Lead Integrity Alert Is Useful for Assessment of Performance of Biotronik Linox Leads
Bibliographic record
Abstract
INTRODUCTION: Medtronic's Lead Integrity Alert (LIA) software algorithm is useful for detecting abnormal parameters across various ICD-lead families. However, its utility in the assessment of the Biotronik Linox™ family of high-voltage (HV) leads is unknown. METHODS: We conducted a retrospective cohort study to assess the performance of the LIA algorithm to detect abnormalities and lead failure in Linox ICD-leads. All LIA-enabled Medtronic devices connected to an active Linox lead were included. The alerts were adjudicated by 2 blinded electrophysiologists and correlated with clinical data. RESULTS: Between 2008 and 2012, data from 208 patients with 564 patient-years of follow-up were available for analysis. The median follow-up duration was 32 (IQR 21-41 months). Twenty-one LIA triggers were noted in 20 different patients. The median delay until a positive LIA was 32 months (IQR 21-41 months) postimplant with a 5-year lead survival free from LIA of 76%. Ninety-five percent (19/20) LIA alerts were true lead failures. The most common LIA triggers were short V-V intervals (85%) and nonsustained ventricular tachycardia (85%). Abrupt changes of the ICD-lead impedance occurred in 5/20 triggers. Inappropriate ICD-shocks were strongly associated with a positive LIA (30% vs. 7.4%; P = 0.006). Of the explanted Linox leads 53% had visible abnormalities. The sensitivity, specificity, and positive predictive value for lead failure in the presence of a LIA trigger were 87%, 99.5%, and 95.2%, respectively. CONCLUSIONS: A positive LIA trigger in Biotronik Linox ICD-leads is highly predictive of lead failure. LIA is useful in ongoing surveillance of lead performance.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".