AI ECG Algorithm for Detecting LV Systolic Dysfunction

Официальное название

Prospective Observational Cohort Study of Deep Learning-based ECG Algorithm for Detecting Left Ventricular Systolic Dysfunction

Обзор

This prospective observational cohort study aims to evaluate the clinical performance of a deep learning-based electrocardiography (ECG) algorithm (DeepECG LVSD) for detecting left ventricular systolic dysfunction (LVSD), defined as left ventricular ejection fraction (LVEF) ≤40%, using transthoracic echocardiography as the reference standard. Approximately 15,000 adult patients undergoing both ECG and echocardiography within 30 days at Ajou University Hospital will be enrolled. Diagnostic performance will be assessed using the area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, positive predictive value, negative predictive value, and accuracy. Secondary analyses will evaluate the association between AI-predicted LVSD and 30-day clinical outcomes, including all-cause mortality, emergency department visits, and heart failure rehospitalization.

Подробное описание

Left ventricular systolic dysfunction (LVSD) is associated with an increased risk of heart failure, hospitalization, and mortality. Although transthoracic echocardiography is the standard method for assessing left ventricular ejection fraction (LVEF), its widespread use as a screening tool is limited by availability, cost, and the need for specialized personnel. Artificial intelligence (AI)-based electrocardiography (ECG) algorithms have emerged as promising tools for identifying patients with reduced LVEF using routinely acquired ECG signals.

DeepECG LVSD is a deep learning-based ECG algorithm developed to detect LVSD (LVEF ≤40%) from standard 12-lead ECG recordings. Previous retrospective validation studies demonstrated high diagnostic performance; however, prospective clinical validation in real-world practice remains limited.

The purpose of this prospective observational cohort study is to evaluate the diagnostic performance and clinical utility of DeepECG LVSD in adult patients undergoing both ECG and transthoracic echocardiography at Ajou University Hospital. Approximately 15,000 patients aged 19 years or older who have undergone ECG and echocardiography within 30 days will be enrolled.

The primary objective is to assess the accuracy of the AI algorithm for detecting LVSD using echocardiographic LVEF as the reference standard. Diagnostic performance will be evaluated using the area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, positive predictive value, negative predictive value, and overall accuracy.

Secondary objectives include evaluating the association between AI-predicted LVSD and short-term clinical outcomes, including 30-day all-cause mortality, emergency department visits, and heart failure rehospitalization. Exploratory subgroup analyses will assess algorithm performance according to demographic and clinical characteristics, including age, sex, heart failure status, chronic kidney disease, hypertension, diabetes mellitus, and the interval between ECG and echocardiography.

This study is designed as a minimal-risk observational study and will provide prospective evidence regarding the effectiveness of AI-enabled ECG screening for LVSD in routine clinical practice. Findings from this study may support broader implementation of AI-based ECG tools for the early identification of patients at risk for heart failure and reduced left ventricular systolic function.

Вмешательства

• Другое None-placebo
  There is no intervention group

Первичные конечные точки

• AUROC for detection of LVSD (LVEF ≤40%) [Срок оценки: During procedure]

Критерии участия

Критерии включения

• Adults aged ≥19 years.
• Patients who underwent both transthoracic echocardiography and 12-lead electrocardiography (ECG) at Ajou University Hospital in the outpatient, inpatient, or emergency department setting.
• ECG and echocardiography performed within 30 days of each other.

Критерии исключения

• Interval between ECG and echocardiography greater than 30 days.
• Missing or corrupted original ECG waveform data (XML or HL7 format).
• Presence of an implanted cardiac device, including a permanent pacemaker, implantable cardioverter-defibrillator (ICD), or cardiac resynchronization therapy (CRT) device.
• Missing age, sex, or left ventricular ejection fraction (LVEF) data.

Критерии приведены из реестра в оригинале (на английском). Окончательную оценку соответствия проводит исследовательский центр.

Здоровые добровольцы: Нет

Дизайн исследования

Модель наблюдения

Когортное

Центры проведения

South Korea · 1 центр

• Ajou University School of Medicine — Suwon

Публикации

• Lopez-Jimenez F, Alger HM, Attia ZI, Barry B, Chatterjee R, Dolor R, Friedman PA, Greene SJ, Greenwood J, Gundurao V, Hackett S, Jain P, Kinaszczuk A, Mehta K, O'Grady J, Pandey A, Pullins C, Puranik AR, Ranganathan MK, Rushlow D, Stampehl M, Subramanian V, Vassor K, Zhu X, Awasthi S. A multicenter pragmatic implementation study of AI-ECG-based clinical decision support software to identify low LV PMID 40276542
• Choi J, Lee S, Chang M, Lee Y, Oh GC, Lee HY. Author Correction: Deep learning of ECG waveforms for diagnosis of heart failure with a reduced left ventricular ejection fraction. Sci Rep. 2022 Oct 13;12(1):17191. doi: 10.1038/s41598-022-22012-7. No abstract available. PMID 36229570
• Attia ZI, Kapa S, Lopez-Jimenez F, McKie PM, Ladewig DJ, Satam G, Pellikka PA, Enriquez-Sarano M, Noseworthy PA, Munger TM, Asirvatham SJ, Scott CG, Carter RE, Friedman PA. Screening for cardiac contractile dysfunction using an artificial intelligence-enabled electrocardiogram. Nat Med. 2019 Jan;25(1):70-74. doi: 10.1038/s41591-018-0240-2. Epub 2019 Jan 7. PMID 30617318

Идентификаторы

NCT: NCT07636759 · AJOUIRB-OB-2026-001

Первоисточники (государственные реестры)

• ClinicalTrials.gov (NIH, США)

Открыть это исследование на ClinicalTrials.gov ↗