Use of continuous cardiac monitoring to assess the influence of atrial fibrillation burden and patterns on patient symptoms and healthcare utilization: The DEFINE AFib study
Dhanunjaya R. Lakkireddy MD , Andrea M. Russo MD , Elaine M. Hylek MD, MPH , Suneet Mittal MD , James Peacock MD, MS , Mellanie True Hills , Evan J. Stanelle MS, MA , Jeffrey D. Lande MS, PhD , Noreli C. Franco PhD , Lawrence C. Johnson PhD , Rahul Kanwar MS , Rod S. Passman MD, MHS , Jonathan P. Piccini MD, MHS
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Abstract
Background
Atrial fibrillation (AF) has a significant impact on health and quality of life. The relationship of AF burden and temporal patterns of AF on patient symptoms, outcomes, and healthcare utilization is unknown. Insertable cardiac monitors (ICMs) are a strategic and as yet untapped, tool to investigate these relationships.
Objective
The DEFINE Atrial Fibrillation (DEFINE AFib) study will evaluate how AF burden and patterns are associated with changes in AF-related healthcare utilization (AFHCU) and patient-reported quality of life.
Methods
This is a prospective, observational, multicenter study with a unique design that supports a complete method of assessing AF as a multifactorial disease. Patients with AF implanted with an ICM will be enrolled in the study and managed through an app-based research platform on their smartphone. Patients will be remotely monitored and patient-reported outcomes will be collected via the app. AFHCU will be confirmed via the participant’s medical record.
Results
The primary analysis will evaluate whether summary and episodic measurements collected by ICMs are associated with changes in AFHCU. Secondary analyses will determine the relationship between AF characteristics and quality of life, timing and severity of AF-related complications, patient engagement, reliability of patient-reported outcomes, data from other digital rhythm detectors, and heterogeneity in care quality and AFHCU.
Conclusion
The DEFINE AFib study will provide valuable insights into the association between dynamic measures of AF and AFHCU in a patient population with known AF. The results may demonstrate the impact of ICM-detected AF on patient outcomes and help isolate novel AF patterns predictive of clinical risk.
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