Multi-Center Validation of an Artificial Intelligence-Enabled ECG Model to Predict 1-Year Risk of Atrial Fibrillation or Flutter

BACKGROUND: Atrial fibrillation (AF) is the most common cardiac arrhythmia associated with an increased risk of stroke and heart failure. AF is often asymptomatic and paroxysmal, making diagnosis challenging. Artificial intelligence (AI) applied to electrocardiogram (ECG) interpretation is a promising approach for improved diagnosis. Although ECG-AI studies have shown promise, the common practice of evaluating based on data from single institutions may overestimate performance. External validation is essential to ensure that AI models generalize well to diverse settings and populations. OBJECTIVE: This study aimed to externally validate an ECG-AI model for predicting 1-year AF risk. METHODS: In this retrospective study, ECG data from 3 clinical sites were aggregated and patients' charts were manually abstracted to define inclusion and exclusion criteria (age 65+ years with no previous AF or history of pacer/defibrillator use) and endpoints (new AF diagnosis within 1 year or 1 year of AF-free follow-up). The sensitivity and specificity of a risk score from an ECG-AI model were evaluated against prespecified minimum values of 20% and 85%, respectively. RESULTS: The analysis included 4017 patients, with 240 (6.0%) developing AF within 1 year. The ECG-AI model returned an "increased risk" result for 391 patients (9.7%), including 74 who developed AF (sensitivity 31%; 95% confidence interval 25-37). A total of 3626 patients had a "no increased risk" result, with 3460 remaining free from AF (specificity 92%; 95% confidence interval 91-92). CONCLUSION: The results validate the performance of the Tempus ECG-AF model and support its clinical use for AF risk stratification.