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Lesion-symptom mapping with NIHSS sub-scores in ischemic stroke patients
  1. Deepthi Rajashekar1,2,3,
  2. Matthias Wilms2,
  3. M Ethan MacDonald2,4,5,
  4. Serena Schimert2,4,
  5. Michael D Hill2,3,4,6,7,
  6. Andrew Demchuk2,7,
  7. Mayank Goyal2,7,
  8. Sean P Dukelow3,4,
  9. Nils Daniel Forkert2,3,4,8
  1. 1 Biomedical Engineering Graduate Program, Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
  2. 2 Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
  3. 3 Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
  4. 4 Department of Clinical Neurosciences, University of Calgary, Calgary, Alberta, Canada
  5. 5 Department of Electrical and Computer Engineering, University of Calgary, Calgary, Alberta, Canada
  6. 6 Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
  7. 7 Department of Medicine, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
  8. 8 Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, Canada
  1. Correspondence to Dr Deepthi Rajashekar; deepthi.rajasheka1{at}ucalgary.ca

Abstract

Background Lesion-symptom mapping (LSM) is a statistical technique to investigate the population-specific relationship between structural integrity and post-stroke clinical outcome. In clinical practice, patients are commonly evaluated using the National Institutes of Health Stroke Scale (NIHSS), an 11-domain clinical score to quantitate neurological deficits due to stroke. So far, LSM studies have mostly used the total NIHSS score for analysis, which might not uncover subtle structure–function relationships associated with the specific sub-domains of the NIHSS evaluation. Thus, the aim of this work was to investigate the feasibility to perform LSM analyses with sub-score information to reveal category-specific structure–function relationships that a total score may not reveal.

Methods Employing a multivariate technique, LSM analyses were conducted using a sample of 180 patients with NIHSS assessment at 48-hour post-stroke from the ESCAPE trial. The NIHSS domains were grouped into six categories using two schemes. LSM was conducted for each category of the two groupings and the total NIHSS score.

Results Sub-score LSMs not only identify most of the brain regions that are identified as critical by the total NIHSS score but also reveal additional brain regions critical to each function category of the NIHSS assessment without requiring extensive, specialised assessments.

Conclusion These findings show that widely available sub-scores of clinical outcome assessments can be used to investigate more specific structure–function relationships, which may improve predictive modelling of stroke outcomes in the context of modern clinical stroke assessments and neuroimaging.

Trial registration number NCT01778335.

  • stroke
  • lesion

Data availability statement

All data relevant to the study are included in the article or uploaded as supplemental information. Not applicable.

http://creativecommons.org/licenses/by-nc/4.0/

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

https://doi.org/10.1136/svn-2021-001091

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    Data availability statement

    All data relevant to the study are included in the article or uploaded as supplemental information. Not applicable.

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    Footnotes

    • Contributors DR—conceptualisation, data curation, formal analysis, investigation, methodology, validation, visualisation, roles/writing—original draft, software, gaurantor; MW—formal analysis, methodology, writing—review and editing, validation; MEM—writing—review and editing, visualisation; SS—writing—review and editing; MDH—conceptualisation, data curation, writing—review and editing; AMD—resources; MG—resources; SPD—writing—review and editing; NDF—conceptualisation, project administration, supervision, writing—review and editing, funding acquisition.

    • Funding This work was funded by the Heart and Stroke Foundation of Canada Grant in aid (G-17-0018368), the Canada Research Chairs program, and the River Fund at Calgary Foundation.

    • Competing interests None declared.

    • Provenance and peer review Not commissioned; externally peer reviewed.

    • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.