Value of routine heart rate variability parameters for atrial fibrillation detection in ischaemic stroke and high-risk TIA patients

TL;DR

Heart rate variability measures from Holter ECGs can better predict new atrial fibrillation in stroke and TIA patients than current clinical scores.

Contribution

HRV parameters PNN50, rMSSD, and SDSD significantly improve AF prediction in non-AF stroke/TIA patients.

Findings

PNN50, rMSSD, and SDSD showed better AF discrimination (AUCs 0.711–0.775) than clinical scores (AUC ≤ 0.612).

Optimal HRV cut-offs (e.g., PNN50 ≥ 5.5%) strongly associated with AF (ORs 5.34–7.70, p < 0.001).

Adding HRV parameters significantly improved AF prediction beyond existing scores.

Abstract

Undetected atrial fibrillation (AF) increases the risk of recurrent ischaemic stroke, but current prediction scores do not incorporate heart rate variability (HRV) measures readily available from 24-h Holter ECGs.

In 697 patients with non-AF ischaemic stroke or non-AF high-risk transient ischaemic attack (TIA) from the STROKE-CARD Registry (NCT04582825), we assessed eight time-domain HRV parameters for predicting incident AF within 1 year. ROC analyses, logistic regression, and the Youden index were used to identify optimal cut-offs and compare HRV performance with Brown-ESUS AF and AS5F scores.

New-onset AF was detected in 28 patients (4.0%). PNN50, rMSSD, and SDSD showed the best discrimination (AUC = 0.711, 0.766, and 0.775), outperforming both clinical scores (AUC ≤ 0.612). Optimal cut-offs were 5.5% (PNN50), 48.5 ms (rMSSD), and 43.5 ms (SDSD). Dichotomized analyses confirmed strong associations with AF (ORs 5.34–7.70, all p < 0.001), and adding HRV parameters significantly improved prediction beyond existing scores.

PNN50, rMSSD, and SDSD from routine Holter ECGs enhance AF risk prediction after non-cardioembolic stroke or high-risk TIA and may support targeted monitoring strategies.

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