Literature DB >> 31961930

The Use of a Novel Perfusion-Based Human Cadaveric Model for Simulation of Dural Venous Sinus Injury and Repair.

Ben A Strickland1, Kristine Ravina1, Alexandra Kammen1, Stephanie Chang1, Martin Rutkowski1, Daniel A Donoho1, Mike Minneti2, Anna Jackanich1, Joshua Bakhsheshian1, Gabriel Zada1.   

Abstract

BACKGROUND: Dural sinus injuries are potentially serious complications associated with acute blood loss. It is imperative that neurosurgery trainees are able to recognize and manage this challenging scenario.
OBJECTIVE: To assess the feasibility of a novel perfusion-based cadaveric simulation model to provide the fundamentals of dural sinus repair to neurosurgical trainees.
METHODS: A total of 10 perfusion-based human cadaveric models underwent superior sagittal sinus (SSS) laceration. Neurosurgery residents were instructed to achieve hemostasis by any method in the first trial and then repeated the trial after watching the instructional dural flap technique video. Trials were timed until hemostasis and control of the region of injury was achieved. Pre- and post-trial questionnaires were administered to assess trainee confidence levels.
RESULTS: The high-flow extravasation of the perfusion-based cadaveric model mimicked similar conditions and challenges encountered during acute SSS injury. Mean ± standard deviation time to hemostasis was 341.3 ± 65 s in the first trial and 196.9 ± 41.8 s in the second trial (P < .0001). Mean trainee improvement time was 144.4 s (42.3%). Of the least-experienced trainees with longest repair times in the initial trial, a mean improvement time of 188.3 s (44.8%) was recorded. All participants reported increased confidence on post-trial questionnaires following the simulation (median pretrial confidence of 2 vs post-trial confidence of 4, P = .002).
CONCLUSION: A perfusion-based human cadaveric model accurately simulates acute dural venous sinus injury, affording neurosurgical trainees the opportunity to hone management skills in a simulated and realistic environment.
Copyright © 2020 by the Congress of Neurological Surgeons.

Entities:  

Keywords:  Complication; Cranial neurosurgery; Dural venous sinus injury; Sinus repair

Mesh:

Year:  2020        PMID: 31961930     DOI: 10.1093/ons/opz424

Source DB:  PubMed          Journal:  Oper Neurosurg (Hagerstown)        ISSN: 2332-4252            Impact factor:   2.703


  2 in total

1.  Expert surgeons and deep learning models can predict the outcome of surgical hemorrhage from 1 min of video.

Authors:  Dhiraj J Pangal; Guillaume Kugener; Yichao Zhu; Aditya Sinha; Vyom Unadkat; David J Cote; Ben Strickland; Martin Rutkowski; Andrew Hung; Animashree Anandkumar; X Y Han; Vardan Papyan; Bozena Wrobel; Gabriel Zada; Daniel A Donoho
Journal:  Sci Rep       Date:  2022-05-17       Impact factor: 4.996

2.  Utility of the Simulated Outcomes Following Carotid Artery Laceration Video Data Set for Machine Learning Applications.

Authors:  Guillaume Kugener; Dhiraj J Pangal; Tyler Cardinal; Casey Collet; Elizabeth Lechtholz-Zey; Sasha Lasky; Shivani Sundaram; Nicholas Markarian; Yichao Zhu; Arman Roshannai; Aditya Sinha; X Y Han; Vardan Papyan; Andrew Hung; Animashree Anandkumar; Bozena Wrobel; Gabriel Zada; Daniel A Donoho
Journal:  JAMA Netw Open       Date:  2022-03-01
  2 in total

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