Serial Electrocardiographic Assessments Significantly Improve Detection of Atrial Fibrillation 2.6-Fold in Patients With Acute Stroke
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Previous studies have reported a low, approximately 1% to 3%, rate of detection of occult atrial fibrillation (AF) with Holter monitor in patients with acute stroke. Furthermore, at least one study has reported that Holter monitoring could not always corroborate initial electrocardiographic (ECG) detection of AF suggesting underestimation of AF by Holter. We compare the detection of new-onset AF by serial ECG assessments and Holter after acute ischemic stroke. METHODS: One hundred forty-four patients with ischemic stroke admitted to a stroke unit were studied. The number of ECGs conducted within the first 3 days up to the detection of AF as well as the time interval for Holter "hookup" and subsequent reporting of AF was documented. RESULTS: ECGs were performed in 143 patients with a baseline of 10 (7%) patients having a history of AF. Serial ECGs detected 15 new AF cases in <2 days of admission, thereby increasing the total number of known AF cases to 25 (17.5%), a 2.6-fold increased realization of AF (P=0.011). Holter was also completed in 12 of 15 new cases of AF but surprisingly identified AF in only 50% (6 of 12). Holter monitoring was performed in 126 cases and in this subgroup, there was no statistically significant difference in the rate of AF detection with ECG or Holter. CONCLUSIONS: Serial ECG assessments within the first 72 hours of an acute stroke significantly improve detection of AF. The discordance regarding the corroboration of AF by Holter in ECG-positive patients with AF supports previous observations and suggests a high incidence of paroxysmal AF as a cause of ischemic stroke.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".