Percutaneous Coronary Sinus Catheter Placement and Management With Transesophageal Echocardiography for Robotic Mitral Valve Surgery
Bibliographic record
Abstract
INTRODUCTION: Placement of a percutaneous coronary sinus catheter (CSC) for retrograde cardioplegia delivery in minimally invasive cardiac surgery has been implemented at various centers. While several techniques have been described for catheter placement, we present our experience utilizing transesophageal echocardiography (TEE) alone for guiding the successful placement and management of percutaneous CSC during robotic mitral valve repair (MVr). METHODS: We retrospectively studied all adult patients who underwent planned robotic MVr by a single surgeon at our institution from August 2013 to December 2021. Confirmation of successful CSC placement was made by review of electronic medical records and TEE data. RESULTS: Out of 144 patients in the study cohort, 135 (94%) patients had successful CSC placement. The median time from anesthesia start in the operating room to surgical incision was 93 min (interquartile range 82-103 min). Of the 135 patients with successful CSC placement, 122 patients (90%) had maintenance of cardiac arrest during cardiopulmonary bypass with retrograde cardioplegia only. Two patients (1.4%) had complications; one had an injury of the coronary sinus (unrelated to CSC placement) requiring sternotomy, and one had an episode of ventricular tachycardia requiring defibrillation. DISCUSSION: In our experience, TEE guidance offers an effective approach for percutaneous CSC placement without requiring fluoroscopic guidance. It allows the safe conduct of surgery with multidose administration of retrograde cardioplegia. The alternative to placing a percutaneous CSC is administration of antegrade cardioplegia only, which may not be feasible in all patients, and if feasible, may have limitations in robotic MVr.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| 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) | 0.000 | 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 teacher head, 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".