of the Updated Diamond-Forrester, 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. 2018; 12(7 Pt 2):1392-1400. DOI: 10.1016/j.jcmg.2018.02.020. View

16.

Yeh R, Sidney S, Chandra M, Sorel M, Selby J, Go A . Population trends in the incidence and outcomes of acute myocardial infarction. N Engl J Med. 2010; 362(23):2155-65. DOI: 10.1056/NEJMoa0908610. View

17.

Agatston A, Janowitz W, HILDNER F, Zusmer N, VIAMONTE Jr M, Detrano R . Quantification of coronary artery calcium using ultrafast computed tomography. J Am Coll Cardiol. 1990; 15(4):827-32. DOI: 10.1016/0735-1097(90)90282-t. View

18.

Poon M, Carr J, Gerstad N, Gillam L, Hodgson J, Kim R . ACCF/ACR/SCCT/SCMR/ASNC/NASCI/SCAI/SIR 2006 appropriateness criteria for cardiac computed tomography and cardiac magnetic resonance imaging: a report of the American College of Cardiology Foundation Quality Strategic Directions Committee.... J Am Coll Cardiol. 2006; 48(7):1475-97. DOI: 10.1016/j.jacc.2006.07.003. View

19.

Peng A, Dudum R, Jain S, Maron D, Patel B, Khandwala N . Association of Coronary Artery Calcium Detected by Routine Ungated CT Imaging With Cardiovascular Outcomes. J Am Coll Cardiol. 2023; 82(12):1192-1202. PMC: 11009374. DOI: 10.1016/j.jacc.2023.06.040. View

20.

Juarez-Orozco L, Saraste A, Capodanno D, Prescott E, Ballo H, Bax J . 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):1198-1207. DOI: 10.1093/ehjci/jez054. View