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Identifying Coronary Artery Calcification on Non-gated Computed Tomography Scans
Published on: August 28, 2018
Clinical likelihood models calibrated against observed obstructive coronary artery disease on computed tomography
Laust D Rasmussen1,2, Samuel Emil Schmidt3, Juhani Knuuti4
1Department of Cardiology, Gødstrup Hospital, Hospitalsparken 15, DK-7400 Herning, Denmark.
Insights
New clinical likelihood models, calibrated for coronary computed tomography angiography (CCTA), accurately predict obstructive coronary artery disease (CAD). These models, including risk-factor and coronary artery calcium score versions, offer improved diagnostic performance over existing methods for chest pain patients.
Area of Science:
- Cardiology
- Medical Imaging
- Machine Learning in Healthcare
Background:
- Existing models for obstructive coronary artery disease (CAD) likelihood are primarily based on invasive coronary angiography.
- Patients with stable, new-onset chest pain often have lower clinical likelihood and undergo non-invasive testing like coronary computed tomography angiography (CCTA).
- There is a need for clinical likelihood models specifically calibrated for obstructive CAD detected by CCTA.
Purpose of the Study:
- To develop and validate clinical likelihood models for obstructive CAD, calibrated against CCTA findings.
- To create risk-factor-based (RF-CLCCTA) and coronary artery calcium score-based (CACS-CLCCTA) models.
- To compare the performance of these new models against a basic pre-test probability (Basic PTP) model.
Main Methods:
- An advanced machine learning algorithm was used to develop RF-CLCCTA and CACS-CLCCTA models.
- The models were trained on a cohort of 38,269 symptomatic outpatients with suspected obstructive CAD.
- Validation was performed on separate cohorts totaling 28,340 patients; obstructive CAD was defined as >50% diameter stenosis on CCTA.
Main Results:
- The RF-CLCCTA and CACS-CLCCTA models demonstrated superior calibration compared to the Basic PTP model, which underestimated obstructive CAD prevalence.
- Both new models showed significantly improved discrimination compared to the Basic PTP model.
- Area under the receiver operating curves (AUC) for RF-CLCCTA was 0.74 (95% CI 0.73-0.75) and for CACS-CLCCTA was 0.87 (95% CI 0.86-0.87), versus 0.71 (95% CI 0.70-0.72) for Basic PTP.
Conclusions:
- Clinical likelihood models calibrated for CCTA significantly enhance the calibration and discrimination of obstructive CAD detection.
- The developed RF-CLCCTA and CACS-CLCCTA models provide more accurate pre-test probability assessments for patients undergoing CCTA.
- These improved models can aid in more precise diagnosis and management of suspected coronary artery disease.
Aims:
Models predicting the likelihood of obstructive coronary artery disease (CAD) on invasive coronary angiography exist. However, as stable patients with new-onset chest pain frequently have lower clinical likelihood and preferably undergo index testing by non-invasive tests such as coronary computed tomography angiography (CCTA), clinical likelihood models calibrated against observed obstructive CAD at CCTA are warranted. The aim was to develop CCTA-calibrated risk-factor- and coronary artery calcium score-weighted clinical likelihood models (i.e. RF-CLCCTA and CACS-CLCCTA models, respectively).
Methods And Results:
Based on age, sex, symptoms, and cardiovascular risk factors, an advanced machine learning algorithm utilized a training cohort (n = 38 269) of symptomatic outpatients with suspected obstructive CAD to develop both a RF-CLCCTA model and a CACS-CLCCTA model to predict observed obstructive CAD on CCTA. The models were validated in several cohorts (n = 28 340) and compared with a currently endorsed basic pre-test probability (Basic PTP) model. For both the training and pooled validation cohorts, observed obstructive CAD at CCTA was defined as >50% diameter stenosis. Observed obstructive CAD at CCTA was present in 6443 (22.7%) patients in the pooled validation cohort. While the Basic PTP underestimated the prevalence of observed obstructive CAD at CCTA, the RF-CLCCTA and CACS-CLCCTA models showed superior calibration. Compared with the Basic PTP model, the RF-CLCCTA and CACS-CLCCTA models showed superior discrimination (area under the receiver operating curves 0.71 [95% confidence interval (CI) 0.70-0.72] vs. 0.74 (95% CI 0.73-0.75) and 0.87 (95% CI 0.86-0.87), P < 0.001 for both comparisons).
Conclusion:
CCTA-calibrated clinical likelihood models improve calibration and discrimination of observed obstructive CAD at CCTA.
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