Software-based analysis of 1-hour Holter ECG to select for prolonged ECG monitoring after stroke
Sonja Gröschel1, Björn Lange2, Katrin Wasser3
1Department of Neurology, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany.
Insights
Automated software analyzing the first hour of Holter ECG can identify ischemic stroke patients at high risk for paroxysmal atrial fibrillation (pAF), improving prediction accuracy.
Area of Science:
- Cardiology
- Neurology
- Medical Informatics
Background:
- Paroxysmal atrial fibrillation (pAF) detection in ischemic stroke patients is crucial for secondary prevention.
- Current methods for prolonged ECG monitoring are not routinely applied due to resource limitations.
Purpose of the Study:
- To evaluate an automated software (AA) for predicting pAF in ischemic stroke patients using the initial hour of Holter ECG data.
- To assess if AA risk stratification improves pAF prediction compared to clinical factors alone.
Main Methods:
- A prospective multicenter study involving 1031 acute ischemic stroke/TIA patients in sinus rhythm.
- The first hour of 72-hour Holter ECG data was analyzed by AA to classify patients into 'no risk' or 'risk of AF' groups.
- Clinical variables and AA classification were used to predict pAF detection.
Main Results:
- pAF was detected in 5.2% of patients, with higher detection rates in the AA 'risk of AF' group (17.8% vs 3.6%).
- AA risk stratification was an independent predictor of pAF (OR 3.814, P < 0.001), alongside age, NIHSS, and prior thrombolysis.
- AA significantly improved the AUC for pAF prediction compared to a clinical score alone (0.789 vs 0.751, P = 0.022).
Conclusions:
- Automated software-based ECG risk stratification effectively identifies high-risk patients for pAF during 72-hour Holter monitoring.
- This approach enhances the predictive value of common clinical risk factors for AF detection in stroke patients.
Objective:
Identification of ischemic stroke patients at high risk for paroxysmal atrial fibrillation (pAF) during 72 hours Holter ECG might be useful to individualize the allocation of prolonged ECG monitoring times, currently not routinely applied in clinical practice.
Methods:
In a prospective multicenter study, the first analysable hour of raw ECG data from prolonged 72 hours Holter ECG monitoring in 1031 patients with acute ischemic stroke/TIA presenting in sinus rhythm was classified by an automated software (AA) into "no risk of AF" or "risk of AF" and compared to clinical variables to predict AF during 72 hours Holter-ECG.
Results:
pAF was diagnosed in 54 patients (5.2%; mean age: 78 years; female 56%) and was more frequently detected after 72 hours in patients classified by AA as "risk of AF" (n = 21, 17.8%) compared to "no risk of AF" (n = 33, 3.6%). AA-based risk stratification as "risk of AF" remained in the prediction model for pAF detection during 72 hours Holter ECG (OR3.814, 95% CI 2.024-7.816, P < 0.001), in addition to age (OR1.052, 95% CI 1.021-1.084, P = 0.001), NIHSS (OR 1.087, 95% CI 1.023-1.154, P = 0.007) and prior treatment with thrombolysis (OR2.639, 95% CI 1.313-5.306, P = 0.006). Similarly, risk stratification by AA significantly increased the area under the receiver operating characteristic curve (AUC) for prediction of pAF detection compared to a purely clinical risk score (AS5F alone: AUC 0.751; 95% CI 0.724-0.778; AUC for the combination: 0.789, 95% CI 0.763-0.814; difference between the AUC P = 0.022).
Interpretation:
Automated software-based ECG risk stratification selects patients with high risk of AF during 72 hours Holter ECG and adds predictive value to common clinical risk factors for AF prediction.
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