Comprehensive analysis of substernal lead removal: experience from EV ICD Pilot, Pivotal, and Continued Access Studies
Bibliographic record
Abstract
AIMS: The extravascular implantable cardioverter-defibrillator (EV ICD) has been shown to be safe and effective for patients at risk of sudden cardiac death, but little is known about EV ICD lead removal in humans. This analysis aimed to characterize the EV ICD lead removal experience thus far. METHODS AND RESULTS: This was a retrospective analysis of lead removals from the EV ICD Pilot, Pivotal, and Continued Access Studies. Patients with a successful EV ICD implant who underwent lead removal were included. The main objective was lead removal success. Ancillary objectives included characterizing technique used, procedure complications, and reimplantation status. An EV ICD system was successfully implanted in 347 patients across the 3 studies (25.9% female; 53.4 ± 13.3 years; left ventricular ejection fraction: 39.7 ± 15.9). Of these patients, 29 (8.4%) underwent lead removal with a mean lead dwell time of 12.6 ± 14.3 months (0.2-58.4). The main reason for lead removal was lead dislodgement (n = 9, 31.0%). Lead removal was successful in 27/29 (93.1%) cases [100% (19/19) success rate <1 year and 80% (8/10) success rate >1 year post-implant]. Simple traction was used in 22/26 (84.6%) and extraction tools in 4/26 (15.4%) successful cases where technique was known. No complications were reported for any of the removal procedures. All 11 EV ICD reimplant attempts were successful. CONCLUSION: Complete removal of the EV ICD lead was successful in 93.1% of cases, and simple traction was sufficient in most instances. Based on these results, lead removal from the substernal space was safe and achievable up to 3 years post-implant.
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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".