Key result
RF-CLCCTA and CACS-CLCCTA models improve obstructive CAD discrimination compared to basic PTP.
Why the study?
Existing models predict obstructive CAD on invasive angiography, but stable patients with new-onset chest pain typically undergo non-invasive CCTA, warranting CCTA-calibrated clinical likelihood models.
Do CCTA-calibrated clinical likelihood models improve the prediction of obstructive CAD compared to the basic pre-test probability model in symptomatic outpatients?
Cohort (n=66,609)
Yes
Do CCTA-calibrated clinical likelihood models improve the prediction of obstructive CAD compared to the basic pre-test probability model in symptomatic outpatients?
Effect estimate: AUC 0.74 (RF-CLCCTA) and 0.87 (CACS-CLCCTA) vs 0.71 (Basic PTP) (95% CI 0.73-0.75 and 0.86-0.87 vs 0.70-0.72)
p-value: p=<0.001
CCTA-calibrated clinical likelihood models incorporating risk factors and calcium scores significantly improve the discrimination and calibration for predicting obstructive CAD compared to basic pre-test probability models.
May refine CAD likelihood estimates on CCTA; extends Basic PTP but hypothesis-generating pending prospective validation.
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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Rasmussen et al. (2025) conducted a cohort in Suspected obstructive coronary artery disease (n=66,609). RF-CLCCTA and CACS-CLCCTA models vs. Basic pre-test probability (Basic PTP) model was evaluated on Discrimination of observed obstructive CAD at CCTA (>50% diameter stenosis) (AUC 0.74 (RF-CLCCTA) and 0.87 (CACS-CLCCTA) vs 0.71 (Basic PTP), 95% CI 0.73-0.75 and 0.86-0.87 vs 0.70-0.72, p=<0.001). The RF-CLCCTA and CACS-CLCCTA models showed superior discrimination for observed obstructive CAD compared with the Basic PTP model (AUC 0.74 and 0.87 vs 0.71; P<0.001).
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