The objective of this study is to evaluate whether an AI-ECG based screening strategy for detecting cardiac functional and structural abnormalities preserves clinical effectiveness and safety, compared with a conventional strategy of routine echocardiography in patients with AF, thereby demonstrating the non-inferiority of AI-ECG guided care.
Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia, with its prevalence having more than doubled over the past decade. AF is associated with an increased risk of stroke, heart failure, and mortality, thereby imposing a substantial burden on both patients and healthcare systems. Accordingly, contemporary clinical guidelines emphasize accurate diagnosis and early, integrated management of AF. In this context, transthoracic echocardiography has become a standard diagnostic tool for the assessment of structural heart disease and cardiac function. Despite being non-invasive and relatively low-cost, echocardiography is subject to several system-level limitations in routine clinical practice, including dependence on specialized equipment and trained personnel, scheduling delays, and inefficiencies related to repeated examinations. These constraints may create bottlenecks in the timely initiation and optimization of AF management. In real-world practice, a considerable proportion of patients with AF undergo echocardiography primarily to confirm the absence of significant structural heart disease or impaired function. A uniform strategy of performing echocardiography in all patients with AF may not be optimal from the perspectives of patient convenience and healthcare resource utilization. Moreover, depending on healthcare system capacity, access to echocardiography may delay the timely selection of optimal AF management. Conversely, selectively performing echocardiography in patients with a higher likelihood of structural or functional cardiac abnormalities may allow for a more efficient, timely, and targeted diagnostic approach. Artificial intelligence-enabled electrocardiography (AI-ECG) offers several practical advantages, including very short acquisition time, patients' convenience, substantially lower cost, and feasibility for repeated assessments during follow-up. AI-ECG may enable sensitive detection of changes in a patient's cardiac status over time. Positioning AI-ECG as an initial screening tool to identify patients with suspected structural or functional heart disease could facilitate a "screening-confirmation" diagnostic pathway, in which echocardiography is reserved for patients with abnormal or suspicious findings on AI-ECG. Such an approach has the potential to streamline initial and follow-up evaluations while maintaining patient safety.
Study Type
INTERVENTIONAL
Allocation
RANDOMIZED
Purpose
TREATMENT
Masking
NONE
Enrollment
1,724
An artificial intelligence algorithm applied to standard 12-lead electrocardiography designed to predict cardiac structural or functional abnormalities. This tool guides the decision to perform or withhold downstream echocardiography.
Standard Transthoracic Echocardiography used to assess cardiac structure and function, serving as the reference standard for guiding clinical management in this study arm.
Seoul National University Hospital
Seoul, South Korea
Composite of all-cause Mortality, Stroke, CV Hospitalization, and AAD-Related SAEs
Evaluation of the effectiveness of the strategy based on a composite endpoint comprising the following clinical events: 1. All-cause mortality; 2. Stroke or systemic thromboembolism; 3. Hospitalization due to worsening heart failure or acute coronary syndrome; 4. Serious adverse events related to antiarrhythmic drug therapy. The endpoint is defined as the time to the first occurrence of any of these components.
Time frame: up to 10 years
All-cause mortality
Time frame: up to 10 years
Stroke or systemic thromboembolism
Time frame: up to 10 years
Heart failure worsening
Heart failure worsening: An outpatient heart failure episode requiring intravenous diuretic therapy or initiation or escalation of oral diuretics, or hospitalization for heart failure (defined as heart failure being the primary reason for admission or requiring treatment in a healthcare facility for ≥12 hours with intravenous diuretics).
Time frame: up to 10 years
Hospitalization due to acute coronary syndrome
Time frame: up to 10 years
Serious adverse events related to antiarrhythmic drug therapy
Serious adverse events related to antiarrhythmic drug therapy: Hypotension, symptomatic drug-induced bradycardia, atrioventricular block, drug-induced atrial flutter or atrial tachycardia, torsade de pointes, ventricular tachycardia, ventricular fibrillation, or syncope.
Time frame: up to 10 years
Proportion of patients receiving rhythm control therapyc after the initial diagnosis of AF
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Rhythm control therapy: Use of antiarrhythmic drugs, electrical cardioversion, or catheter ablation for AF.
Time frame: up to 10 years
Time from initial diagnosis of AF to first rhythm control therapy
Rhythm control therapy: Use of antiarrhythmic drugs, electrical cardioversion, or catheter ablation for AF.
Time frame: up to 10 years
Changes in oral anticoagulation from warfarin to a DOAC or vice versa, based on the reassessment of cardiac function and structure
Time frame: up to 10 years
Changes in the class of antiarrhythmic drugs (AADs) prescribed, based on the reassessment of cardiac function and structure
i.e., modification of Class Ic AAD to Class III AAD. Changes in antiarrhythmic drug therapy due solely to inadequate AF rate or rhythm control are not included.
Time frame: up to 10 years
Changes in heart failure medications resulting from reassessment of cardiac function and structure
Initiation, dose escalation, or dose reduction of heart failure medication classes including beta-blockers, mineralocorticoid receptor antagonists (MRAs), renin-angiotensin-aldosterone system (RAAS) inhibitors or angiotensin receptor-neprilysin inhibitors (ARNIs), sodium-glucose cotransporter 2 inhibitors (SGLT2i), or other agents (e.g., ivabradine, vericiguat, hydralazine/nitrate
Time frame: up to 10 years
The proportion of patients maintaining sinus rhythm
Time frame: up to 10 years
Quality of life assessed by European Quality of Life-5 Dimensions (EQ-5D) at baseline, 12 months, and 24 months
EQ-5D scores range from 0 to 100, with higher scores indicating better health status.
Time frame: up to 10 years
NT-proBNP levels at baseline, 12 months, and 24 months
Time frame: up to 10 years
Diagnostic performance of the AI-ECG algorithm for detecting cardiac functional and structural abnormalities
Assessment of the AI-ECG algorithm's ability to detect functional and structural cardiac abnormalities. Performance metrics will include Accuracy, Sensitivity, Specificity, Positive Predictive Value, and Negative Predictive Value.
Time frame: up to 10 years
Investigator satisfaction with AI-ECG use at 12 months and 24 months reported by a self-reported questionnaire
Time frame: up to 10 years