Ventricular arrhythmia ablation in the presence of mechanical valve utilization and complications of catheter ablation for ventricular arrhythmia in patients with mechanical prosthetic valves
Bibliographic record
Abstract
BACKGROUND: Catheter ablation (CA) for ventricular arrhythmias (VAs) is increasingly utilized in recent years. We aimed to investigate the nationwide trends in utilization and procedural complications of CA for VAs in patients with mechanical valve (MV) prosthesis. METHODS: We drew data from the US National Inpatient Sample database to identify cases of VA ablations, including premature ventricular contraction and ventricular tachycardia, in patients with MVs, between 2003 and 2015. Sociodemographic and clinical data were collected and the incidence of catheter ablation complications, mortality, and length of stay were analyzed. We compared the outcomes to a propensity-matched cohort of patients without prior valve surgery. RESULTS: The study population included a weighted total of 647 CA cases in patients with prior MVs. The annual number of ablations almost doubled, from 34 ablations on average during the "early years" (2003-2008) to 64 on average during the "late years" (2009-2015) of the study (p = .001). Length of stay at the hospital did not differ significantly between patients with MVs and 649 matched patients without prior MVs (5.4 ± 0.4, 4.7 ± 0.3 days, respectively, p = .12). The data revealed a trend toward a higher incidence of complications (12.6% vs. 7.5% respectively, p = .14) and mortality (3.7% vs. 0.7%, respectively, p = .087) among patients with MVs compared to the matched control group, not reaching statistical significance. CONCLUSION: The data show increased utilization of VA ablations in patients with MVs and a trend toward a higher incidence of in-hospital mortality and complications compared to the propensity-matched control group without MVs.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".