Multimodality Risk Assessment of Patients with Ischemic Heart Disease Using Deep Learning Models Applied to Electrocardiograms and Chest X-rays

• Published in: International Heart Journal Vol. 65; no. 1; pp. 29 - 38
• Main Authors: Sawano, Shinnosuke, Kodera, Satoshi, Sato, Masataka, Shinohara, Hiroki, Kobayashi, Atsushi, Takiguchi, Hiroshi, Hirose, Kazutoshi, Kamon, Tatsuya, Saito, Akihito, Kiriyama, Hiroyuki, Miura, Mizuki, Minatsuki, Shun, Kikuchi, Hironobu, Takeda, Norifumi, Morita, Hiroyuki, Komuro, Issei
• Format: Journal Article
• Language: English
• Published: Japan International Heart Journal Association 01.01.2024; Japan Science and Technology Agency
• Subjects: Cardiovascular disease; Chest; Deep Learning; EKG; Electrocardiography; Heart diseases; Humans; Ischemia; Myocardial Ischemia - diagnosis; Myocardial Ischemia - epidemiology; Neural networks; Percutaneous coronary intervention; Prognosis prediction; Risk Assessment; Risk groups; X-Rays; Percutaneous coronary intervention; Prognosis prediction
• ISSN: 1349-2365; 1349-3299
• DOI: 10.1536/ihj.23-402

Comprehensive management approaches for patients with ischemic heart disease (IHD) are important aids for prognostication and treatment planning. While single-modality deep neural networks (DNNs) have shown promising performance for detecting cardiac abnormalities, the potential benefits of using DNNs for multimodality risk assessment in patients with IHD have not been reported. The purpose of this study was to investigate the effectiveness of multimodality risk assessment in patients with IHD using a DNN that utilizes 12-lead electrocardiograms (ECGs) and chest X-rays (CXRs), with the prediction of major adverse cardiovascular events (MACEs) being of particular concern.DNN models were applied to detection of left ventricular systolic dysfunction (LVSD) on ECGs and identification of cardiomegaly findings on CXRs. A total of 2107 patients who underwent elective percutaneous coronary intervention were categorized into 4 groups according to the models' outputs: Dual-modality high-risk (n = 105), ECG high-risk (n = 181), CXR high-risk (n = 392), and No-risk (n = 1,429).A total of 342 MACEs were observed. The incidence of a MACE was the highest in the Dual-modality high-risk group (P < 0.001). Multivariate Cox hazards analysis for predicting MACE revealed that the Dual-modality high-risk group had a significantly higher risk of MACE than the No-risk group (hazard ratio (HR): 2.370, P < 0.001), the ECG high-risk group (HR: 1.906, P = 0.010), and the CXR high-risk group (HR: 1.624, P = 0.018), after controlling for confounding factors.The results suggest the usefulness of multimodality risk assessment using DNN models applied to 12-lead ECG and CXR data from patients with IHD.