Machine learning aids clinical decision-making in patients presenting with angina and non-obstructive coronary artery disease. Issue 4 (14th October 2021)

• Record Type: Journal Article
• Authors: Ahmad, Ali; Shelly-Cohen, Michal; Corban, Michel T; Murphree Jr, Dennis H; Toya, Takumi; Sara, Jaskanwal D; Ozcan, Ilke; Lerman, Lilach O; Friedman, Paul A; Attia, Zachi I; Lerman, Amir
• Is Part Of: European heart journal. Volume 2:Issue 4(2021)
• Page Start: 597
• Page End: 605
• Publication Date: 2021-10-14
• Subjects: AI -- Machine learning -- Coronary microvascular dysfunction -- ECG -- Coronary circulation; Medical informatics -- Periodicals; Medical technology -- Periodicals; Cardiovascular system -- Periodicals; 616.100284
• DOI: 10.1093/ehjdh/ztab084
• Languages: English
• ISSNs: 2634-3916

Abstract

Abstract: Aims: 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. Graphical Abstract: