Machine learning aids clinical decision making in patients presenting with angina and non-obstructive coronary artery disease
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 1,893 NOCAD patients (<50% angiographic stenosis) who underwent CMD evaluation as well as an 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 = 1,257). AUC values ranged from 0.49–0.67 for all the outcomes. The best performance in our analysis was for the outcome CFR ≤ 2.5 with clinical variables. AUC and accuracy were 0.67 and 60%. When decreasing the threshold of positivity, sensitivity and NPV increased to 92% and 90% respectively, while specificity and PPV decreased to 25% and 29% respectively. Conclusion An AI-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.