High-Sensitivity Estimate of the Incidence of New-Onset Atrial Fibrillation in Critically Ill Patients
Bibliographic record
Abstract
To estimate the incidence of new-onset atrial fibrillation in critically ill patients. DESIGN: Prospective cohort. SETTING: Medical-surgical ICU. SUBJECTS: Consecutive patients without a history of atrial fibrillation but with atrial fibrillation risk factors. INTERVENTIONS: Electrocardiogram patch monitor until discharge from hospital or up to 14 days. MEASUREMENTS AND MAIN RESULTS: A total of 249 participants (median age of 71 yr [interquartile range] 64-78 yr; 35% female) completed the study protocol of which 158 (64%) were admitted to ICU for medical illness, 78 (31%) following noncardiac surgery, and 13 (5%) with trauma. Median Acute Physiology and Chronic Health Evaluation II score was 16 (interquartile range, 12-22). Median duration of patch electrocardiogram monitoring, ICU, and hospital lengths of stay were 6 (interquartile range, 3-12), 4 (interquartile range, 2-8), and 11 days (interquartile range, 5-23 d), respectively.Atrial fibrillation ≥ 30 seconds was detected by the patch in 44 participants (17.7%), and three participants (1.2%) had atrial fibrillation detected clinically after patch removal, resulting in an overall atrial fibrillation incidence of 18.9% (95% CI, 14.2-24.3%).Total duration of atrial fibrillation ranged from 53 seconds to the entire monitoring time. The proportion of participants with ≥1 episode(s) of ≥6 minute, ≥1 hour, ≥12 hour and ≥24 hour duration was 14.8%, 13.2%, 7.0%, and 5.3%, respectively. The clinical team recognized only 70% of atrial fibrillation cases that were detected by the electrocardiogram patch. CONCLUSIONS: Among patients admitted to an ICU, the incidence of new-onset atrial fibrillation is approximately one in five, although approximately one-third of cases are not recognized by the clinical team.
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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 | 0.008 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".