Anesthesia for Minimally Invasive Coronary Artery Bypass Surgery
Bibliographic record
Abstract
Minimally invasive coronary artery bypass grafting (MI-CABG) has emerged as a transformative approach to coronary revascularization, offering reduced morbidity, faster recovery and improved cosmesis compared to conventional coronary artery bypass grafting (CABG). Performed without full sternotomy and commonly without cardiopulmonary bypass (CPB), MI-CABG encompasses a variety of techniques. These procedures present unique challenges for the anesthesiologist, necessitating a tailored perioperative strategy. This review explores the anesthetic management of MI-CABG, focusing on preoperative assessment, intraoperative techniques, and postoperative care. Preoperative evaluation emphasizes cardiac, respiratory, and vascular considerations, including suitability for one-lung ventilation (OLV) and the impact of comorbidities. Intraoperatively, anesthesiologists must manage hemodynamic instability, ensure effective OLV, and maintain normothermia. Postoperative strategies prioritize multimodal analgesia, early extubation, and rapid mobilization to leverage the benefits of a minimally invasive approach. By integrating surgical and anesthetic perspectives, this review underscores the anesthesiologist's pivotal role in navigating the physiological demands of MI-CABG. As techniques evolve and experience grows, a comprehensive understanding of these principles will enhance the safety and efficacy of MI-CABG, making it a viable option for an expanding patient population.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".