High Impedance Alerts with Pulse Generator -- Lead Mismatch
Bibliographic record
Abstract
Background: Cardiac Implantable Electronic Devices (CIED) include pulse generators and leads. In some implanting centres, it is common practice to combine devices with leads from different companies. Case series have reported episodic high-impedance changes in Boston Scientific CIEDs with competitor leads. We investigated the incidence of high-impedance abnormalities in matched vs. mismatched Boston Scientific CIEDs. Methods: Retrospective chart review identified all consecutive Boston Scientific Accolade pacemakers and Autogen implantable cardioverter defibrillators (ICD) implanted between January 2017 and June 2019 at a Canadian tertiary care centre. The primary outcome was the occurrence of transient, high-impedance changes which resulted in a switch to unipolar pacing / sensing in the absence of any other identifiable lead issue. Fisher exact tests (two-tailed, α = 0.05) were used to compare the incidence of outcomes in matched vs. mismatched systems. Results: 564 Boston Scientific CIEDs were identified associated with 969 individual leads. The primary outcome occurred with 22 leads (21 Medtronic and 1 Abbott), associated with occasional pacing inhibition, syncope, and/or early surgical revision. Mismatched systems were significantly associated with CIED malfunction compared to matched systems (3.4% vs. 0%, P = 0.0001). Median time from implant to unipolar safety switch was 19.3 months. Median follow-up time was 21.6 months. Conclusion: Use of mismatched leads with a Boston Scientific Accolade or Autogen device was associated with increased system malfunction and adverse clinical outcomes. Awareness of this interaction can allow for institution of appropriate programming remedies and may increase scrutiny of the use of mismatched CIED systems.
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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 | 0.005 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".