Laser Lead Extraction in Adult Congenital Heart Disease
Bibliographic record
Abstract
BACKGROUND: In adults with congenital heart disease (ACHD), lead extraction procedures are expected to parallel increasing transvenous pacemaker and defibrillator implantations. We sought to assess the safety and feasibility of laser lead extraction in ACHD. METHODS AND RESULTS: All laser lead extractions (Spectranectics, Colorado Springs, CO, USA) performed at the Montreal Heart Institute between September 2000 and August 2005 were prospectively registered. Efficacy and complications in patients with ACHD were compared to the larger cohort. Laser lead extraction was attempted on 270 leads in 175 patients. In ACHD, 23 (five atrial, 15 ventricular pacing, and three defibrillator) leads were targeted in 16 patients. Indications were: infection 44%, dysfunction 25%, upgrade 25%, and pain 6%. Patients with ACHD were younger (43.0 +/- 13.5 vs 63.7 +/- 14.7 years, P < 0.0001) and had a higher proportion of active fixation leads (74% vs 37%, P = 0.0013). Lead age in patients with and without ACHD was 9.0 +/- 5.2 vs 7.7 +/- 5.2 years (P = 0.2713). Overall, 21 of 23 leads (91%) were successfully extracted in ACHD compared with 220 of 247 leads (89%) (P = 0.7405). One major complication (6.3%) occurred in ACHD (tricuspid valve laceration) compared with five major (3.0%) and eight minor (5.0%) complications in patients without ACHD. Presence of ACHD did not modulate procedural success (OR 1.3, 95% CI [0.3, 5.8]) or complications (OR 1.0, 95% CI [0.2, 4.4]). Median procedural time was 27 minutes longer in ACHD (127 vs 100 minutes, P = 0.0595). CONCLUSION: In selected patients with ACHD, laser lead extraction may be performed with a safety and efficacy profile comparable to patients without ACHD.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".