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

Abstract

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.

Trial registration: ClinicalTrials.gov NCT01138644.

Conflict of interest statement

There are no conflicts of interest in connection with this submission or are there any copyright constraints.

Copyright © 2015 Elsevier Inc. All rights reserved.

Figures

Appendix Figure A.1. Calibration Curve For AFFORD Decision Aid Using The 4th Level Of The Outcome Hierarchy As The Cut-Off (n= 94 Events)

This plot illustrates the calibration accuracy of the original model (“Apparent”) and the bootstrap model (“Bias-corrected”) for the subgroup of 30-day adverse events (i.e. atrial fibrillation-related unscheduled hospitalization or more severe event) with locally weighted scatterplot smoothing used to model the relationship between actual and predicted probabilities. As can be seen, the model’s calibration function estimate is slightly nonlinear, with the corrected calibration showing good agreement with the apparent calibration. The corresponding c-statistic (95% CI) using the 4th level of the outcome as the cut-off was 0.74 (0.69, 0.8).

Appendix Figure A.2. Calibration Curve For AFFORD Decision Aid Using The 8th Level Of The Outcome Hierarchy As The Cut-Off (n= 41 Events)

This plot illustrates the calibration accuracy of the original model (“Apparent”) and the bootstrap model (“Bias-corrected”) for the subgroup of 30-day adverse events (i.e. stroke or death) with locally weighted scatterplot smoothing used to model the relationship between actual and predicted probabilities. As can be seen, the model’s calibration function estimate is slightly nonlinear, with the corrected calibration showing good agreement with the apparent calibration. The corresponding c-statistic (95% CI) using the 8th level of the outcome as the cut-off was 0.81 (95% CI, 0.74, 0.88).

Appendix Figure A.3. Calibration Curve For AFFORD Decision Aid In Patients (n =326) Whose ED Visit Was For Primary Atrial Fibrillation

This plot illustrates the calibration accuracy of the original model (“Apparent”) and the bootstrap model (“Bias-corrected”) for 30-day adverse events with locally weighted scatterplot smoothing used to model the relationship between actual and predicted probabilities. As can be seen, the model’s calibration function estimate is slightly nonlinear, with the corrected calibration showing good agreement with the apparent calibration. The c-statistic (95% CI) for the prediction of any 30-day adverse event was 0.72 (95% CI, 0.66, 0.78).

Figure 1

AFFORD Study Flow Diagram

Figure 2. Hierarchy Of Adverse Events For AFFORD Decision Aid

The Figure presents the 10-level ordinal outcome representing the most severe adverse event experienced within 30 days of the index ED evaluation. The events of the outcome were ordered from least severe (no event) to most severe (AF-related).

Figure 3. Thirty-Day Adverse Event AFFORD Nomogram

Points are assigned for each of the 17 predictors. The total points correspond to an absolute predicted risk for 30-day adverse events.

Figure 4. Calibration Curve for AFFORD Decision Aid

This plot illustrates the calibration accuracy of the original model (“Apparent”) and the bootstrap model (“Bias-corrected”) for 30-day adverse events with locally weighted scatterplot smoothing used to model the relationship between actual and predicted probabilities. As can be seen, the model’s calibration function estimate is slightly nonlinear, with the corrected calibration showing good agreement with the apparent calibration.