ArticleAnnals of clinical and translational neurology2020
Software-based analysis of 1-hour Holter ECG to select for prolonged ECG monitoring after stroke.
Article in Annals of clinical and translational neurology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed, 5 citations in OpenAlex.
- Artificial intelligence for prediction of atrial fibrillation in the stroke unit: a retrospective derivation validation cohort study.EBioMedicine · 2025Article
- The diagnostic efficiency of artificial intelligence based 2 hours Holter monitoring in premature ventricular and supraventricular contractions detection.Clinical cardiology · 2024Article
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveIdentification 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.
methodsIn 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.
resultspAF 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).
interpretationAutomated 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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