Classification of atrial fibrillation burden pattern in cardiac implantable electronic devices

Abstract Background Atrial Fibrillation (AF) Burden has been shown to be temporally associated with increased heart failure (HF) hospitalization risk in patients with cardiac implantable electronic devices (CIEDs). AF burden patterns prior to HF events may provide additional information regarding the temporal association. Purpose Investigate the dominating AF burden pattern in CIED patients prior to HF events. Methods Patients with HF events from de-identified Electronic Health Record dataset from 2007-2021 and 120 days of preceding CIED-derived continuous rhythm data from a linked manufacturer’s data warehouse were included. Patients were categorized into three groups based on the occurrence of AF burden of ≥5.5 hours (defined as AF event per earlier studies) on any given day over a 30-day period in 1-30 days before (case period) and 91-120 days (control period) before the first HF event in each patient. Self-organizing map was used to find dominant AF burden patterns in the case and control periods for these three groups of patients. Results A total of 7257 earliest HF events with AF burden available in case and control period was included. Three dominant AF burden patterns were observed in the self-organizing maps analysis: patients with no AF or paroxysmal AF, new onset persistent AF (≥7 continuous days of AF), chronic AF or long-standing persistent AF (≥30 continuous days of AF). Patients with AF burden (a day with ≥5.5 hours) in case period and no AF burden in control (763 patients) have new onset persistent AF in 56% of cases and paroxysmal AF in the rest 44%. Whereas, in patients with AF burden (a day with ≥5.5 hours) in control period but no AF burden in case period (194 patients) had chronic or long-standing persistent AF in 17% and paroxysmal AF in 83%. Finally, patients with a day of AF burden (a day with ≥5.5 hours) in both case and control period (1324 patients) had chronic AF or long-standing persistent AF in 72%, paroxysmal AF in 12%, and new onset persistent AF in 16% of the cases. Conclusion Patients with HF events and significant AF burden have larger proportion of paroxysmal and new onset persistent AF in patients with AF 30 days prior to HF events. Larger proportion of patients with HF events had chronic or long-standing persistent AF in patients who have AF burden more than 90 days before HF event.AF Burden Pattern Chart

Authors

Institutions

Publication Details

Journal
EP Europace
Published
2025-05-01
DOI
https://doi.org/10.1093/europace/euaf085.248
Primary Topic
Cardiac pacing and defibrillation studies
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Classification of atrial fibrillation burden pattern in cardiac implantable electronic devices

Shounak Majumder, Neethu Vasudevan, Shantanu Sarkar
EP Europace
Cardiac pacing and defibrillation studies
article

Classification of atrial fibrillation burden pattern in cardiac implantable electronic devices

Shounak Majumder, Neethu Vasudevan, Shantanu Sarkar
article en

Abstract

Abstract Background Atrial Fibrillation (AF) Burden has been shown to be temporally associated with increased heart failure (HF) hospitalization risk in patients with cardiac implantable electronic devices (CIEDs). AF burden patterns prior to HF events may provide additional information regarding the temporal association. Purpose Investigate the dominating AF burden pattern in CIED patients prior to HF events. Methods Patients with HF events from de-identified Electronic Health Record dataset from 2007-2021 and 120 days of preceding CIED-derived continuous rhythm data from a linked manufacturer’s data warehouse were included. Patients were categorized into three groups based on the occurrence of AF burden of ≥5.5 hours (defined as AF event per earlier studies) on any given day over a 30-day period in 1-30 days before (case period) and 91-120 days (control period) before the first HF event in each patient. Self-organizing map was used to find dominant AF burden patterns in the case and control periods for these three groups of patients. Results A total of 7257 earliest HF events with AF burden available in case and control period was included. Three dominant AF burden patterns were observed in the self-organizing maps analysis: patients with no AF or paroxysmal AF, new onset persistent AF (≥7 continuous days of AF), chronic AF or long-standing persistent AF (≥30 continuous days of AF). Patients with AF burden (a day with ≥5.5 hours) in case period and no AF burden in control (763 patients) have new onset persistent AF in 56% of cases and paroxysmal AF in the rest 44%. Whereas, in patients with AF burden (a day with ≥5.5 hours) in control period but no AF burden in case period (194 patients) had chronic or long-standing persistent AF in 17% and paroxysmal AF in 83%. Finally, patients with a day of AF burden (a day with ≥5.5 hours) in both case and control period (1324 patients) had chronic AF or long-standing persistent AF in 72%, paroxysmal AF in 12%, and new onset persistent AF in 16% of the cases. Conclusion Patients with HF events and significant AF burden have larger proportion of paroxysmal and new onset persistent AF in patients with AF 30 days prior to HF events. Larger proportion of patients with HF events had chronic or long-standing persistent AF in patients who have AF burden more than 90 days before HF event.AF Burden Pattern Chart

EP EuropaceVol. 27(Supplement_1)
Medtronic (Ireland) (IE)
Openalex Percentile: Top 79%
Cardiac pacing and defibrillation studies
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.