Artificial intelligence for prediction of atrial fibrillation in the stroke unit: a retrospective derivation validation cohort study.

ABSTRACT:

Background

Paroxysmal atrial fibrillation (AF) is a major cause of stroke but is often undetected in routine clinical practice. Effective stratification is needed to identify patients with stroke who might benefit the most from intensified AF screening. Several artificial intelligence models have been proposed to predict AF based on ECG in sinus rhythm, but broad implementation has been limited. The most valuable input features and most effective model design for AF prediction are also unclear.

Methods

We developed and tested AF prediction models utilising continuous electrocardiogram monitoring (CEM) recordings from the first 72 h after admission and multiple clinical input features from patients with stroke hospitalised at Charité, Berlin, Germany, between September 2020 a

SUBMITTER: Schoels M 

PROVIDER: S-EPMC12341230 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature