Pre‐operative Eligibility for Minimally Invasive Coronary Artery Bypass Grafting Using the DaVinci Robot: An Examination of Anatomical Parameters using Computed Tomography
Bibliographic record
Abstract
Introduction Minimally invasive coronary artery bypass procedures have been shown to reduce morbidity, 30‐day complication rates, and length of hospital stays, as well as increasing patient quality of life scores post‐operatively. The robotic‐assisted endoscopic single‐vessel small thoracotomy (endo‐SVST) bypass procedure is a minimally invasive procedure that confers similar benefits to the more invasive conventional full‐sternotomy revascularization approach. A limitation to the endo‐SVST procedure that still requires attention is the pre‐operative selection criteria. Inappropriate selection can result in intra‐operative conversions from the endo‐SVST to a conventional full‐sternotomy resulting in increased patient morbidity, and both operative and general anesthetic times along with increased costs. One of the primary intraoperative concerns, necessitating conversion to a conventional full‐sternotomy, is the inability of the endoscopic camera to visualize the left anterior descending (LAD) coronary artery, the target vessel, under the surrounding epicardial adipose tissue. The purpose of this study is to determine if anatomical and anthropometric parameters, examined using both patient data and pre‐operative computed tomography (CT) images, are able to predict and thus reduce the need for conversion based on effective pre‐operative exclusion criteria. Methods Retrospective analysis of patient pre‐operative CT angiography scans from both converted (N=13) and robotic‐assisted (N=13) procedures using the DaVinci Surgical Robot was performed. Patient scans were anonymized and measurements were made using 3D Slicer 4.4.0. Where possible, measurements of thoracic cavity dimensions and epicardial adipose depths were acquired from axial slices, at the most accessible segment of the LAD, in the fourth anterior intercostal space. An independent‐samples Student T Test (∝=0.05) and Pearson Correlation (∝=0.05) were performed using SPSS. Results Preliminary results indicate that patients who successfully underwent the endo‐SVST procedure had significantly less epicardial adipose tissue (p=0.03) overlying the LAD in the transverse measurement than those who were converted to the full‐sternotomy intra‐operatively. This data also suggests that there are no significant differences between the two groups with respect to the remaining epicardial adipose tissue and anthropometric measurements. A moderate, but non‐significant, positive correlation R=0.36 (p=0.07) appears to exist between body mass index (BMI) and the depth of epicardial adipose tissue within the anterior inter‐ventricular sulcus. Discussion These data suggest that a transverse depth measurement of epicardial adipose tissue overlying the LAD of 7.6±3.3mm may indicate a greater risk for conversion to the full‐sternotomy. Preliminary findings also indicate that the relationship between the depth of epicardial adipose tissue and conversion to full‐sternotomy may not be fully explained by a patient's BMI. However, future studies with a larger sample size need to be conducted in order to examine the relationship between these anatomical and anthropometric parameters and to also assess whether their combined use may further enhance the precision of pre‐operative exclusion criteria.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".