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TRUVACE RECORD VERSION
record: TRV-2026-1286
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-10-05T06:56:30.828375Z
status: published
lens: trace
sector: health
headline: [Atrial fibrillation screening-reading the P-wave]
dek: 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, electrophysio…
gain_title: AI models analyzing sinus-rhythm ECGs can identify atrial risk phenotypes and help select patients for prolonged rhythm monitoring to improve AF detection yield.
problem_title: An AI-based P-wave risk score derived from sinus-rhythm ECG does not establish an AF diagnosis and alone does not constitute an indication for treatment, risking overinterpretation in screening.
trace_subject: AI-based P-wave risk scoring from sinus-rhythm ECG to guide atrial fibrillation screening and prolonged monitoring decisions
gain_reading: AI models analyzing sinus-rhythm ECGs can identify atrial risk phenotypes and help select patients for prolonged rhythm monitoring to improve AF detection yield.
gain_evidence: AI models additionally extract high-dimensional patterns from sinus-rhythm ECGs and can predict occult or future AF. | such risk signals can enrich the diagnostic yield of prolonged rhythm monitoring.
problem_reading: An AI-based P-wave risk score derived from sinus-rhythm ECG does not establish an AF diagnosis and alone does not constitute an indication for treatment, risking overinterpretation in screening.
problem_evidence: A P-wave or AI-based risk score neither establishes a diagnosis of AF nor, by itself, constitutes an indication for treatment.
quick_read: This narrative review examined conventional P-wave markers and AI-based ECG analysis for identifying an atrial risk phenotype that predicts occult or future atrial fibrillation despite a normal sinus-rhythm resting ECG.

It matters because AI-derived risk signals could make prolonged rhythm monitoring more efficient by targeting higher-risk patients, but uncertainty remains about prospective validation, clinical thresholds, and the risk that scores are misused as a diagnosis or treatment trigger without confirmed AF.
limitation: Narrative appraisal rather than systematic trial, and AI-based risk scores do not by themselves establish AF diagnosis or justify treatment, limiting direct clinical actionability.
tag: Dual reading
key_points: Atrial fibrillation is frequently intermittent and may remain undetected on short resting ECG. | Conventional markers like P-wave duration, interatrial block, and P-wave terminal force in V1 are associated with atrial remodelling and future AF. | AI models extract high-dimensional patterns from sinus-rhythm ECGs to predict occult or future AF. | Initial prospective data suggest AI-based risk signals can enrich diagnostic yield of prolonged monitoring.
rundown: The review appraised consensus documents and clinical and population studies on P-wave parameters and AI-ECG for AF risk stratification, noting that sinus-rhythm ECGs contain information on underlying atrial substrate.

Authors concluded the most plausible current application is risk-adapted selection for prolonged rhythm monitoring, explicitly stating that a P-wave or AI-based score does not diagnose AF or indicate therapy by itself.
sources:
- peer_reviewed | Die Innere Medizin | https://doi.org/10.1007/s00108-026-02193-3 | 2026-10-02
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