Utilization and complications of catheter ablation for ventricular arrhythmias in patients with mechanical valves
Bibliographic record
Abstract
Abstract Background Catheter ablation (CA) for ventricular arrhythmias (VAs) is increasingly utilized in the recent years. Ablation for VAs in patients with mechanical valves (MVs), can be challenging due to the chronic anticoagulation therapy, limitations in accessing the cardiac chambers, the risk for entrapment of mapping or ablation catheters between the leaflets of MVs and more. Purpose To investigate the nationwide trends in utilization and complications of CA for VAs in patients with prior MVs. Methods We drew data from the US National Inpatient Sample database to identify cases of VA ablations, including premature ventricular contraction (PVC) and ventricular tachycardia (VT) ablations, in patients with MVs between 2003 and 2015. Sociodemographic and clinical data were collected, and 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 annual ablation procedures on average during the “late years” (2009–2015) of the study (p=0.001). 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=0.12). The incidence of complications was higher among patients with and without MVs (12.6% vs. 7.5% respectively, p=0.14), however, not reaching statistical significance. Moreover, the data revealed a trend toward higher mortality among patients with MVs undergoing CA compared to matched control patients without MVs (3.7% vs. 0.7% respectively, p=0.087). Conclusion The utilization of catheter ablations for ventricular arrhythmias in patients with mechanical valves increased substantially over the years. The data show a trend towards increased incidence of morality and complications in the study population, requiring further investigation in larger population cohort. Funding Acknowledgement Type of funding sources: Public hospital(s). Main funding source(s): Padeh Medical Center Research Fund
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 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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".