Key result
Simple CAC likelihood tables outperform the Diamond-Forrester model with an AUC of ~0.88 for obstructive CAD.
Why the study?
The Diamond-Forrester model overestimates the probability of obstructive CAD in current populations, and CAC is not routinely integrated with other clinical features.
Does a simplified likelihood table integrating coronary artery calcium with age, sex, and chest pain improve the prediction of obstructive CAD compared to standard risk models?
Cohort (n=5,376)
Yes
Does a simplified likelihood table integrating coronary artery calcium with age, sex, and chest pain improve the prediction of obstructive CAD compared to standard risk models?
Effect estimate: AUC 0.875 (95% CI 0.862-0.889)
Absolute Event Rate: 0.875% vs 0.679%
p-value: p=<0.001
Integrating coronary artery calcium with age, sex, and chest pain into a simplified likelihood table provides highly accurate predictions for obstructive CAD, outperforming older legacy models without requiring complex calculations.
May refine obstructive CAD pretest probability estimates; leaves open need for prospective outcome trials before clinical adoption.
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]; P =0.030) and the updated Diamond‐Forrester model (AUC, 0.679 [95% CI, 0.658–0.699]; P <0.001). The calibration for the likelihood tables was better than the CAD Consortium model (Brier score, 0.116 versus 0.121; P =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.
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Miller et al. (2023) conducted a cohort in Suspected coronary artery disease without known CAD (n=5,376). Likelihood tables integrating coronary artery calcium (CAC), age, sex, and cardiac chest pain vs. Updated Diamond-Forrester model and CAD Consortium clinical+CAC model was evaluated on Prediction performance for obstructive CAD (any stenosis ≥50% on CCTA) as measured by Area Under the Curve (AUC) in the external testing cohort (AUC 0.875, 95% CI 0.862-0.889, p=<0.001). Simple likelihood tables integrating CAC with age, sex, and cardiac chest pain predicted obstructive CAD with an AUC of 0.875, significantly outperforming the updated Diamond-Forrester model (AUC 0.679, p<0.001).
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