Atriumfibrilleren voorspellen met een sinusritme-ecg

In patients with cryptogenic stroke, the detection of atrial fibrillation (AF) is important, since it is an indication for the prescription of oral anticoagulation, instead of anti-platelet therapy, to decrease the chance of a recurrent ischaemic cerebral event. The presence of permanent AF is easily detected by means of an electrocardiogram (ECG). However, in paroxysmal AF patients, expensive long-term rhythm monitoring might be necessary to detect the arrhythmia. In the present article, the authors describe an algorithm, constructed by artificial intelligence and big data, to detect a ‘footprint’ of AF on a 12-lead ECG during sinus rhythm. However, despite this promising premise, the algorithm comes from a ‘black-box’ calculation and, therefore, it cannot rule out possible bias from medication or patient characteristics. Furthermore, the combination of a moderate test specificity and a low prevalence of only 10% undetected AF in the post-stroke population, results in a low positive predictive value, which is not useful for initiation of anticoagulation therapy.

Conflict of interest and financial support: potential conflicts of interest have been reported for this article. An ICMJE form provided by the author is available online along with the full text of this article.