Apical versus Non‐Apical Lead: Is ICD Lead Position Important for Successful Defibrillation?
Bibliographic record
Abstract
INTRODUCTION: We aim to compare the acute and long-term success of defibrillation between non-apical and apical ICD lead position. METHODS AND RESULTS: The position of the ventricular lead was recorded by the implanting physician for 2,475 of 2,500 subjects in the Shockless IMPLant Evaluation (SIMPLE) trial, and subjects were grouped accordingly as non-apical or apical. The success of intra-operative defibrillation testing and of subsequent clinical shocks were compared. Propensity scoring was used to adjust for the impact of differences in baseline variables between these groups. There were 541 leads that were implanted at a non-apical position (21.9%). Patients implanted with a non-apical lead had a higher rate of secondary prevention indication. Non-apical location resulted in a lower mean R-wave amplitude (14.0 vs. 15.2, P < 0.001), lower mean pacing impedance (662 ohm vs. 728 ohm, P < 0.001), and higher mean pacing threshold (0.70 V vs. 0.66 V, P = 0.01). Single-coil leads and cardiac resynchronization devices were used more often in non-apical implants. The success of intra-operative defibrillation was similar between propensity score matched groups (89%). Over a mean follow-up of 3 years, there were no significant differences in the yearly rates of appropriate shock (5.5% vs. 5.4%, P = 0.98), failed appropriate first shock (0.9% vs. 1.0%, P = 0.66), or the composite of failed shock or arrhythmic death (2.8% vs. 2.3% P = 0.35) according to lead location. CONCLUSION: We did not detect any reduction in the ICD efficacy at the time of implant or during follow-up in patients receiving a non-apical RV lead.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".