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Published on: August 28, 2018
Performance of CAC-prob in predicting coronary artery calcium score: an external validation study in a high-CAC
Pakpoom Wongyikul1,2, Phichayut Phinyo3,4, Pannipa Suwannasom5
1Department of Biomedical Informatics and Clinical Epidemiology (BioCE), Faculty of Medicine, Chiang Mai University, Chiang Mai, 50200, Thailand.
Insights
This study validates CAC-prob, a tool predicting coronary artery calcium (CAC) scores, in Northern Thailand. The model shows good predictive performance, supporting its use in clinical practice for cardiovascular risk assessment.
Area of Science:
- Cardiology
- Medical Informatics
- Public Health
Background:
- Coronary artery calcium (CAC) screening is increasingly recognized in developing nations like Thailand.
- Official guidelines for CAC score utilization in cardiovascular risk assessment are currently lacking.
- This study addresses the need for robust tools to aid CAC screening decisions.
Purpose of the Study:
- To externally validate CAC-prob, a prediction model for estimating the probability of CAC > 0 and CAC ≥ 100.
- To confirm the robustness and clinical utility of CAC-prob in a Northern Thai population.
Main Methods:
- External validation of the CAC-prob model using retrospective data from 329 patients in a tertiary care center in Northern Thailand (2019-2022).
- CAC-prob comprises two models: Model 1 (CAC > 0) and Model 2 (CAC ≥ 100).
- Model performance was evaluated using discrimination (Ordinal C-index) and calibration (calibration slope).
Main Results:
- The validation cohort exhibited a higher prevalence of cardiovascular risk factors and CAC ≥ 100 compared to the development cohort.
- The Ordinal C-index was 0.78, indicating good discrimination.
- CAC-prob demonstrated comparable performance for Model 1 and slightly improved performance with higher sensitivity for Model 2.
Conclusions:
- The external validation confirms CAC-prob's predictive performance in Northern Thai patients.
- Findings support integrating CAC-prob into routine clinical practice for cardiovascular risk assessment.
- The tool can assist physicians in making informed recommendations for CAC screening.
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.
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