# 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.

## Key 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.