Current of Injury Predicts Acute Performance of Catheter‐Delivered Active Fixation Pacing Leads
Bibliographic record
Abstract
BACKGROUND: During pacemaker lead (PPML) implantation, the implanter must assess lead stability (fixation) and pacing threshold adequacy. Implanters rely principally on lead impedance (IMP) and pacing threshold measurements after fixation of the PPML to determine adequacy of pacing sites. Continuously monitoring lead parameters during fixation might better identify predictors of acute lead stability and performance. METHODS: At the time of PPML implantation with a catheter delivered, fixed screw, 4-Fr PPML (Medtronic 3830, Minneapolis, MN, USA) patients underwent measurements of R-wave amplitude, slew rate, and current of injury (COI) (maximum and at 80 ms) during each turn of the helix. Lead stability was tested with traction applied to the lead body. RESULTS: Eighteen patients (age 70 +/- 9 years, 9 males) were studied. Right ventricular lead positioning was attempted 43 times; 26 positions demonstrated good fixation and 18 had satisfactory threshold. Sites of good fixation consistently showed larger COI (maximum and at 80 ms) compared to poor fixation sites throughout each turn of the helix; R wave, slew rate, and IMP did not differ significantly. When all measures of COI were examined in a stepwise regression model only the final measure of COI at 80 ms proved significantly associated with acute stability (P = 0.032). CONCLUSIONS: Lead stability and threshold adequacy are predictable from assessment of the magnitude of injury current. Continuous monitoring of lead parameters during fixation does not appear to confer any benefit over assessment of the parameters after final rotation of the lead. A negative COI is associated with poor threshold and/or fixation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".