RV Pacing Percentage
Bibliographic record
Abstract
Abstract Introduction Chronic right ventricular pacing (RVP) has been associated with dyssynchrony, leading to increased mortality. However, there have been discrepancies in previous reports in the effect of RVP levels. Objective To sub-stratify mortality risk by age for different RVP level groups within a large real-world ICD cohort. Methods Optum® de-identified electronic health records were linked to the Medtronic Carelink data to identify dual chamber ICD recipients (2007–2017). RVP level was based on median daily pacing during the first 90 days post-implant and categorized either into groups with a cutoff of 40%, or with groups of 0–9%, 10–19%, 20–29%, 30–39%, 40–49%, and 50–100%. The endpoint was death more than 90 days post-implant. Kaplan-Meier survival curves, log-rank tests, and Cox regression were used to analyze the relationship between RVP and risk of death. Results Among 14,832 ICD patients (median age 67; 74.0% male), there were 2,602 deaths within 10 years after implant. In unadjusted comparisons, high RVP (>40%) increased the risk of death relative to low RVP (≤40%) (p<0.001). This effect remained significant in older cohort (≥67 years old at implant) (p<0.001), but not in younger cohort (<67 years old) (p=0.955) (Figure). After controlling for age, gender, pacing mode, MI, SCA, HF hospitalization, diabetes, and renal dysfunction, similar or increased risk was associated with higher pacing groups relative to the 0–9% pacing group in the older cohort, but not in the younger cohort. Conclusions Our data from a large contemporaneous real-world source suggests that older age or characteristics associated with age make patients more sensitive to chronic RVP effects. These results help reconcile differences observed in prior studies. Funding Acknowledgement Type of funding source: Private company. Main funding source(s): Medtronic, Inc.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.082 | 0.037 |
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".