Detection of Severe Structural Heart Disease Using an AI-Enhanced Portable 1-Lead ECG: The ACCESS-SHD Study
Year: 2026
Type: preprint
Study design: Observational Study
Abstract: Aims: Structural heart disease (SHD) often remains undetected until symptoms develop. Portable 1-lead ECG devices return an automated rhythm-based interpretation, but whether AI-ECG adds diagnostic value beyond this interpretation is unknown. We prospectively evaluated a noise-adapted 1-lead AI-ECG algorithm for detecting severe SHD from portable KardiaMobile 6L recordings, and its relative diagnostic value beyond the device's rhythm interpretation. Methods and Results: Adults undergoing outpatient echocardiography between June 2024 and January 2025 recorded a 30-second, 1-lead ECG with real-time AI-ECG inference. The primary endpoint was discrimination for echocardiography-defined severe SHD. Secondary analyses assessed net reclassification improvement (NRI) versus the native interpretation and the number needed to test (NNT). Among 597 participants (median age 61.7 years; 51.4% women), 30 (5.1%) had severe SHD. AI-ECG achieved an AUROC of 0.872 (95% CI: 0.806-0.938), meeting the prespecified endpoint, with 86.7% (70.3-94.7) sensitivity, 72.5% (68.7-76.0) specificity, 99.0% (97.5-99.6) negative predictive value, and 14.4% (10.1-20.3) positive predictive value, with comparable performance across subgroups. AI-ECG increased sensitivity by 34.6 percentage points (95% CI: 13.0-56.0) over the native interpretation and yielded a categorical NRI of 24.3% (95% CI: 2.8-45.9), with 76.9% sensitivity and 80.0% specificity among tracings the device read as normal. An AI-ECG-guided strategy for detecting severe SHD reduced the NNT from 19.7 to 6.9 (a 64.8% reduction). Conclusions: A noise-adapted AI-ECG algorithm detected severe SHD from real-world portable 1-lead ECGs and substantially improved case finding beyond the device's rhythm interpretation, supporting AI-ECG-guided triage as a potential scalable screening strategy.
Source: medRxiv
Institutions: Yale University, New York University, Yale New Haven Hospital
Language: English
Topic: ECG Monitoring and Analysis
Subfield: Cardiology and Cardiovascular Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: green
Awards: Deep learning enhanced detection and personalized monitoring of aortic stenosis - The DETECT-AS Study, Translating Personalized Inference from Randomized Clinical Trials to Real-World Cardiovascular Care, Evaluating and Improving Utilization of Evidence-Based Medical Therapy in Patients with Heart Failure using Automated Tools in the Electronic Health Record, Yale Clinical and Translational Science Award, Claude D. Pepper Older Americans Independence Center at Yale
OpenAlex ID: W7213299684