Safety of Lead Repair Compared to Lead Revision for Visible Lead Insulation Defects in Patients With Cardiac Implantable Electronic Devices
Bibliographic record
Abstract
BackgroundCardiac implantable electronic devices deliver life-sustaining therapy and may be prone to hardware degeneration over time. Functioning transvenous endocardial leads with visible insulation breaks are amenable to lead revision (LRV) or lead repair (LRP), with medical adhesive. The latter is a less invasive and more cost-effective strategy. However, data are sparse on the overall safety of such an approach.MethodsThis is a retrospective cohort study of patients with lead insulation defects managed by either LRV or LRP with medical adhesive. The data analyzed were from January 2010 to January 2021. All-cause mortality, and both early and late complications, was ascertained for all cases.ResultsA total of 57 cases were identified, with a mean age (standard deviation) of 75 (±11.8) years; 18 (31.6%) were women. A total of 35 patients (62.5%) underwent LRV for an insulation defect, and 21 (37.5%) underwent LRP. There was no statistical difference in the rate of early and late complications between the 2 groups over a mean follow-up period of 1.15 (±0.78) years [3 (8%)] LRV vs 1 (5%) LRP, P = 0.88). One death was identified in each group, unrelated to either the device or a device-related procedure. There was no association between device type and the likelihood of LRP vs LRV as an attempted strategy (χ2 = 2.25, P = 0.53).ConclusionsThe results of this study suggest that the use of a lead-repair strategy, with silicone adhesive glue and an anchoring sleeve, is not associated with an increased rate of early or late complications, compared with lead revision in the management of visible lead insulation defects with stable lead function.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".