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Transcondylar Odontoid Resection and Stabilization for Craniovertebral Degenerative Compression: 2-Dimensional Operative Video.

Walid I B N EssayedOssama Al-Mefty
Published in: Operative neurosurgery (Hagerstown, Md.) (2021)
Non-neoplastic craniovertebral junction lesions are well known with various etiologies.1,2 They are frequently associated with craniovertebral junction instability. Many require only stabilization for their management.2 However, when significant irreducible anterior compression is present, surgical decompression becomes necessary.2-4 The traditional decompression route is direct anterior, such as the transoral, transmaxillary, or endoscopic endonasal approaches with a separate posterior stabilization.1,2 The transcondylar approach offers a wide and direct exposure to the anterolateral foramen magnum and atlantoaxial space, allowing extensive decompression, total resection of the odontoid, and associated pannus, even with large lateral extension, as well as fusion in the same surgical setting.5 The surgical manipulation is parallel to the dural sac in the sagittal plane, which could be safer than perpendicular dissection.5 Understanding the regional anatomy allows safe exposure and transposition of the vertebral artery with the surrounding alveolar and venous plexus (suboccipital cavernous sinus).5-7 We present this technique's details in a case of a 72-yr-old female who presented with progressively worsening bilateral upper extremity weakness and significant anterior compression due to irreducible odontoid degenerative changes. We demonstrate the steps necessary to achieve adequate exposure and decompression. The patient agreed to the surgical intervention. Images at 2:46, 3:00, and 3:25 reused from Al-Mefty et al,5 by permission from JNSPG. Images at 9:28 from Symonds et al,3 by permission of Oxford University Press. Image at 2:15, © Ossama Al-Mefty, used with permission.
Keyphrases
  • minimally invasive
  • deep learning
  • randomized controlled trial
  • case report
  • ultrasound guided
  • optical coherence tomography
  • machine learning