Open Access

Machine learning aids clinical decision-making in patients presenting with angina and non-obstructive coronary artery disease

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Date: 14 October 2021
Journal: European Heart Journal - Digital Health , Volume 2 , Issue 4 , Pages 597 - 605
Authors: J. Sara , A. Ahmad , M. Shelly-Cohen , I. Ozcan , M. Corban , L. Lerman , D. Murphree Jr , P. Friedman , T. Toya , Z. Attia , A. Lerman

ESC Journals

AbstractAims

The current gold standard comprehensive assessment of coronary microvascular dysfunction (CMD) is through a limited-access invasive catheterization lab procedure. We aimed to develop a point-of-care tool to assist clinical guidance in patients presenting with chest pain and/or an abnormal cardiac functional stress test and with non-obstructive coronary artery disease (NOCAD).

Methods and results

This study included 1893 NOCAD patients (<50% angiographic stenosis) who underwent CMD evaluation as well as an electrocardiogram (ECG) up to 1-year prior. Endothelial-independent CMD was defined by coronary flow reserve (CFR) ≤2.5 in response to intracoronary adenosine. Endothelial-dependent CMD was defined by a maximal percent increase in coronary blood flow (%ΔCBF) ≤50% in response to intracoronary acetylcholine infusion. We trained algorithms to distinguish between the following outcomes: CFR ≤2.5, %ΔCBF ≤50, and the combination of both. Two classes of algorithms were trained, one depending on ECG waveforms as input, and another using tabular clinical data. Mean age was 51 ± 12 years and 66% were females (n = 1257). Area under the curve values ranged from 0.49 to 0.67 for all the outcomes. The best performance in our analysis was for the outcome CFR ≤2.5 with clinical variables. Area under the curve and accuracy were 0.67% and 60%. When decreasing the threshold of positivity, sensitivity and negative predictive value increased to 92% and 90%, respectively, while specificity and positive predictive value decreased to 25% and 29%, respectively.

Conclusion

An artificial intelligence-enabled algorithm may be able to assist clinical guidance by ruling out CMD in patients presenting with chest pain and/or an abnormal functional stress test. This algorithm needs to be prospectively validated in different cohorts.

About the contributors

Jaskanwal D Sara

Role: Author

Ali Ahmad

Role: Author

Michal Shelly-Cohen

Role: Author