Point of care screening for diverse heart failure phenotypes in Japan using handheld single lead artificial intelligence electrocardiography

The applicability of existing Convolutional Neural Network (CNN) models for diagnosing heart failure in Japanese patients remains unclear. We aimed to evaluate the accuracy of a CNN-based single-lead electrocardiographic device for point-of-care heart failure screening in primary care settings. We included patients aged ≥ 20 years with a chief complaint of dyspnea or lower leg edema. A single-lead electrocardiogram was recorded, following which a CNN-based algorithm derived a quantitative index of heart failure severity (HF index). The primary endpoint was the diagnostic accuracy of the HF index. Secondary endpoints included identifying factors affecting the diagnostic performance and accuracy of the HF index in classifying heart failure phenotypes and disease severity. We enrolled 112 patients, including 50 patients with heart failure. The HF index had excellent diagnostic performance, with an area under the receiver operating characteristic curve of 0.905. Its sensitivity and specificity for heart failure diagnosis were 0.800 and 0.871, respectively. No significant interactions were observed with the HF index for several evaluated factors. The HF index exhibited strong predictive ability for heart failure phenotypes and disease severity. In older Japanese patients, the single-lead electrocardiographic device enabled simple and accessible screening for different heart failure phenotypes in primary care setting. Trial registration: This manuscript reports the results of a clinical study registered with UMIN-CTR (UMIN000057871) on May 15, 2025.