Involvement of Autonomic Nervous System in New-Onset Atrial Fibrillation during Acute Myocardial Infarction

Area of Science:

• Cardiology
• Autonomic Nervous System Physiology
• Cardiac Electrophysiology

Background:

• Atrial fibrillation (AF) is a common complication following acute myocardial infarction (AMI), significantly increasing mortality risk.
• The underlying pathophysiology of AF in the context of AMI remains incompletely understood.
• Heart rate variability (HRV), assessed via Holter-ECG, reflects autonomic nervous system (ANS) activity and may play a crucial role in AF onset during AMI.

Purpose of the Study:

• To investigate the association between autonomic dysregulation and the occurrence of new-onset AF in patients experiencing AMI.
• To identify specific HRV parameters indicative of ANS imbalance predictive of AF in AMI.

Main Methods:

• Analysis of consecutive patients hospitalized for AMI between June 2001 and November 2014 as part of the RICO survey.
• Holter-ECG monitoring performed within 24 hours of admission to measure temporal and spectral HRV.
• Multivariate logistic regression analysis to determine predictors of AF, including HRV parameters, age, heart rate, and NT-proBNP levels.

Main Results:

• Of 2040 included patients, 168 (8.2%) developed AF. AF patients were older, had more hypertension, and lower left ventricular ejection fraction (LVEF).
• Patients with AF exhibited higher pNN50 values and a significantly lower LF/HF ratio (median 0.88 vs. 2.75, p < 0.001) compared to the sinus rhythm group.
• A low LF/HF ratio (<1.735) was a strong independent predictor of AF (OR: 3.377), surpassing age, heart rate, and NT-proBNP.

Conclusions:

• New-onset AF in AMI is strongly associated with autonomic nervous system dysregulation, characterized by a low LF/HF ratio.
• Elevated PNN50 and RMSSD values, alongside a low LF/HF ratio, suggest a significant role for ANS imbalance in AF development post-AMI.
• The findings highlight the potential of HRV analysis, particularly the LF/HF ratio, for predicting AF risk in AMI patients.