Trends, indications and outcomes of cardiac implantable device system extraction: a single UK centre experience over the last decade
Bibliographic record
Abstract
BACKGROUND: The rising number of device implantation has seen a parallel in the rising numbers of lead extraction. Herein we have analysed our experience in cardiac device and lead extraction in a single tertiary centre over the last decade. METHOD: Retrospective analysis of all consecutive patients undergoing lead extractions performed between 2001 and 2010. Procedural success and complications as defined by the Heart Rhythm Society policy. RESULTS: A total of 745 leads were extracted with a procedural success of 98.9% [382 cases; partial success in 6.9% (26) cases] and failure in 1.1% (4). Major complication rate was 1% (four cases) and minor complication rate was 3.6%. By both univariate and multivariate analysis only duration of lead implantation was an indicator for success (p < 0.0001). The mean implantation time for failed lead extraction was 203 ± 64 months compared with 71.8 ± 16.5 months in the successful cohort (p < 0.0001). Laser-assisted extraction was required in 176 cases. With regard to extraction indication, lead malfunction/recall showed a significant increase during the study period (p = 0.03). On time trend analysis the rise in coronary sinus (CS) lead extraction over time was significant. (p = 0.02) Despite a trend for increased laser use over time this did not achieve statistical significance, p = 0.06. CONCLUSIONS: A decade's experience of percutaneous lead extraction suggests that a high procedural success rate with a low complication rate is achieved in a high-volume centre. During this time, an increase in both defibrillator and CS lead explantation and a rising trend in laser assistance with almost 50% of cases needing laser usage were observed.
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 | 0.001 | 0.006 |
| 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 | 0.001 |
| 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".