Key result
Ambulatory 24-hour ECG risk model predicts incident AF with ~0.75 AUC.
Why the study?
Access to long-term ambulatory recording to detect AF is limited for economic and practical reasons, prompting investigation into whether 24 h ECG data can predict AF on extended cardiac monitoring.
Does a risk prediction model based on 24-h ambulatory ECG data predict atrial fibrillation detection on extended monitoring in unselected patients?
Cohort (n=18,220)
Does a risk prediction model based on 24-h ambulatory ECG data predict atrial fibrillation detection on extended monitoring in unselected patients?
Effect estimate: ROC statistic 0.7497 (95% CI 0.7336-0.7659)
Risk stratification using 24-hour ECG variables can safely identify patients at very low risk of atrial fibrillation, potentially avoiding unnecessary extended monitoring in 20% of patients.
Hypothesis-generating for deferring extended monitoring in low-risk patients; leaves open prospective validation before practice change.
BACKGROUND: Access to long-term ambulatory recording to detect atrial fibrillation (AF) is limited for economical and practical reasons. We aimed to determine whether 24 h ECG (24hECG) data can predict AF detection on extended cardiac monitoring. METHODS: We included all US patients from 2020, aged 17-100 years, who were monitored for 2-30 days using the PocketECG device (MEDICALgorithmics), without AF ≥30 s on the first day (n = 18,220, mean age 64.4 years, 42.4% male). The population was randomly split into equal training and testing datasets. A Lasso model was used to predict AF episodes ≥30 s occurring on days 2-30. RESULTS: The final model included maximum heart rate, number of premature atrial complexes (PACs), fastest rate during PAC couplets and triplets, fastest rate during premature ventricular couplets and number of ventricular tachycardia runs ≥4 beats, and had good discrimination (ROC statistic 0.7497, 95% CI 0.7336-0.7659) in the testing dataset. Inclusion of age and sex did not improve discrimination. A model based only on age and sex had substantially poorer discrimination, ROC statistic 0.6542 (95% CI 0.6364-0.6720). The prevalence of observed AF in the testing dataset increased by quintile of predicted risk: 0.4% in Q1, 2.7% in Q2, 6.2% in Q3, 11.4% in Q4, and 15.9% in Q5. In Q1, the negative predictive value for AF was 99.6%. CONCLUSION: By using 24hECG data, long-term monitoring for AF can safely be avoided in 20% of an unselected patient population whereas an overall risk of 9% in the remaining 80% of the population warrants repeated or extended monitoring.
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Johnson et al. (2023) conducted a cohort in Atrial fibrillation risk (n=18,220). 24-hour ambulatory ECG data risk model vs. Age and sex-based model was evaluated on Detection of atrial fibrillation episodes ≥30 s occurring on days 2-30 (ROC statistic 0.7497, 95% CI 0.7336-0.7659). A risk model utilizing 24-hour ambulatory ECG data, including maximum heart rate and ectopic beats, accurately predicted subsequent atrial fibrillation with a ROC statistic of 0.7497.
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