Reducing Unnecessary Right Ventricular Pacing with the Managed Ventricular Pacing Mode in Patients with Sinus Node Disease and AV Block
Bibliographic record
Abstract
BACKGROUND: Frequent and unnecessary right ventricular apical pacing increases the risk of atrial fibrillation or congestive heart failure. We evaluated a new pacing algorithm, managed ventricular pacing (MVP) which automatically changes modes between AAI/R and DDD/R in patients receiving pacemakers for symptomatic bradycardia. METHODS: Patients were randomized to the MVP mode or DDD/R mode for 1 month and then crossed over to the alternate pacing modality for an additional month. On completion of the crossover phase, the pacing mode selected was individualized and patients were followed for an additional 4 months. RESULTS: Of the 129 patients who successfully completed the crossover study, the cumulative percent ventricular pacing was significantly reduced in the MVP mode (median 1.4%) compared to the DDD/R mode (median 89.6%, 94.0% relative reduction; 95% CI 89.3-98.8%, P < 0.001). Patients with sinus node disease (SND, n = 51) when compared to patients with AV block (AVB) (n = 68) experienced a greater reduction in ventricular pacing with the MVP mode compared to the DDD/R mode (median relative reduction 99.1%; 95% CI 97.5-99.9% vs median relative reduction 60.1%; 95% CI 16.7-93.9% P < 0.001). The reduced percent ventricular pacing during MVP was sustained over longer term follow-up. CONCLUSIONS: The majority of patients with a bradycardia indication for cardiac pacing do not require ventricular pacing most of the time. The MVP mode significantly reduces unnecessary right ventricular pacing. This mode benefits even patients with intermittent AVB and is sustained over longer term follow-up.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| 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".