Local versus general anesthesia for transcatheter aortic valve implantation (TAVR) – systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: The hypothesis of this study was that local anesthesia with monitored anesthesia care (MAC) is not harmful in comparison to general anesthesia (GA) for patients undergoing Transcatheter Aortic Valve Implantation (TAVR).TAVR is a rapidly spreading treatment option for severe aortic valve stenosis. Traditionally, in most centers, this procedure is done under GA, but more recently procedures with MAC have been reported. METHODS: This is a systematic review and meta-analysis comparing MAC versus GA in patients undergoing transfemoral TAVR. Trials were identified through a literature search covering publications from 1 January 2005 through 31 January 2013. The main outcomes of interest of this literature meta-analysis were 30-day overall mortality, cardiac-/procedure-related mortality, stroke, myocardial infarction, sepsis, acute kidney injury, procedure time and duration of hospital stay. A random effects model was used to calculate the pooled relative risks (RR) with 95% confidence intervals. RESULTS: Seven observational studies and a total of 1,542 patients were included in this analysis. None of the studies were randomized. Compared to GA, MAC was associated with a shorter hospital stay (-3.0 days (-5.0 to -1.0); P = 0.004) and a shorter procedure time (MD -36.3 minutes (-58.0 to -15.0 minutes); P <0.001). Overall 30-day mortality was not significantly different between MAC and GA (RR 0.77 (0.38 to 1.56); P = 0.460), also cardiac- and procedure-related mortality was similar between both groups (RR 0.90 (0.34 to 2.39); P = 0.830). CONCLUSION: These data did not show a significant difference in short-term outcomes for MAC or GA in TAVR. MAC may be associated with reduced procedural time and shorter hospital stay. Now randomized trials are needed for further evaluation of MAC in the setting of TAVR.
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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 | 0.012 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.032 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".