Anesthesia for subcutaneous implantable cardioverter‐defibrillator implantation: Perspectives from the clinical experience of a U.S. panel of physicians
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Worldwide adoption of the subcutaneous implantable cardioverter-defibrillator (S-ICD) for preventing sudden cardiac death continues to increase, as longer-term evidence demonstrating the safety and efficacy of the S-ICD expands. As a relatively new technology, comprehensive anesthesia guidance for the management of patients undergoing S-ICD placement is lacking. This article presents advantages and disadvantages of different periprocedural sedation and anesthesia options for S-ICD implants including general anesthesia, monitored anesthesia care, regional anesthesia, and nonanesthesia personnel administered sedation and analgesia. METHODS: Guidance, for approaches to anesthesia care during S-ICD implantation, is presented based upon literature review and consensus of a panel of high-volume S-ICD implanters, a regional anesthesiologist, and a cardiothoracic anesthesiologist with significant S-ICD experience. The panel developed suggested actions for perioperative sedation, anesthesia, surgical practices, and a decision algorithm for S-ICD implantation. CONCLUSIONS: While S-ICD implantation currently requires higher sedation than transvenous ICD systems, the panel consensus is that general anesthesia is not required or is obligatory for the majority of patients for the experienced S-ICD implanter. The focus of the implanting physician and the anesthesia services should be to maximize patient comfort and take into consideration patient-specific comorbidities, with a low threshold to consult the anesthesiology team.
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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 | 0.001 |
| 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".