Abstract

Purpose: Silent brain infarcts (SBI) are frequently detected in patients with atrial fibrillation (AF), but it is unknown whether SBI are linked to autonomic dysfunction. We aimed to explore the association of autonomic dysfunction with SBI in AF patients.Methods: 1,358 AF patients without prior stroke or TIA underwent brain MRI and 5-min resting ECG. We divided our cohort into AF patients who presented in sinus rhythm (SR-group, n = 816) or AF (AF-group, n = 542). HRV triangular index (HRVI), standard deviation of normal-to-normal intervals, mean heart rate, root mean square root of successive differences of normal-to-normal intervals, 5-min total power and power in the low frequency, high frequency and very low frequency range were calculated. Primary outcome was presence of SBI in the SR group, defined as large non-cortical or cortical infarcts. Secondary outcomes were SBI volumes and topography.Results: Mean age was 72 ± 9 years, 27% were female. SBI were detected in 10.5% of the SR group and in 19.9% of the AF group (p < 0.001). HRVI <15 was the only HRV parameter associated with the presence of SBI after adjustment for clinical covariates in the SR group [odds ratio (OR) 1.67; 95% confidence interval (CI): 1.03–2.70; p = 0.037]. HRVI <15 was associated with larger brain infarct volumes [β (95% CI) −0.47 (−0.84; −0.09), p = 0.016] in the SR group and was more frequently observed in patients with right- than left-hemispheric SBI (p = 0.017).Conclusion: Impaired HRVI is associated with SBI in AF patients. AF patients with autonomic dysfunction might undergo systematic brain MRI screening to initiate intensified medical treatment.Clinical Trials Gov Identifier: NCT02105844.

Highlights

  • Patients with atrial fibrillation (AF) have a high burden of silent brain infarcts (SBI) [1]

  • We examined whether the association of heart rate variability triangular index (HRVI) and presence of SBI depends on patient characteristics first by testing the interaction of HRVI with the relevant characteristic and by fitting models again within the subgroup

  • HRVI

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Summary

Introduction

Patients with atrial fibrillation (AF) have a high burden of silent brain infarcts (SBI) [1]. SBI increase the risk of cognitive decline to a similar degree as overt strokes [1,2,3]. It has been shown that silent large cortical and non-cortical infarcts (LNCCI) in AF patients were related to a 10-year age difference in cognitive performance [1]. Individuals with silent subcortical brain infarcts are at high risk for overt stroke [4]. Systematic brain magnetic resonance imaging (bMRI) screening to detect SBI in patients with AF is not feasible due to reduced availability, costs and contraindications. An available tool that would allow for identification of AF patients at high risk for SBI would be of high clinical value

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