ECG predictors of AF: A systematic review (predicting AF in ischaemic stroke-PrAFIS)

• Published in: Clinical neurology and neurosurgery 2024-02, Vol.237, p.108164-108164, Article 108164
• Main Authors: Berry-Noronha, Alexander, Bonavia, Luke, Song, Edmund, Grose, Daniel, Johnson, Damian, Maylin, Erin, Oqueli, Ernesto, Sahathevan, Ramesh
• Format: Article
• Language: English
• Subjects: Atrial fibrillation; Atrial Fibrillation - complications; Atrial Fibrillation - diagnosis; Brain Ischemia - diagnosis; Cardiac arrhythmia; Cardiomyopathy; Cohort analysis; EKG; Electrocardiogram; Electrocardiography; Fibrillation; Heart; Humans; Hypertrophy; Ischemia; Ischemic Stroke - diagnosis; Mortality; Prediction; Prediction models; Prevention; Prospective Studies; Retrospective Studies; Risk factors; Stroke; Systematic review
• ISSN: 0303-8467; 1872-6968
• DOI: 10.1016/j.clineuro.2024.108164

In 25% of patients presenting with embolic stroke, a cause is not determined. Atrial fibrillation (AF) is a commonly identified mechanism of stroke in this population, particularly in older patients. Conventional investigations are used to detect AF, but can we predict AF in this population and generally? We performed a systematic review to identify potential predictors of AF on 12-lead electrocardiogram (ECG). We conducted a search of EMBASE and Medline databases for prospective and retrospective cohorts, meta-analyses or case-control studies of ECG abnormalities in sinus rhythm predicting subsequent atrial fibrillation. We assessed quality of studies based on the Newcastle-Ottawa scale and data were extracted according to PRISMA guidelines. We identified 44 studies based on our criteria. ECG patterns that predicted the risk of developing AF included interatrial block, P-wave terminal force lead V1, P-wave dispersion, abnormal P-wave-axis, abnormal P-wave amplitude, prolonged PR interval, left ventricular hypertrophy, QT prolongation, ST-T segment abnormalities and atrial premature beats. Furthermore, we identified that factors such as increased age, high CHADS-VASC, chronic renal disease further increase the positive-predictive value of some of these parameters. Several of these have been successfully incorporated into clinical scoring systems to predict AF. There are several ECG abnormalities that can predict AF both independently, and with improved predictive value when combined with clinical risk factors, and if incorporated into clinical risk scores. Improved and validated predictive models could streamline selection of patients for cardiac monitoring and initiation of oral anticoagulants. •ECG abnormalities can be used to predict AF independently.•When these are used together or combined with clinical risk factors, there is improved predictive value.•There is a urgent need to better develop clinical risk scores to identify ESUS cohorts that are at increased risk of AF.