Clinical and radiographic predictors of cardiovascular implantable electronic device lead failure at the time of initial implantation
Bibliographic record
Abstract
Abstract Objective To assess the clinical and radiographic factors associated with lead failure by comparing subjects with lead failure within 10 years of implantation with an implant‐year‐matched group without lead failure. Methods A case‐control study with 49 subjects who received Cardiac Implantable Electronic Device (CIED) between January 1, 1999 and July 31, 2008 and developed lead failure within 10 years of implantation in a single center. The control group consisted of subjects (n = 54) with normally functioning leads matched one‐to‐one by implant year. Results Among the failure group, the meantime from implantation to device lead failure was 4.70 ± 2.94 years. Older age at implantation was associated with a lower likelihood of lead failure (Odds Ratio (OR) = 0.28 (75 vs 42 years old), 95% CI 0.12‐0.63, P = .002). A larger smallest loop diameter on the chest radiograph was also associated with a lower likelihood of lead failure (OR = 0.51 (31 vs 14 mm), 95% CI 0.27‐0.97, P = .04). CIED type (defibrillator vs pacemaker) and Ottawa scores were not significantly associated with lead failure. Among lead‐specific parameters, defibrillation lead vs pace‐sense lead was associated with lead failure (OR = 3.91, 95% CI 1.95‐7.81, P < .001). Conclusions Younger age, defibrillation leads, and small lead loops are associated with lead failure in CIEDs. Techniques to avoid tight loops in the pocket could potentially reduce the risk of lead failure and bear important implications for the implanting physician.
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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 | 0.001 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".