Low-temperature electrocautery reduces lead-related complications: insights from the WRAP-IT study
Bibliographic record
Abstract
Abstract Background Patients with an existing cardiovascular implantable electronic device (CIED) often require a generator replacement or system upgrade/revision, during which some degree of dissection is usually necessary to free the existing lead(s). Commonly used techniques include blunt dissection, standard surgical electrocautery, or newer forms of electrocautery such as the low-temperature electrosurgical device (PlasmaBlade Soft Tissue Dissection Device) designed to minimize inadvertent thermal injury to leads. Objective Determine whether the dissection technique impacts the likelihood of developing a lead-related complication. Methods The WRAP-IT trial enrolled patients undergoing CIED replacement, upgrade, revision or de novo CRT-D implant. This analysis excluded patients undergoing a de novo procedure. All adverse events were adjudicated by an independent physician committee. Data were analyzed using Cox proportional hazard regression modeling, controlling for capsulectomies and lead dissections. Results 5639 patients (mean [±SD] age: 70.6±12.7 years; 28.8% female) underwent a replacement/upgrade/revision. Electrocautery was used in 5203 (92.3%) patients and among these, low-temperature electrocautery was used in 1866 (35.9%) patients. Compared to standard electrocautery, low-temperature electrocautery was used more often when leads were dissected or mobilized (P<0.001) or when a partial or complete capsulectomy was performed (P<0.001). Use of low-temperature electrocautery was associated with a 31% reduction in lead-related complications (HR: 0.69, 95% CI: 0.49–0.98, P=0.037) (Figure). Conclusion The low-temperature electrosurgical device (PlasmaBlade) uses precise pulses of radiofrequency energy to dissect tissue with only minimal thermal damage. In this large cohort of replacement, revision, and upgrade procedures, use of low-temperature electrocautery led to significantly fewer lead-related complications. Funding Acknowledgement Type of funding source: Private company. Main funding source(s): Medtronic
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".