Abstract 79: 30-Day Cardiac Monitoring is Feasible and Effective in a Community Setting
Bibliographic record
Abstract
Prolonged cardiac rhythm monitoring reveals atrial fibrillation (AF) in 16-42% of patients who undergo monitoring for up to 6 months after a cryptogenic stroke. The feasibility of prolonged cardiac monitoring, AF detection rates and the impact of earlier screening after suspected cardioembolic infarct in a community setting is unknown. Methods: This prospective study examined patient compliance and AF detection rates with prolonged cardiac monitoring started within 30 days of a stroke/TIA. 181 patients ≥ 55 years old with a suspected cardioembolic stroke/TIA that occurred in the preceding 30 days were referred for 30-day cardiac monitoring. Patients were educated about reasons for monitoring and test procedures for 15-20 minutes. A Spiderflash-t or Northeast DR 200 monitor, programmed for detecting AF, was used. Auto-triggered and patient-activated recordings obtained during two consecutive 15-day monitoring periods were assessed. Either 3-day serial ECG or ≥24h Holter monitor was also completed in 119/181 patients. The primary outcome was patient compliance with 30 days of monitoring. Secondary outcomes included detection of any duration of AF within the two monitoring periods. Results: The compliance rate for the full 30-day monitoring period was 98.9% (179/181). AF was detected on 30-day monitor in 11.6% (21/181) of patients. Of the patients who received both 3-day serial ECG or ≥24h Holter and 30-day monitoring, AF was only detected in 0.84% (1/119) with 3-day serial ECG and/or Holter monitoring vs. 14.3% (17/119) with 30-day monitoring. AF was detected in 76.2% (16/21) of patients in the first 15 day period. 14 patients had AF lasting longer than 6 minutes, 5 had AF lasting between 30 secs - 6 min and 2 had AF < 30 secs. Of patients who completed the 30-day monitoring, 38.8% had AF recurring through the 30 days. 85.7% (18/21) of patients were switched to a novel oral anticoagulant (NOAC), while 3 patients died and thus could not be switched to a NOAC. In conclusion, 30-day cardiac monitoring is feasible in a community setting with high compliance rates, yields similar AF detection rates as seen in clinical trials and positively impacts patient management. There are missed opportunities for secondary stroke prevention in the community without 30-day monitoring.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.011 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".