AI ECG Algorithm for Detecting LV Systolic Dysfunction

Summary

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.

Eligibility

Inclusion Criteria:

* 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.

Exclusion Criteria:

* 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.

Conditions2

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