Innere Medizin (Heidelberg, Germany) · 2026
[Atrial fibrillation screening-reading the P-wave].
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Wiedmann Felix, Weitkamp Frederike Marie, Schmidt Constanze
Аннотация
BACKGROUND: Atrial fibrillation (AF) is frequently intermittent and may therefore remain undetected on a short resting electrocardiogram (ECG). At the same time, a sinus-rhythm ECG contains information on the underlying atrial substrate.
OBJECTIVE: To review conventional and artificial intelligence (AI)-based ECG markers of an atrial risk phenotype and their potential role in targeted AF screening.
MATERIALS AND METHODS: Narrative appraisal of current consensus and guideline documents and of clinical, electrophysiological, and population-based studies on P‑wave parameters, AI-ECG, and risk-adapted AF screening.
RESULTS: P‑wave duration, interatrial block, P‑wave terminal force in V1, and other parameters are associated with atrial remodelling and future AF. AI models additionally extract high-dimensional patterns from sinus-rhythm ECGs and can predict occult or future AF. Initial prospective data indicate that such risk signals can enrich the diagnostic yield of prolonged rhythm monitoring.
CONCLUSION: The most plausible current clinical application is risk-adapted selection for prolonged rhythm monitoring. A P-wave or AI-based risk score neither establishes a diagnosis of AF nor, by itself, constitutes an indication for treatment.
OBJECTIVE: To review conventional and artificial intelligence (AI)-based ECG markers of an atrial risk phenotype and their potential role in targeted AF screening.
MATERIALS AND METHODS: Narrative appraisal of current consensus and guideline documents and of clinical, electrophysiological, and population-based studies on P‑wave parameters, AI-ECG, and risk-adapted AF screening.
RESULTS: P‑wave duration, interatrial block, P‑wave terminal force in V1, and other parameters are associated with atrial remodelling and future AF. AI models additionally extract high-dimensional patterns from sinus-rhythm ECGs and can predict occult or future AF. Initial prospective data indicate that such risk signals can enrich the diagnostic yield of prolonged rhythm monitoring.
CONCLUSION: The most plausible current clinical application is risk-adapted selection for prolonged rhythm monitoring. A P-wave or AI-based risk score neither establishes a diagnosis of AF nor, by itself, constitutes an indication for treatment.