Timing of detection of atrial fibrillation recurrence in patients with transient new-onset atrial fibrillation detected during hospitalization for noncardiac surgery or medical illness
Bibliographic record
Abstract
Abstract Background Approximately one third of patients who experience transient new-onset atrial fibrillation (AF) during a hospitalization for noncardiac surgery or medical illness will have recurrent AF within 1 year. Purpose To estimate the expected rate of detection of AF recurrence using different durations of continuous ECG monitoring during follow-up of patients who have had AF occurring transiently during physiologic stress. Methods We used continuous ECG data from participants enrolled in the AFOTS Follow-up Study. Participants had transient new-onset AF while hospitalized for noncardiac surgery or medical illness and were discharged from hospital in sinus rhythm. We calculated the median time to detection of the first recurrent AF episode and the proportion of recurrent AF events that would be captured with an ECG monitor worn for 24 h, 48 h, 7 days and 14 days, and with a repeated 14-day monitor worn approximately 6 months after the first monitor. Results A total of 139 participants (41.0% female, median CHA2DS2-VaSC Score 3.0 (Interquartile Range [IQR] 2.0- 4.0)) wore at least one ECG monitor for a median of 13.5 days (IQR 11.7-13.8). A second ECG monitor was worn by 83 (59.7%) participants for a median of 12.9 days (IQR 10.0-13.9); the second monitor was worn a median of 175.0 days (IQR 153.0-216.0) after the first monitor. Among the study participants in whom recurrent AF was detected (n = 40, 28.8%), the median time to AF lasting at least 30 seconds was 128.0 hours (IQR 32.5-232.5). The proportion of AF recurrences detected after more than 24 hours of monitoring was 75.0% (n = 30). The yield of AF detection increased with longer monitoring times (Figure 1). AF was detected in 5.0% of participants at 24 hours, 5.8% at 48 hours, 6.5% at 72 hours, 12.2% at 7 days, 21.6% at 14 days and in 28.8% of participants by the end of the second 14-day monitor. Conclusions In patients who have experienced transient new-onset AF during a hospitalization for noncardiac surgery or medical illness, the rate of capture of AF recurrence in follow-up increases with longer monitoring durations. 14-day monitors capture nearly 4 times as many AF events as 48 h monitors.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".