Re-CHARGE-AF: Recalibration of the CHARGE-AF Model for Atrial Fibrillation Risk Prediction in Patients With Acute Stroke.

Background Performance of existing atrial fibrillation (AF) risk prediction models in poststroke populations is unclear. We evaluated predictive utility of an AF risk model in patients with acute stroke and assessed performance of a fully refitted model. Methods and Results Within an academic hospital, we included patients aged 46 to 94 years discharged for acute ischemic stroke between 2003 and 2018.

Usefulness of Rhythm Monitoring Following Acute Ischemic Stroke.

We characterized monitor utilization in stroke survivors and assessed associations with underlying clinical atrial fibrillation (AF) risk. We retrospectively analyzed consecutive patients with acute ischemic stroke 10/2018-6/2019 without prevalent AF and assessed the 6-month incidence of monitor utilization (Holter/ECG, event/patch, implantable loop recorder [ILR]) using Fine-Gray models accounting for the competing risk of death. We assessed for predictors of monitor utilization using cause-specific hazards regression adjusted for the Cohorts for Heart and Aging Research in Genomic Epidemiology AF (CHARGE-AF) score, stroke subtype, and discharge disposition.

The Implementation and Acceptability of a Mobile Application for Screening for Atrial Fibrillation at Home.

Although patients are able to easily record electrocardiograms using consumer devices, these are typically not shared with their clinicians. This article discusses the development and acceptability of a mobile application (app) that integrates with the electronic health record to facilitate screening for atrial fibrillation (AF). After app development and implementation, we compared workflows with and without the mobile app.