Using Atrial Fibrillation Burden Trends and Machine Learning to Predict Near-Term Risk of Cardiovascular Hospitalization

Abstract

BACKGROUND: Atrial fibrillation is associated with an increased risk of cardiovascular hospitalization (CVH), which may be triggered by changes in daily burden. Machine learning of dynamic trends in atrial fibrillation burden, as measured by insertable cardiac monitors (ICMs), may be useful in predicting near-term CVH. METHODS: Using Optum's deidentified Clinformatics Data Mart Database (2007-2019), linked with the Medtronic CareLink ICM database, we identified patients with >1 days of ICM-detected atrial fibrillation. ICM-detected diagnostic parameters were transformed into simple moving averages over different periods for daily follow-up. A diagnostic trend was defined as the comparison of 2 simple moving averages of different periods for each diagnostic parameter. CVH was defined as any hospital, emergency department, or ambulatory surgical center encounter with a cardiovascular diagnosis-related group or diagnosis code. Machine learning was used to determine which diagnostic trends could best predict patient risk 5 days before CVH. RESULTS: <0.001), a 20% increase in predictive power. CONCLUSIONS: Trends in atrial fibrillation were strongly associated with near-term CVH, especially above-average burden coupled with low patient activity. This approach could provide actionable information to guide treatment and reduce CVH.

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