Anesthetic management of transcatheter aortic valve implantation
Bibliographic record
Abstract
PURPOSE OF REVIEW: The revolution in transcatheter aortic valve implantation (TAVI) for the treatment of aortic stenosis has been well described by the large number of randomized trials, registries, and single and multicenter experiences published during 2010-2011. The aim of this review is to describe the challenges of the anesthetic management related to TAVI. RECENT FINDINGS: Recent data show that TAVI is clinically effective in patients with inoperable aortic stenosis when compared with standard therapy. It can be accomplished in high-risk patients with favorable outcomes compared with surgery as predicted by standard estimates of mortality and is associated with functional and hemodynamic improvement. Currently, TAVI is targeted at high-risk patients, but may be extended to lower risk groups in the near future. Outstanding questions concerning TAVI are related to its long-term durability and to procedural complications. SUMMARY: Preprocedural, multidisciplinary assessment of the patient is essential prior to TAVI and should include a full anesthetic evaluation, consideration of patient comorbidities, and determination of technical feasibility. The role of scoring systems for risk prediction requires further scrutiny. Multidevice/multiple access approaches allow for treatment of a wide range of patients. Anesthetic techniques and supportive measures vary depending on procedural concerns, patient comorbidity, and severe, often unstable cardiac disease. Echocardiography is fundamental to preoperative evaluation, procedure guidance, and assessment of complications. Planned bailout strategies should be discussed with all members of the medical team. Postoperative standardized monitoring and management protocols are essential.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".