A Clinical Prediction Model to Estimate Risk for 30-Day Adverse Events in Emergency Department Patients With Symptomatic Atrial Fibrillation

Study objectiveAtrial fibrillation affects more than 2 million people in the United States and accounts for nearly 1% of emergency department (ED) visits. Physicians have little information to guide risk stratification of patients with symptomatic atrial fibrillation and admit more than 65%. Our aim is to assess whether data available in the ED management of symptomatic atrial fibrillation can estimate a patient's risk of experiencing a 30-day adverse event.MethodsWe systematically reviewed the electronic medical records of all ED patients presenting with symptomatic atrial fibrillation between August 2005 and July 2008. Predefined adverse outcomes included 30-day ED return visit, unscheduled hospitalization, cardiovascular complication, or death. We performed multivariable logistic regression to identify predictors of 30-day adverse events. The model was validated with 300 bootstrap replications.ResultsDuring the 3-year study period, 914 patients accounted for 1,228 ED visits. Eighty patients were excluded for non–atrial-fibrillation-related complaints and 2 patients had no follow-up recorded. Of 832 eligible patients, 216 (25.9%) experienced at least 1 of the 30-day adverse events. Increasing age (odds ratio [OR] 1.20 per decade; 95% confidence interval [CI] 1.06 to 1.36 per decade), complaint of dyspnea (OR 1.57; 95% CI 1.12 to 2.20), smokers (OR 2.35; 95% CI 1.47 to 3.76), inadequate ventricular rate control (OR 1.58; 95% CI 1.13 to 2.21), and patients receiving β-blockers (OR 1.44; 95% CI 1.02 to 2.04) were independently associated with higher risk for adverse events. C-index was 0.67.ConclusionIn ED patients with symptomatic atrial fibrillation, increased age, inadequate ED ventricular rate control, dyspnea, smoking, and β-blocker treatment were associated with an increased risk of a 30-day adverse event.