Underutilization of Ambulatory ECG Monitoring After Stroke and Transient Ischemic Attack
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Detection and treatment of atrial fibrillation is a major goal in secondary stroke prevention. Guidelines recommend at least 24 hours of ECG monitoring after stroke. However, it is unclear how often this is done in routine practice. METHODS: In this longitudinal cohort study using data from the Ontario Stroke Registry, we analyzed consecutive patients presenting to designated stroke centers in Ontario, Canada (2003-2013) with a first acute ischemic stroke or transient ischemic attack (TIA) in sinus rhythm and without known atrial fibrillation. The primary outcome was the proportion of patients who received at least 24-hour Holter monitoring within 30 days after stroke/TIA. Secondary analyses assessed total duration of ECG monitoring completed within 90 days after stroke/TIA, temporal trends in monitoring use, and use of Holter monitoring relative to echocardiography. RESULTS: Among 17 398 consecutive eligible patients (mean age 68.8±14.3 years), 30.6% had at least 24 hours of Holter monitoring within 30 days after stroke/TIA. Less than 1% of patients received prolonged monitoring beyond 48 hours. The median time to start monitoring was 9 days poststroke (interquartile range 3-25). Stroke/TIA patients were nearly twice as likely to receive an echocardiogram than a Holter monitor within 90 days (odds ratio 1.8, 95% confidence interval 1.67-2.01). CONCLUSIONS: Less than one third of patients in our cohort received guideline-recommended 24-hour Holter monitoring, and <1% received prolonged ambulatory ECG monitoring. These findings highlight a modifiable evidence-practice gap that likely contributes to an overdiagnosis of strokes as cryptogenic, an underdiagnosis of atrial fibrillation, and missed anticoagulant treatment opportunities for secondary stroke prevention.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| 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".