Resection of intradural spinal lesions with concomitant instrumented fusion in children: a systematic review and representative cases.

OBJECTIVE: More than one-third of pediatric patients who undergo resection of intradural spine lesions develop progressive postoperative deformity, with as many as half of these patients subsequently requiring surgical fusion. Intradural spinal procedures with simultaneous instrumented fusion in children, however, are infrequently performed. Moreover, the rationale for patient selection, outcomes, and safety of this single-stage surgery in children has not been systematically investigated. In this study, the authors review the practice of simultaneous intradural spinal resection and instrumented fusion in pediatric patients and provide two representative case examples from their institution. METHODS: The authors searched the PubMed and Embase databases and performed a systematic review following the PRISMA protocol. Original articles of pediatric patients (age ≤ 18 years) who underwent intradural spine surgery, regardless of pathology, with concomitant instrumented fusion and reported outcomes were included. An institutional database of all spinal operations with instrumented fusion performed in patients aged ≤ 18 years over a 3-year period was screened to identify those who underwent intradural spine surgery with concomitant fusion. RESULTS: Nine patients (median age 12 years) from 6 studies who underwent intradural lesion resection and concomitant fusion met inclusion criteria. Among all 11 patients included, primary rationales for concomitant fusion were extensive bone removal (i.e., corpectomy or total facetectomy, 73%), concerns for deformity in the setting of multilevel laminectomy/laminoplasty (18%), and severe baseline deformity (9%). The most represented pathology was neurenteric cyst (55%) followed by schwannoma (18%). Myxopapillary ependymoma, granular cell tumor, and pilocytic astrocytoma each were seen in 1 case. Seven patients (64%) underwent an anterior-approach corpectomy, tumor resection, and fusion, while the remaining 4 patients (36%) underwent a posterior approach. All patients with at least 1 year of follow-up cases achieved bony fusion. CSF leak and new-onset neurological deficit each occurred in 9% (1/11). CONCLUSIONS: The rationales for performing single-stage intradural resection and fusion in pediatric patients in studies to date include the presence of severe baseline deformity, large extent of bone resection, and multilevel laminectomy/laminoplasty across cervicothoracic or thoracolumbar junctions. As current literature involving this cohort is limited, more data are needed to determine when concomitant fusion in intradural resections is appropriate in pediatric patients and whether its routine implementation is safe or beneficial.

Transfer of extensor carpi radialis brevis branch of radial nerve to anterior interosseous nerve for lower trunk brachial plexopathy after motor vehicle accident.

The patient is a 15-year-old male who sustained injury to his right lower brachial plexus (C8-T1) in a motor vehicle accident. Six months after the injury, the patient still had persistent hand weakness and wished to regain function in his first and second digits. Transfer of the extensor carpi radialis brevis (ECRB) branch of the radial nerve to the anterior interosseous nerve (AIN) was performed to restore motor function. The patient did well after the surgery, although it may take 12-24 months for benefits to fully manifest. Pertinent surgical anatomy and techniques are highlighted in this video demonstration. The video can be found here: https://stream.cadmore.media/rr10.3171/2022.10.FOCVID2287.

Utility of machine learning algorithms in degenerative cervical and lumbar spine disease: a systematic review.

Machine learning is a rapidly evolving field that offers physicians an innovative and comprehensive mechanism to examine various aspects of patient data. Cervical and lumbar degenerative spine disorders are commonly age-related disease processes that can utilize machine learning to improve patient outcomes with careful patient selection and intervention. The aim of this study is to examine the current applications of machine learning in cervical and lumbar degenerative spine disease. A systematic review was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A search of PubMed, Embase, Medline, and Cochrane was conducted through May 31st, 2020, using the following terms: "artificial intelligence" OR "machine learning" AND "neurosurgery" AND "spine." Studies were included if original research on machine learning was utilized in patient care for degenerative spine disease, including radiographic machine learning applications. Studies focusing on robotic applications in neurosurgery, navigation, or stereotactic radiosurgery were excluded. The literature search identified 296 papers, with 35 articles meeting inclusion criteria. There were nine studies involving cervical degenerative spine disease and 26 studies on lumbar degenerative spine disease. The majority of studies for both cervical and lumbar spines utilized machine learning for the prediction of postoperative outcomes, with 5 (55.6%) and 15 (61.5%) studies, respectively. Machine learning applications focusing on degenerative lumbar spine greatly outnumber the current volume of cervical spine studies. The current research in lumbar spine also demonstrates more advanced clinical applications of radiographic, diagnostic, and predictive machine learning models.