Computer International Standards for Neurological Classification of Spinal Cord Injury (ISNCSCI) algorithms: a review

• Published in: Spinal cord Vol. 61; no. 2; pp. 125 - 132
• Main Authors: Walden, Kristen, Schuld, Christian, Noonan, Vanessa K, Rupp, Rüdiger
• Format: Journal Article
• Language: English
• Published: England Nature Publishing Group 01.02.2023; Nature Publishing Group UK
• Subjects: Algorithms; Classification; Computers; Datasets; Education; Evaluation; Humans; International standards; Literature reviews; Multicenter Studies as Topic; Neurologic Examination; Quality control; Reclassification; Spinal cord injuries; Spinal Cord Injuries - diagnosis; Surveys; Surveys and Questionnaires; Workflow
• ISSN: 1362-4393; 1476-5624
• DOI: 10.1038/s41393-022-00854-2

Literature review and survey. To provide an overview of existing computerized International Standards for Neurological Classification of Spinal Cord Injury (ISNCSCI) algorithms and to evaluate the use of the current algorithms in research and clinical care. Not applicable. Literature review according to three organizing concepts for evaluation of Health Information Products (reach, usefulness, and use) was conducted. While the use of computerized ISNCSCI algorithms has been around for many years, many were developed and used internally for specific projects or not maintained. Today the International SCI community has free access to algorithms from the European Multicenter Study about Spinal Cord Injury (EMSCI) and the Praxis Spinal Cord Institute. Both algorithms have been validated in large datasets and are used in different SCI registries for quality control and education purposes. The use of the Praxis Institute algorithm by clinicians was highlighted through the Praxis User Survey (n = 76) which included participants from 27 countries. The survey found that over half of the participants using the algorithm (N = 69) did so on a regular basis (51%), with 54% having incorporated it into their regular workflow. Validated computerized ISNCSCI classification tools have evolved substantially and support education, clinical documentation, communication between clinicians and their patients, and ISNCSCI data quality around the world. They are not intended to replace well-trained clinicians, but allow for reclassification of ISNCSCI datasets with updated versions of the ISCNSCI, and support rapid classification of large datasets.