Objective:

To evaluate the performance of an explainable machine-learning model in predicting angiographic coronary artery disease (CAD) among patients referred for coronary angiography.

Approach:

Key Findings:

• The final model achieved a balanced accuracy of 77% and an area under the curve of 0.815.
• Hypertension, hyperlipidemia, sex, diabetes, and triglyceride levels were the most influential predictors.
• Inflammatory biomarkers had unclear roles, with some showing inverse relationships with CAD.

Interpretation:

The model was selected for interpretability and balanced performance.

Limitations:

• The model has not been validated in an independent external cohort.
• It was developed in patients already undergoing coronary angiography, limiting its applicability to lower-prevalence populations.
• The study's retrospective design and limited inflammatory biomarker panel may affect results.

Conclusion:

Prospective multicenter validation and additional multimodal data are needed to determine the model's clinical utility.

Sources:

• Open Heart
Source

Based on findings from:

Coronary Model Shows Limits in Cath-Referred Patients

Andrea Surnit. MDSpire News, 2026.

https://news.mdspire.com/cardiology/articles/coronary-model-shows-limits-in-cathreferred-patients/

Attribution Notice

This content is an AI-generated, fully rewritten summary based on a published scholarly article. It does not reproduce the original text and is not a substitute for the original publication. Readers are encouraged to consult the source for full context, data, and methodology.