Epidural Anesthesia Improves Outcome and Resource Use in Cardiac Surgery: A Single-Center Study of a 1293-Patient Cohort
Bibliographic record
Abstract
Thoracic epidural anesthesia (TEA) combined with general anesthesia in cardiac surgery has the potential to initiate earlier spontaneous ventilation and extubation, improved hemodynamics, less arrhythmia or myocardial ischemia, and an attenuated neurohormonal response. The aim of the current study was to characterize the correlation between TEA and postoperative resource use or outcome in a consecutive-patient cohort. The study was performed in a tertiary care, 3-surgeon, university-affiliated hospital that performs 350 to 400 cardiac surgeries per year. All 1293 adult patients who underwent cardiac surgery between July 1, 2002, and February 1, 2006, were included. Patients were assigned to anesthesiologists practicing TEA (TEA group, n = 506) or not (control group, n = 787) for cardiac surgery. The preoperative parameter values and Parsonnet scores for the 2 groups were similar. The 2 groups had the same distribution of surgery types. The TEA group presented with fewer intensive care unit (ICU) complications, such as delirium, pneumonia, and acute renal failure, and presented with better myocardial protection. The TEA group presented with a higher proportion of immediately postoperative extubations and with shorter ventilation times and ICU stays. Total ICU costs decreased from US $18,700 to $9900 per patient. Combining TEA and general anesthesia for cardiac surgery allows a significant change in anesthesia strategy. This change improves immediate postoperative outcomes and reduces the use and costs of ICU resources.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".