Preoperative Vascular Imaging for Predicting Intraoperative Modification of Peripheral Arterial Cannulation during Minimally Invasive Mitral Valve Surgery
Bibliographic record
Abstract
OBJECTIVE: Minimally invasive mitral valve surgery using peripheral cannulation for cardiopulmonary bypass (CBP) is increasingly prevalent. Although conceptually straightforward, peripheral CBP involves challenges and risks specific to this method of perfusion. The utility of preoperative vascular imaging in predicting these technical challenges and preventing vascular complications was studied. METHODS: We performed a retrospective analysis of 73 consecutive patients undergoing minimally invasive mitral valve surgery using femorofemoral CBP with intraluminal aortic occlusion balloon catheter. All patients underwent preoperative computed tomography angiogram or magnetic resonance angiography to study the iliofemoral axes. RESULTS: None of the patients operated with this technique was found to have arterial stenoses. Patients with a femoral artery diameter of less than 7.3 mm needed bilateral or side-graft arterial cannulation significantly more frequently than patients with larger femoral arteries (46.2% vs 9.1%, P = 0.001). There was a trend toward more frequent modification of arterial cannulation strategy in patients with body surface area less than 1.7 m compared with larger patients (body surface area, 1.7-2.0) (26.3% vs 8.3%, P = 0.07). Patients needing high CBP flow rate (>5 L/min) were no more likely to need dual arterial cannulation (18.2% vs 19.1%, P = 0.68). No patient experienced a vascular complication. CONCLUSIONS: This preliminary study suggests that preoperative vascular imaging and patient evaluation may predict difficulties with femoral cannulation and perfusion, which can lead to better preoperative planning and potentially prevent vascular complications. Further data will be accumulated and analyzed to confirm these findings.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".