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Development and validation of a computerized algorithm for International Standards for Neurological Classification of Spinal Cord Injury (ISNCSCI)


Walden K, Bélanger LM, Biering-Sřrensen F, Burns SP, Echeverria E, Kirshblum S, Marino RJ, Noonan VK, Park SE, Reeves RK, Waring W, Dvorak MF



Publication Info:

Spinal Cord, Epub ahead of print 09/2015:


STUDY DESIGN: Validation study.
OBJECTIVES: To describe the development and validation of a computerized application of the international standards for neurological classification of spinal cord injury (ISNCSCI).
SETTING: Data from acute and rehabilitation care.
METHODS: The Rick Hansen Institute-ISNCSCI Algorithm (RHI-ISNCSCI Algorithm) was developed based on the 2011 version of the ISNCSCI and the 2013 version of the worksheet. International experts developed the design and logic with a focus on usability and features to standardize the correct classification of challenging cases. A five-phased process was used to develop and validate the algorithm. Discrepancies between the clinician-derived and algorithm-calculated results were reconciled.
RESULTS: Phase one of the validation used 48 cases to develop the logic. Phase three used these and 15 additional cases for further logic development to classify cases with ‘Not testable’ values. For logic testing in phases two and four, 351 and 1998 cases from the Rick Hansen SCI Registry (RHSCIR), respectively, were used. Of 23 and 286 discrepant cases identified in phases two and four, 2 and 6 cases resulted in changes to the algorithm. Cross-validation of the algorithm in phase five using 108 new RHSCIR cases did not identify the need for any further changes, as all discrepancies were due to clinician errors. The web-based application and the algorithm code are freely available at
CONCLUSION: The RHI-ISNCSCI Algorithm provides a standardized method to accurately derive the level and severity of SCI from the raw data of the ISNCSCI examination. The web interface assists in maximizing usability while minimizing the impact of human error in classifying SCI.

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