What does this research mean for the field?

Heart rate variability parameters PNN50, rMSSD, and SDSD improve atrial fibrillation prediction (AUC up to 0.775) in high-risk TIA and stroke patients. Novelty: ClaimNovelty.NOVEL_FINDING. Consensus alignment: ConsensusAlignment.CHALLENGES_CONSENSUS.

What question did this study set out to answer?

This research aims to evaluate the predictive value of heart rate variability parameters for detecting atrial fibrillation in patients with ischaemic stroke or high-risk TIA.

January 24, 2026Frontiers in Stroke0 citationsOpen Access

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

KMKurt MoelggInnsbruck Medical University AKAnel KarisikInnsbruck Medical University LBLucie BuergiInnsbruck Medical University

Key Result

Heart rate variability parameters PNN50, rMSSD, and SDSD improved atrial fibrillation prediction (AUC up to 0.775) in high-risk TIA and stroke patients.

Key Points

This research aims to evaluate the predictive value of heart rate variability parameters for detecting atrial fibrillation in patients with ischaemic stroke or high-risk TIA.
Analyzed data from 697 patients with non-AF ischaemic stroke or high-risk TIA from the STROKE-CARD Registry.
Assessed eight time-domain heart rate variability parameters using 24-hour Holter ECGs.
Employed ROC analyses, logistic regression, and the Youden index to identify optimal cut-offs and compare results with existing clinical scores.
New-onset atrial fibrillation was detected in 28 patients (4.0%).
PNN50, rMSSD, and SDSD showed the best discrimination for predicting AF, with AUC values of 0.711, 0.766, and 0.775 respectively.
Optimal cut-offs were identified as 5.5% for PNN50, 48.5 ms for rMSSD, and 43.5 ms for SDSD.
Adding HRV parameters significantly improved AF risk prediction beyond existing clinical scores.

Structured PICO

Do routine heart rate variability parameters improve the detection of incident atrial fibrillation in patients with non-AF ischaemic stroke or high-risk TIA compared to existing clinical scores?

Population

697 patients with non-AF ischaemic stroke or non-AF high-risk transient ischaemic attack (TIA) (ABCD2-score ≥4) in sinus rhythm, median age 71, 36.7% female, from the STROKE-CARD Registry in Austria.

Intervention

Assessment of eight time-domain heart rate variability (HRV) parameters (including PNN50, rMSSD, and SDSD) derived from routine 24-h Holter ECGs.

Comparator

Existing clinical risk scores (Brown-ESUS AF and AS5F scores).

Outcome

Incident atrial fibrillation (new-onset AF) detected within 1 year follow-up.hard clinical

Routine Holter ECG-derived HRV parameters (PNN50, rMSSD, SDSD) significantly enhance the prediction of occult atrial fibrillation in patients with non-cardioembolic stroke or high-risk TIA compared to existing clinical scores.

Main Result

Absolute Event Rate: 0% vs 0%

Abstract

Introduction 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. Methods 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. Results 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. Conclusions 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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