Thursday, October 9, 2025

9:40 - 10:30

Plenary Room F1+F2+F3

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Details

Background for this lecture is that the increased survival after spinal cord injury (SCI) worldwide has enhanced the need for quality data that can be compared and shared between centres, countries, as well as across research studies, to better understand how best to prevent and treat SCI. Such data should be standardized and be able to be uniformly collected at any SCI centre or within any SCI study. Standardization will make it possible to collect information from larger SCI populations for multi-centre research studies. With this aim, the international SCI community has obtained consensus regarding the best available data and measures for use in SCI clinical practice and research. Data elements are continuously updated and developed using an open and transparent process. There are ongoing internal, as well as external review processes, where all interested parties are encouraged to participate. The first standardisation related to individuals with SCI gaining international impact was the Frankel Classification, which was further developed in the International Standards for Neurological Classification of Spinal Cord Injury (ISNCSCI) and is now the choice for neurological classification world-wide: https://asia-spinalinjury.org/learning/. Although its complexity is being challenged by simpler classifications. Similarly has the International Standards to document Autonomic Function following SCI (ISAFSCI) been developed including General autonomic function (Cardiovascular, Thermoregulation, and Broncho-pulmonary system) and Sacral autonomic function (Bladder emptying, Bowel emptying, and Sexual function): https://asia-spinalinjury.org/learning/. The Spinal Cord Independence Measure (SCIM) is likewise a tool developed for Activities of Daily Living for individuals with SCI. Now available as version IV. To obtain worldwide success, measures and data sets must be relevant and simple. If clinically relevant, these measures and data elements will also be appropriate for research. It is likewise imperative that these are easily retrieved, and available for use without restrictions and free of charge. International SCI Data Sets has been developed within many areas relevant for individuals with SCI and include the Core Data Set, which is the recommended minimal data set to be collected for all individuals with a new SCI during their initial in-patient period. Basic SCI Data Sets are meant as the minimal number of data elements, which together should be collected in daily clinical practice for a particular topic. Extended SCI Data Sets are more detailed data sets, which may be used as optional for a topic, but may be recommended for specific research within the topic. Apart from the Core Data Set there are when writing 24 Basic and 5 Extended Data Sets available at https://www.iscos.org.uk/page/Int-SCI-Data-Sets. These Data Sets are as well adopted by the National Institute of Health (NIH), National Institute of Neurological Disorders and Stroke (NINDS), Common Data Elements, which is another source where standardized measures for use in relation to individuals with SCI are available: https://commondataelements.ninds.nih.gov/Spinal%20Cord%20Injury. These international examinations/measurements and SCI Data Sets are included as part of SCI registries around the world, including in electronic medical records. Likewise, translations are available in several languages. Cultural differences between countries may be greater for some than for other topics, which imply that the use of some data elements may be more difficult to implement globally. However, it is strived making the variables/elements as universal as possible, not least by having individuals from various areas of the world as participants or as reviewers. Future Opportunities: Given the complexity of SCI, e.g. Big Data perspectives are needed. Implementation of these require multi-modal data and open science and data to be FAIR (Findable, Accessible, Interoperable and Reusable). Advanced analytics such as artificial intelligence require data to be interoperable so data can be exchanged. This is needed to facilitate translation in research and care. It is critical that people with lived experience are engaged to ensure data are relevant and enhances quality of life. The analysis of Big Data may identify new associations and new ways of understanding and informing new treatment approaches.

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Presenter (if the session has co-presenters, they will be listed in the APP)

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Moderator