Long-Term Follow-up of Enhanced Holter-Electrocardiography Monitoring in Acute Ischemic Stroke
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Prolonged electrocardiography (ECG)-monitoring in stroke patients improves the detection of paroxysmal atrial fibrillation (pAF). However, most randomized studies only had short follow-up. We aimed to provide 3-year follow-up data for AF detection and stroke recurrence risk. METHODS: We randomized 402 patients aged ≥60 years with acute ischemic strokes without AF to either enhanced and prolonged monitoring (EPM; 3×10-day Holter-ECG-monitoring) or standard-of-care (≥24 hours ECG-monitoring). The endpoint of the current analysis was AF within 36 months analyzed by intention to treat. Long-term follow-up was performed for 36 months. RESULTS: Two hundred and seventy-four patients (80%) participated in the extended follow-up (median duration of follow-up was 36 months [interquartile range, 12 to 36]). During the first 6 months, more AF was documented in the EPM arm compared to the control arm (13.5% vs. 5.1%; 95% confidence interval, 2.9% to 14.4%; P=0.004). During months 6 to 36, AF was less detected in the EPM intervention arm than in the control arm (2.0% vs. 7.3%; 95% confidence interval, 0.7% to 9.9%; P=0.028). Overall, the detection rate of AF within 36 months was numerically higher within the EPM group (15.0% vs. 11.1%, P=0.30). Numerically less patients in the EPM arm had recurrent ischemic strokes (5.5% vs. 9.1%, P=0.18), transient ischemic attacks (3.0% vs. 4.5%, P=0.44) or died (4.5% vs. 6.6%, P=0.37). CONCLUSIONS: Enhanced and prolonged ECG monitoring increased AF detection during the first six months, but there was significantly more clinical AF during months 6 to 36 observed in the usual-care arm. This suggests that EPM leads to an earlier detection of clinically relevant AF.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".