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

Introduction: 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). Methods: After app development and implementation, we compared workflows with and without the mobile app. Seven older adults used it during a prospective twice-daily 2-week home-based AF screening protocol and completed an acceptability survey with Likert scale responses. Results: Compliance with the screening protocol was 82%. Acceptability and usability was favorable. Patients reported confidence in the connection between the app and their medical record. Discussion: The availability of apps to capture data and facilitate a connection with health systems is critical. The app developed is a feasible solution for older patients with AF to self-monitor and report results to their health provider.

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. We estimated 5-year predicted probabilities of AF using the Cohorts for Heart and Aging Research in Genomic Epidemiology for Atrial Fibrillation (CHARGE-AF) model, by recalibrating CHARGE-AF to the baseline risk of the sample, and by fully refitting a Cox proportional hazards model to the stroke sample (Re-CHARGE-AF) model. We compared discrimination and calibration between models and used 200 bootstrap samples for optimism-adjusted measures. Among 551 patients with acute stroke, there were 70 incident AF events over 5 years (cumulative incidence, 15.2%; 95% CI, 10.6%-19.5%). Median predicted 5-year risk from CHARGE-AF was 4.8% (quartile 1-quartile 3, 2.0-12.6) and from Re-CHARGE-AF was 16.1% (quartile 1-quartile 3, 8.0-26.2). For CHARGE-AF, discrimination was moderate (C statistic, 0.64; 95% CI, 0.57-0.70) and calibration was poor, underestimating AF risk (Greenwood-Nam D'Agostino chi-square, P<0.001). Calibration with recalibrated baseline risk was also poor (Greenwood-Nam D'Agostino chi-square, P<0.001). Re-CHARGE-AF improved discrimination (P=0.001) compared with CHARGE-AF (C statistic, 0.74 [95% CI, 0.68-0.79]; optimism-adjusted, 0.70 [95% CI, 0.65-0.75]) and was well calibrated (Greenwood-Nam D'Agostino chi-square, P=0.97). Conclusions Covariates from an established AF risk model enable accurate estimation of AF risk in a poststroke population after recalibration. A fully refitted model was required to account for varying baseline AF hazard and strength of associations between covariates and incident AF.

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. Of 493 patients with acute ischemic stroke (age 65±16; 47% women), the 6-month incidence of monitor utilization was 36.5% (95% CI 31.7, 41.3), and 6-month mortality was 13.6% (10.4, 16.8). Monitoring was performed with Holter/event (n = 107; 72.3%), ILR (n = 34; 23.0%) or both (n = 7; 4.7%). Monitoring was more likely after cryptogenic (hazard ratio [HR] 4.53 [3.22, 6.39]; 6-month monitor incidence 70.6%) and cardioembolic (HR 2.43 [1.28, 4.62]; incidence 47.7%) stroke, versus other/undocumented (incidence 22.7%). Among patients with cryptogenic stroke, the 6-month incidence of ILR was 27.5% [18.5, 36.5]. Monitoring was more likely after discharge home (HR 1.80 [1.29, 2.52]; incidence 46.1%) versus facility (incidence 24.9%). Monitoring was not associated with CHARGE-AF score (HR 1.08 per 1-SD increase [0.91, 1.27]), even though CHARGE-AF was associated with incident AF (HR 1.56 [1.03, 2.35]). In conclusion, rhythm monitors are utilized after one-third of ischemic strokes. Monitoring is more frequent after cryptogenic strokes, though ILR use is low. Monitor utilization is not associated with AF risk.