Effectiveness of a simple medication adjustment protocol for optimizing peri-cardioversion rate control: A derivation and validation cohort study
Bibliographic record
Abstract
Background Rate control medications are foundational in the management of persistent atrial fibrillation (AF). There are no guidelines for adjusting these medications prior to elective direct-current cardioversion (DCCV). Objective To derive and validate a preprocedural medication adjustment protocol that maintains peri-DCCV rate control and minimizes risk of postconversion bradycardia, pauses, need for pacing, and cardiopulmonary resuscitation (CPR). Methods Consecutive patients with persistent AF awaiting elective DCCV across 2 hospitals were screened for inclusion into derivation, validation, and control cohorts. In the derivation cohort, each patient taking an atrioventricular (AV) nodal blocker had medications adjusted based on heart rate (HR) 2 days before DCCV, and the magnitude of dose adjustment was compared with peri-DCCV HR. The adjustment protocol that achieved the highest percentage of optimal peri-DCCV rate control was tested prospectively in the validation cohort and compared to a standard-of-care control group. Results The optimal protocol from the derivation cohort (n = 71), based on the 2-day pre-DCCV HR, was to (1) CONTINUE AV nodal blocker for HR ≥ 100 beats per minute (bpm), (2) reduce dose by ONE increment when 80–99 bpm, (3) reduce dose by TWO increments when 60–79 bpm, and (4) HOLD when <60 bpm. In the prospective validation cohort (n = 106), this protocol improved peri-DCCV rate control (82% vs 62%, P < .001) compared to current standard of care (n = 107). There were no conversion pauses ≥5 seconds, need for pacing, or CPR post-DCCV. Conclusion This simple preprocedural medication adjustment protocol provides an effective strategy of optimizing peri-DCCV rate control in patients with AF.
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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 | 0.034 | 0.059 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".