ArticleYonsei medical journal2025
Comparative Evaluation of Pre-Test Probability Models for Coronary Artery Disease with Assessment of a New Machine Learning-Based Model.
Article in Yonsei medical journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
purposeThis study aimed to validate pivotal pre-test probability (PTP)-coronary artery disease (CAD) models (CAD consortium model and IJC-CAD model). MATERIALS AND
methodsTraditional PTP models-CAD consortium models: two traditional PTP models were used under the CAD consortium framework, namely CAD1 and CAD2. Machine learning (ML)-based PTP models: two ML-based PTP models were derived from CAD1 and CAD2, and used to enhance predictive capabilities [ML-CAD2 and ML-IJC (IJC-CAD)]. The primary endpoint was obstructive CAD. The performance evaluation of these PTP models was conducted using receiver-operating characteristic analysis.
resultsThe study included 238 participants, among whom 157 individuals (65.9% of the total sample) had CAD. The IJC-CAD model demonstrated the highest performance with an area under the curve (AUC) of 0.860 [95% confidence interval (CI): 0.812-0.909]. Following this, the ML-CAD2 model exhibited an AUC of 0.814 (95% CI: 0.758-0.870), CAD1 showed an AUC of 0.767 (95% CI: 0.705-0.830), and CAD2 had an AUC of 0.785 (95% CI: 0.726-0.845). Each of the PTP models was adjusted to have a CAD score cutoff that classified cases with a sensitivity of over 95%. The respective cutoff values were as follows: CAD1 and CAD2 >12, ML-CAD2 >0.380, and IJC-CAD >0.367. All PTP models achieved a CAD sensitivity of over 95%. Similar to the AUC performance, the accuracy of the PTP models was highest for IJC-CAD, reaching 80.3%. The accuracy of ML-CAD2 was 77.7%, while that for CAD1 and CAD2 was 74.8% and 75.2%, respectively.
conclusionML-CAD2 and IJC-CAD showed superior performance compared to traditional existing models (CAD1 and CAD2).
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