• Markus Hupp, Chiara Pavese, Lucas M Bachmann, René Koller, Martin Schubert, and EMSCI Study Group.
• 1 Spinal Cord Injury Center, University Hospital Balgrist, Zurich, Switzerland.
• J. Neurotrauma. 2018 Dec 15; 35 (24): 2916-2923.

AbstractOutcome prediction after spinal cord injury (SCI) is essential for early counseling and orientation of the rehabilitative intervention. Moreover, prognostication of outcome is crucial to achieving meaningful stratification when conceiving clinical trials. Neurophysiological examinations are commonly employed for prognostication after SCI, but whether neurophysiology could improve the functional prognosis based on clinical predictors remains an open question. Data of 224 patients included in the European Multicenter Study about Spinal Cord Injury were analyzed with bootstrapping analysis and multivariate logistical regression to derive prediction models of complete functional recovery in the chronic stage after traumatic cervical SCI. Within 40 days after SCI, we evaluated age, gender, the motor and sensory cumulative scores of the International Standards for Neurological Classification of Spinal Cord Injury (ISNCSCI), and neurophysiological variables (motor evoked potentials, sensory evoked potentials, nerve conduction study) as possible predictors. Positive outcome was defined by a Spinal Cord Independence Measure total score of 100. Analyzing clinical variables, we derived a prediction model based on the ISNCSCI total motor score and age: the area under the receiver operating curve (AUC) was 0.936 (95% confidence interval [CI]: 0.904-0.968). Adding neurophysiological variables to the model, the AUC increased significantly: 0.956 (95% CI: 0.930-0.982; p = 0.019). More patients could be correctly classified by adding the electrophysiological data. Our study demonstrates that neurophysiological assessment improves the prediction of functional prognosis after traumatic cervical SCI, and suggests the use of neurophysiology to optimize patient information, rehabilitation, and discharge planning and the design of future clinical trials.

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