Real-time 3D Transesophageal Echocardiography for the Placement of Ventriculoatrial Shunt: A Case Series and Technical Note
Bibliographic record
Abstract
BACKGROUND: Ventriculoatrial (VA) shunts are used to manage hydrocephalus and idiopathic intracranial hypertension when peritoneal drainage of cerebrospinal fluid is not feasible. The technique of distal catheter placement during VA shunt insertion is controversial, especially between fluoroscopy-guided and transesophageal echocardiography (TEE)-guided techniques. METHODS: We retrospectively reviewed our utilization of 2-dimensional (2D) ultrasound-guided internal jugular vein catheterization combined with 3-dimensional (3D) TEE-guided distal VA shunt placement and compared it to the conventional fluoroscopy-guided technique. RESULTS: Ten patients underwent 18 VA shunt insertion procedures between November 2012 and October 2022. The patients had a mean (SD) age of 50 (19) years, body mass index of 35 (14) m/kg², and minimal comorbidities. All had previously undergone failed ventriculoperitoneal shunt procedures. The use of 2D ultrasound to guide internal jugular vein catheterization and 3D TEE to guide distal catheter placement resulted in 22-minute shorter surgical times compared with the fluoroscopy-guided technique (91 minutes vs. 113 minutes, respectively). No complications were noted with either technique. CONCLUSIONS: The combined use of 2D ultrasound and 3D TEE allowed for faster procedure times and more precise distal catheter confirmation, contributing to a more streamlined surgical procedure. This small case series underscores the feasibility, efficiency, and safety of anesthesiologist-delivered combined 2D ultrasound and 3D TEE during VA shunt insertion. The use of 3D TEE allows repeated confirmation of distal catheter position and has potential to improve patient safety during rare but complex VA shunt insertion procedures.
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 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".