74Subclinical atrial fibrillation before and after acute medical illness or noncardiac surgery: insights from ASSERT
Bibliographic record
Abstract
Abstract OnBehalf ASSERT Investigators Background Atrial fibrillation (AF) is frequently detected perioperatively or during acute medical illness. It is unclear if such AF is reversible and unlikely to recur, or is a manifestation of paroxysmal AF. Objective To compare the prevalence of pacemaker-detected, subclinical AF (atrial rate >190 bpm) before and after hospitalization for noncardiac surgery or medical illness in patients without a history of clinical AF. Methods ASSERT enrolled patients who were >65 years old and had hypertension but no known AF. Pacemakers and defibrillators recorded episodes of subclinical AF. We identified participants who were hospitalized for noncardiac surgery or medical illness, and created heart rhythm profiles, centred on the day of hospitalization. We compared the prevalence of subclinical AF before and after hospitalization. We blanked the 30 days before hospitalization, because of uncertainty in defining the precise onset of illness. Results Among 2580 patients, 436 had a documented surgical or medical hospitalization. In the 30 days following a first hospitalization, 43 patients (9.9%) had >1 episode of >6 minutes of subclinical AF; 20 (4.6%) had >6 hours and 13 (3%) had >24 hours. A higher proportion of patients had >1 episode of subclinical AF >6 minutes in the 30 days following a first surgical or medical hospitalization, as compared to the period between 30 and 60 days before hospitalization (9.9% versus 4.4%, P < 0.001). There was no significant difference when comparing 0-90 days after hospitalization to 30-120 days before (13.7% versus 10.6%, P = 0.1). Similar results were observed for the same comparisons with episodes >6 hours (4.6% versus 2.3%, P = 0.03 and 5.9% versus 5.6%, P = 0.8, respectively). The majority of patients with subclinical AF in the 30 days following hospitalization had at least one episode of subclinical AF of the same duration in the 6 months prior (50% for episodes >6 minutes; 69% for >6 hours and 60% for >24 hours). Those who did have subclinical AF in the 30 days following hospitalization were more likely to have had subclinical AF in the past 6 months than those who did not (OR 7.2 95%CI 3.2-15.8 for episodes >6 minutes; OR 32.6, 95%CI 10.3-103.4 for >6 hours and OR 36.3 95%CI 9.0-146.0 for >24 hours). Conclusions The prevalence of subclinical AF increased following hospitalization for noncardiac surgery or medical illness. However, most patients with subclinical AF following hospitalization had previously experienced similar episodes, particularly those with longer episodes of subclinical 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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 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".