Header

UZH-Logo

Maintenance Infos

Electrophysiological multimodal assessments improve outcome prediction in traumatic cervical spinal cord injury


Hupp, Markus; Pavese, Chiara; Bachmann, Lucas; Koller, Rene; Schubert, Martin (2018). Electrophysiological multimodal assessments improve outcome prediction in traumatic cervical spinal cord injury. Journal of Neurotrauma, 35(24):2916-2923.

Abstract

Outcome prediction after spinal cord injury (SCI) is essential for early counseling and orientation of the rehabilitative intervention. Moreover, prognostication of outcome is crucial to achieve 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 open. Data of 224 patients included in the European Multicenter Study about Spinal Cord Injury were analyzed with bootstrapping analysis and multivariate logistic regression to derive prediction models of complete functional recovery in chronic stage after traumatic cervical SCI. As possible predictors, 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 ] within 40 days after SCI. 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. ClinicalTrials.gov Identifier: NCT01571531.

Abstract

Outcome prediction after spinal cord injury (SCI) is essential for early counseling and orientation of the rehabilitative intervention. Moreover, prognostication of outcome is crucial to achieve 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 open. Data of 224 patients included in the European Multicenter Study about Spinal Cord Injury were analyzed with bootstrapping analysis and multivariate logistic regression to derive prediction models of complete functional recovery in chronic stage after traumatic cervical SCI. As possible predictors, 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 ] within 40 days after SCI. 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. ClinicalTrials.gov Identifier: NCT01571531.

Statistics

Citations

Dimensions.ai Metrics

Altmetrics

Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > Balgrist University Hospital, Swiss Spinal Cord Injury Center
Dewey Decimal Classification:610 Medicine & health
Uncontrolled Keywords:Clinical Neurology
Language:English
Date:15 December 2018
Deposited On:29 May 2018 14:33
Last Modified:24 Sep 2019 23:29
Publisher:Mary Ann Liebert
ISSN:0897-7151
OA Status:Closed
Free access at:Publisher DOI. An embargo period may apply.
Publisher DOI:https://doi.org/10.1089/neu.2017.5576
PubMed ID:29792368

Download

Full text not available from this repository.
View at publisher

Get full-text in a library