Clinical Predictors of Fidelis Lead Failure
Bibliographic record
Abstract
BACKGROUND: Approximately 268,000 Fidelis leads were implanted worldwide until distribution was suspended because of a high rate of early failure. Careful analyses of predictors of increased lead failure hazard are required to help direct future lead design and also to inform decision making on lead replacement. We sought to perform a comprehensive analysis of all potential predictors in a multicenter study. METHODS AND RESULTS: A total of 3169 Sprint Fidelis leads were implanted in 11 centers with a total of 251 failures. Lead failure rates at 3, 4, and 5 years were 5.3%, 10.6%, and 16.8%, respectively. The rate of lead failure continues to accelerate (P<0.001). There were 4 independent predictors of failure: center, sex, access vein, and previous lead failure. Women had a higher hazard of failure (hazard ratio 1.51; 95% confidence interval, 1.14-2.04; P=0.005). Both axillary and subclavian access increased the hazard of failure (P=0.007); hazard ratio for axillary was 1.94, (95% confidence interval, 1.23-3.04) and for subclavian 1.63 (95% confidence interval, 1.08-2.46). Previous lead failure increased the hazard of a subsequent Fidelis failure with a hazard ratio of 3.12 (95% confidence interval, 1.80-5.41; P<0.001). CONCLUSIONS: The rate of Fidelis failure continues to increase over time, with failures approaching 17% at 5 years. Women, patients with leads inserted via the subclavian or axillary vein, and those with a previous lead fracture were at greatest risk of Fidelis failure. Our data suggest that Fidelis replacement should be strongly considered at the time of generator replacement.
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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 | 0.008 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".