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Spinal Cord Injury ll: Pathophysiology

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Evaluation of Respiratory Muscle Activation Using Respiratory Motor Control Assessment RMCA in Individuals with Chronic Spinal Cord Injury

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Development and validation of a computerized algorithm for International Standards for Neurological Classification of

K Walden¹, L M Bélanger^1,2, F Biering-Sørensen³

¹Rick Hansen Institute, Vancouver, British Columbia, Canada.

|September 2, 2015

Summary

A new algorithm standardizes spinal cord injury (SCI) classification using international standards. This computerized tool improves accuracy and usability in SCI assessment.

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Area of Science:

Neurology
Medical Informatics
Rehabilitation Medicine

Background:

The International Standards for Neurological Classification of Spinal Cord Injury (ISNCSCI) are crucial for SCI assessment.
Standardizing the application of ISNCSCI is essential for consistent and accurate patient evaluation.
Previous methods may have introduced variability in SCI classification.

Purpose of the Study:

To develop and validate a computerized application for the ISNCSCI.
To enhance the usability and accuracy of SCI classification.
To standardize the interpretation of complex or challenging SCI cases.

Main Methods:

Development of the Rick Hansen Institute-ISNCSCI Algorithm (RHI-ISNCSCI Algorithm) based on ISNCSCI 2011 and worksheet 2013.
A five-phased development and validation process involving international experts.
Reconciliation of discrepancies between clinician-derived and algorithm-calculated results using data from acute and rehabilitation care settings.

Main Results:

The algorithm logic was refined through multiple phases using 48, 15, 351, and 1998 cases.
Cross-validation with 108 new cases confirmed algorithm accuracy, with discrepancies attributed to clinician errors.
The web-based application and algorithm code are freely available online.

Conclusions:

The RHI-ISNCSCI Algorithm offers a standardized and accurate method for SCI classification.
The web interface enhances usability and minimizes human error in SCI assessment.
This tool supports consistent neurological classification of spinal cord injury.