A machine learning-based clinical decision support algorithm for reducing unnecessary coronary angiograms.

Background: Conventional clinical risk scores and diagnostic algorithms are proving to be suboptimal in the prediction of obstructive coronary artery disease, contributing to the low diagnostic yield of invasive angiography. Machine learning could help better predict which patients would benefit from invasive angiography vs other noninvasive diagnostic modalities.

Objective: To reduce patient risk and cost to the healthcare system by improving the diagnostic yield of invasive coronary angiography through optimized outpatient selection.

Methods: Retrospective analysis of 12 years of referral data from a provincial cardiac registry, including all patients referred for invasive angiography of more than 1.4 million individuals in Ontario, Canada. Stable outpatients undergoing coronary angiography during the study period were included in the analysis. The training set (80% random sample, n = 23,750) was used to develop 8 prediction models in Python using grid-search cross-validation. The test set (20% random sample, n = 5938), evaluated the discrimination performance of each model.

Results: The machine-learning model achieved a substantially better performance (area under the receiver operating characteristics curve: 0.81) than existing models for predicting obstructive coronary artery disease in patients referred for invasive angiography. It significantly outperformed both the reference model and current clinical practice with a net reclassification index of 27.8% (95% confidence interval [CI]: [24.9%-30.8%], value <.01) and 44.7% (95% CI: [42.4%-47.0%], value <.01), respectively.

Conclusion: This prediction model, when coupled with a point-of-care, online decision support tool to be used by referring physicians, could improve the diagnostic yield of invasive coronary angiography in stable, elective outpatients, thus improving patient safety and reducing healthcare costs.