The effect of electronic repositioning on left ventricular pacing and phrenic nerve stimulation
Bibliographic record
Abstract
AIMS: Cardiac resynchronization therapy (CRT) improves survival and reduces heart failure symptoms. However, phrenic nerve stimulation and high pacing thresholds are common problems that limit CRT effectiveness. Current technology allows reprogramming of left ventricular (LV) pacing vectors, permitting 'electronic repositioning' to overcome both phrenic nerve stimulation and high pacing output without the need for re-operation. METHODS AND RESULTS: Patients underwent prospective evaluation of a CRT system implantation with a bipolar LV. Optimal LV threshold and avoidance of phrenic nerve stimulation were determined at baseline and at 6 months. A subset of 48 patients underwent more detailed evaluation of pacing threshold and phrenic nerve stimulation at baseline and at 6 months. Between 2004 and 2007, 228 patients underwent CRT implantation (64 CRT pacemakers, 164 CRT defibrillators). At baseline, electronic reprogramming to determine an alternate configuration compared with standard LVtip to LVring found a ≥ 1.0 V reduction in pacing threshold in 80 patients (35%). Of the 17 patients who had an LVtip to LVring configuration and high pacing threshold (>5.0 V), 16 could be reduced by >1.0 V (94%) and 11 could be reduced by >2.0 V through electronic repositioning alone without repositioning the lead (65%). At implant, there were 48 patients with phrenic nerve stimulation at less than maximum pacing output (21%) using the standard LVtip to LVring configuration. In 37 cases (77%), there was at least one other configuration with no phrenic nerve stimulation, which prevented the need for lead revision. CONCLUSIONS: Electronic repositioning is an important tool in the management of CRT patients which may help to lower thresholds, avoid phrenic nerve stimulation, and prevent unnecessary re-operations for LV lead repositioning.
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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) | 0.000 | 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".