Use of artificial intelligence in the multimodal evaluation of ischemia in patients with hypertrophic cardiomyopathy.
Authors
Affiliations (2)
Affiliations (2)
- Division of Cardiology, Department of Medicine, Weill Cornell Medical College, New York Presbyterian Hospital, 1305 York Avenue, 8th Floor , New York, NY, 10021, USA. [email protected].
- Division of Cardiology, Department of Medicine, Weill Cornell Medical College, New York Presbyterian Hospital, 1305 York Avenue, 8th Floor , New York, NY, 10021, USA.
Abstract
Hypertrophic cardiomyopathy (HCM) presents diagnostic challenges for the diagnosis of ischemia due to microvascular disease and abnormal myocardium. Traditional methods such as angiography and instantaneous wave-free ratio (iFR) may not fully capture ischemic risk in this population. This case highlights the use of Artificial Intelligence-guided quantitative coronary computed tomography angiography (AI-QCT) as a complementary tool to detect ischemia in HCM patients. A 72-year-old woman with hypertension, hyperlipidemia, apical HCM, and non-obstructive coronary artery disease presented with atypical chest pain. ECG showed worsening anterolateral repolarization abnormalities, raising concerns about progressive coronary artery disease. Coronary CTA revealed severe stenosis of the proximal left anterior descending (LAD) artery with high-risk plaque features, and AI-QCT indicated likely ischemia. Invasive angiography however showed only 40% stenosis of the LAD, and physiologic assessment with iFR suggested non-obstructive disease. Cardiac MRI revealed a 2% subendocardial scar in the hypertrophied apex, suggesting in fact prior ischemic burden. The patient's chest pain was attributed to a combination of non-obstructive HCM with microvascular disease and epicardial coronary disease. Medical therapy was intensified, including the addition of clopidogrel. HCM is associated with reduced coronary flow reserve, microvascular disease, and structural abnormalities, complicating ischemia detection. The diagnostic yield of iFR may be reduced due to abnormal microcirculation in these patients. AI-QCT provides additional ischemic insights, especially in cases of non-obstructive epicardial disease. In this case, AI-QCT identified ischemia that was missed by invasive testing, correlating with myocardial scar on MRI. This suggests AI-QCT could offer a valuable non-invasive approach in assessing ischemia in HCM patients.