Prognostic Parameters of Acute Transverse Myelitis in Children.
Esther Ganelin-CohenOsnat KonenYoram NevoRony CohenAyelet HalevyAvinoam ShuperSharon AharoniPublished in: Journal of child neurology (2020)
Acute transverse myelitis is a rare and disabling disorder. Data on the imaging features in children are sparse. The aim of this study was to describe the clinical and magnetic resonance imaging findings characteristic of pediatric idiopathic acute transverse myelitis and to identify those with prognostic value. The database of a tertiary pediatric medical center was retrospectively reviewed for patients aged less than 18 years who were diagnosed in 2002-2017 with acute transverse myelitis that was not associated with recurrence of a demyelinating autoimmune event. Data were collected on clinical, laboratory, and imaging findings and outcome. A total of 23 children (11 male, 12 female) met the study criteria. Mean age at disease onset was 10 years, and mean duration of follow-up was 6 years 10 months. Spinal cord and brain magnetic resonance imaging scans were performed on admission or shortly thereafter. The most common finding was cross-sectional involvement, in 16 patients (70%). The mean number of involved spinal segments was 8. The most frequently involved region was the thoracic spine, in 17 patients (74%). Clinical factors predicting good prognosis were cerebrospinal fluid pleocytosis, absence of tetraparesis, and prolonged time to nadir. In conclusion, most children with acute transverse myelitis appear to have a good outcome. Prompt diagnosis and treatment are important. Further research is needed in a larger sample to evaluate the predictive value of imaging features.
Keyphrases
- magnetic resonance imaging
- end stage renal disease
- spinal cord
- liver failure
- ejection fraction
- newly diagnosed
- chronic kidney disease
- high resolution
- cross sectional
- peritoneal dialysis
- drug induced
- respiratory failure
- computed tomography
- prognostic factors
- magnetic resonance
- machine learning
- tyrosine kinase
- neuropathic pain
- cerebrospinal fluid
- artificial intelligence
- deep learning
- subarachnoid hemorrhage