Performance of CAC-prob in predicting coronary artery calcium score: an external validation study in a high-CAC burden population.

Background: Although CAC screening is gaining recognition in developing countries such as Thailand, official guidelines for using the CAC score in cardiovascular risk assessment remain lacking. This study aims to externally validate CAC-prob, a recently developed prediction model that can estimate the probability of CAC > 0 and CAC ≥ 100, to confirm its robustness.

Method: This study externally validated the CAC-prob model using retrospective data from a tertiary care centre in northern Thailand. Patients who underwent CAC screening between 2019 and 2022 were included. CAC-prob consists of two models: one predicting the probability of CAC > 0 (Model 1) and another predicting the probability of CAC ≥ 100 (Model 2). Model performance was assessed in terms of discrimination (Ordinal C-index), calibration slope, and diagnostic indices for each model.

Results: A total of 329 patients were included. The patient characteristics observed in this study indicated a higher prevalence of DM, hypertension, dyslipidaemia, CKD, and CAC ≥ 100 compared to the development study. The ordinal C-index derived from the validation study showed a slight decline (0.78). The calibration slope for Model 1 and Model 2 was 1.28 (95% CI 0.95-1.63) and 1.06 (95% CI 0.78-1.36), respectively. In Model 1, CAC-prob demonstrated comparable diagnostic performance. However, in Model 2, it showed slightly better performance, with significantly improved sensitivity compared to the development study.

Conclusion: This external validation study confirms the predictive performance of CAC-prob in Northern Thai patients. The findings support the integration of CAC-prob into routine clinical practice to aid physicians in making recommendations for CAC screening.