Prevalence and management of electrical lead abnormalities in cardiac implantable electronic device leads
Bibliographic record
Abstract
Background Electrical lead abnormalities (ELAs) can result in device malfunction, leading to significant morbidity in patients with cardiac implantable electronic devices (CIEDs). Objective We sought to determine the prevalence and management of ELAs in patients with CIEDs. Methods This was a retrospective cohort study of patients implanted with a CIED between 2012 and 2019 at a tertiary care center. The primary outcome was ELA defined as increased capture threshold (≥2× implantation value), decreased sensing (≤0.5 implantation value), change in impedance (>50% over 3 months), or nonphysiologic potentials. A secondary outcome of device clinic utilization was also collected. Results There were 2996 unique patients (35% female) included with 4600 leads (57% Abbott, 43% Medtronic). ELAs were observed in 135 (3%) leads, including 124 (92%) Abbott and 10 (7%) Medtronic leads (hazard ratio 9.25, P < .001). Mean follow-up was 4.5 ± 2.2 years. ELAs were associated smaller lead French size, atrial location, and Abbott leads. Lead revision was required in 28% of cases. Patients with lead abnormalities had 38% more in-clinic visits per patient year of follow-up compared with those without ( P < .001). Conclusion ELAs were more frequent in certain models, which increased rates of revision and follow-up. Identification of factors that mitigate these abnormalities to improve lead performance are required to improve care for these devices and provide efficient healthcare.
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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 | 0.006 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".