The AFFORD clinical decision aid to identify emergency department patients with atrial fibrillation at low risk for 30-day adverse events.

Barrett TW, Storrow AB, Jenkins CA, Abraham RL, Liu D, Miller KF, Moser KM, Russ S, Roden DM, Harrell FE, Darbar D
Am J Cardiol. 2015 115 (6): 763-70

PMID: 25633190 · PMCID: PMC4346475 · DOI:10.1016/j.amjcard.2014.12.036

There is wide variation in the management of patients with atrial fibrillation (AF) in the emergency department (ED). We aimed to derive and internally validate the first prospective, ED-based clinical decision aid to identify patients with AF at low risk for 30-day adverse events. We performed a prospective cohort study at a university-affiliated tertiary-care ED. Patients were enrolled from June 9, 2010, to February 28, 2013, and followed for 30 days. We enrolled a convenience sample of patients in ED presenting with symptomatic AF. Candidate predictors were based on ED data available in the first 2 hours. The decision aid was derived using model approximation (preconditioning) followed by strong bootstrap internal validation. We used an ordinal outcome hierarchy defined as the incidence of the most severe adverse event within 30 days of the ED evaluation. Of 497 patients enrolled, stroke and AF-related death occurred in 13 (3%) and 4 (<1%) patients, respectively. The decision aid included the following: age, triage vitals (systolic blood pressure, temperature, respiratory rate, oxygen saturation, supplemental oxygen requirement), medical history (heart failure, home sotalol use, previous percutaneous coronary intervention, electrical cardioversion, cardiac ablation, frequency of AF symptoms), and ED data (2 hours heart rate, chest radiograph results, hemoglobin, creatinine, and brain natriuretic peptide). The decision aid's c-statistic in predicting any 30-day adverse event was 0.7 (95% confidence interval 0.65, 0.76). In conclusion, in patients with AF in the ED, Atrial Fibrillation and Flutter Outcome Risk Determination provides the first evidence-based decision aid for identifying patients who are at low risk for 30-day adverse events and candidates for safe discharge.

Copyright © 2015 Elsevier Inc. All rights reserved.

MeSH Terms (19)

Aged Algorithms Atrial Fibrillation Decision Support Techniques Emergency Service, Hospital Female Follow-Up Studies Hospitals, University Humans Male Middle Aged Prospective Studies Reproducibility of Results Risk Assessment Risk Factors Stroke Time Factors Treatment Outcome United States

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