Sprint Fidelis Lead Fractures in Patients With Cardiac Resynchronization Therapy Devices
Bibliographic record
Abstract
BACKGROUND: Using data from the Resynchronization/Defibrillation for Ambulatory Heart Failure (RAFT) study, we examined whether Fidelis lead failure was more common in patients with implantable cardioverter-defibrillators (ICDs) and cardiac resynchronization therapy (ICD-CRT) than in patients with an ICD only. METHODS AND RESULTS: All cases of patients who had a right ventricular defibrillation lead revision in the RAFT study were adjudicated for the presence of lead fracture. Criteria for fracture were at least 2 of the following: (1) Impedance rise (>50% or >500 Ω in 1 week), (2) short interval count >10 times per day or 300 times per month, or (3) inappropriate shock caused by noise, verified by stored electrogram. A total of 1798 patients were enrolled into the RAFT study, with a mean follow-up of 40±20 months. There were 818 patients (45.5%) who received a Fidelis lead at the original implantation, 405 with an ICD only and 413 with an ICD-CRT. There were 47 confirmed defibrillation lead fractures; 45 were Fidelis leads (5.5% of Fidelis leads). The overall rate of fracture in the ICD group was 3.2% compared with 7.8% in the ICD-CRT group (P=0.006; hazard ratio, 2.42; 95% confidence interval, 1.27-4.61). Significant correlates of lead fracture in this population were undergoing an ICD-CRT implantation and having ≥2 leads. CONCLUSIONS: In this analysis of the RAFT study, patients with an ICD-CRT were found to have a significantly higher fracture rate than patients with an ICD. This finding needs to be considered when these patients are assessed for possible lead revision at the time of an elective 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".