Key result
PREDICT-OCAD nomogram outperforms CACS alone in predicting obstructive CAD.
Why the study?
Conventional cardiovascular risk models perform relatively poorly in predicting obstructive CAD, prompting the development and validation of a CACS-based nomogram (PREDICT-OCAD) to predict obstructive CAD.
Does the PREDICT-OCAD nomogram improve the prediction of obstructive coronary artery disease compared to conventional cardiovascular risk factors or CACS alone in patients undergoing CCTA?
Observational (n=4,994)
Yes
Does the PREDICT-OCAD nomogram improve the prediction of obstructive coronary artery disease compared to conventional cardiovascular risk factors or CACS alone in patients undergoing CCTA?
Effect estimate: AUC 0.893 (95% CI 0.869-0.917)
Absolute Event Rate: 0.893% vs 0.849%
p-value: p=0.027
A simple-to-use nomogram combining CACS and clinical risk factors significantly improves the prediction of obstructive CAD compared to traditional risk models, potentially reducing unnecessary CCTA testing.
May refine obstructive CAD prediction in CCTA candidates; leaves open prospective validation before clinical adoption.
Screening for obstructive coronary artery disease (CAD) is crucial. Conventional cardiovascular risk models have relatively poor performance in predicting obstructive coronary artery disease. CAC score (CACS) is the strongest predictor of obstructive CAD. This study developed and validated the performance of obstructive CAD prediction model (PREDICT-OCAD) and further studied that PREDICT-OCAD can save unnecessary expense by correctly reclassifying patients as low likelihood for obstructive CAD without further testing. To develop and validate the performance of coronary artery calcium score (CACS) based nomogram for predicting obstructive coronary artery disease (CAD). 4994 participants undergoing CACS and coronary CT angiography (CCTA) from four medical centers were retrospectively included for model development (n = 2649), internal (n = 1135) and external validation (n = 1210). Coronary artery stenosis on CCTA and CACS were measured. Regression models were developed and compared. A nomogram was generated to identify potential predictors associated with obstructive CAD. In development sets, CACS had incremental value for prediction of obstructive CAD compared to cardiovascular risk factors-based model, with higher area under the curves (AUCs) (0.848 [95%CI: 0.830–0.866] vs. 0.879 [95%CI: 0.857-0.900]; P = 0.029), net reclassification improvement (NRI) index (0.577 [95%CI: 0.482–0.672]; P < 0.001) and integrated discrimination improvement (IDI) index (0.186 [95%CI: 0.159–0.213]; P < 0.001). A nomogram (PREDICT-OCAD) provided significantly incremental discriminate value to CACS (AUC: 0.893 [95%CI: 0.869–0.917]; P = 0.027), which was confirmed by continuous NRI (0.392 [95%CI: 0.247–0.538]; P < 0.001), categorical NRI (0.069 [95%CI: 0.001–0.137]; P = 0.045) and IDI (0.039 [95%CI: 0.02–0.0580]; P < 0.001), resulted in the revision of management plan as determined by cardiovascular risk factors in 18.6% (492/2649) of patients, of whom 77.2% (380/492) were correctly reclassified. Our developed simple-to-use CACS based nomogram model (PREDICT-OCAD) performs better than conventional clinical model or CACS alone, which is a useful tool for risk stratification of CAD in primary health system.
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Zuo et al. (2025) conducted an observational in Obstructive coronary artery disease (n=4,994). PREDICT-OCAD nomogram vs. CACS alone was evaluated on Prediction of obstructive CAD (AUC) (AUC 0.893, 95% CI 0.869-0.917, p=0.027). The PREDICT-OCAD nomogram demonstrated significantly better discrimination for predicting obstructive coronary artery disease compared to CACS alone (AUC 0.893 vs 0.849).
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