QTc Dynamics Following Cardioversion for Persistent Atrial Fibrillation
Bibliographic record
Abstract
Background Cardioversion (CV) for atrial fibrillation (AF) is common. We aimed to assess changes in QTc over time following electrical CV (ECV) for persistent AF, and to compare the benefit of using continuous Holter monitoring vs. conventional follow-up by ECG. Methods Prospective observational cohort study. We comprised 90 patients admitted to our center for elective ECV due to persistent AF who were prospectively enrolled from July 2017 to August 2018. All patients underwent 7-days Holter started prior to ECV. Baseline QTc was defined as median QTc during 1 h post ECV. The primary endpoint was QTc prolongation defined as QTc ≥500 ms, or ≥10% increase (if baseline QTc was >480 ms). Conventional monitoring was defined as 2-h ECG post ECV. Results Mean age was 67 ± 11 years and 61% were male. Median baseline QTc was 452 ms (IQ range: 431–479 ms) as compared with a maximal median QTc of 474 ms (IQ range: 433–527 ms; p <0.001 for the change in QTc from baseline). Peak median QTc occurred 44 h post ECV. The primary endpoint was met in 3 patients (3%) using conventional monitoring, compared with 39 new patients (43%) using Holter ( p <0.001 for comparison). The Holter monitoring was superior to conventional monitoring in detecting clinically significant QTc prolongation (OR = 13; p <0.001). Conclusions ECV of patients with persistent AF was associated with increased transient risk of QTc prolongation in nearly half of the patients. Peak median QTc occurs during end of second day following ECV and prolonged ECG monitoring provides superior detection of significant QTc prolongation compared with conventional monitoring.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| 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".