A data-driven approach to categorize patients with traumatic spinal cord injury: cluster analysis of a multicentre database

Shahin Basiratzadeh; Ramtin Hakimjavadi; Natalie Baddour; Wojtek Michalowski; Herna Viktor; Eugene Wai; Alexandra Stratton; Stephen Kingwell; Jean-Marc Mac-Thiong; Eve C Tsai; Zhi Wang; Philippe Phan

doi:10.3389/fneur.2023.1263291

A data-driven approach to categorize patients with traumatic spinal cord injury: cluster analysis of a multicentre database

Front Neurol. 2023 Oct 12:14:1263291. doi: 10.3389/fneur.2023.1263291. eCollection 2023.

Authors

Shahin Basiratzadeh¹, Ramtin Hakimjavadi², Natalie Baddour³, Wojtek Michalowski¹, Herna Viktor⁴, Eugene Wai^{5

6}, Alexandra Stratton^{5

6}, Stephen Kingwell^{5

6}, Jean-Marc Mac-Thiong^{7

8}, Eve C Tsai^{9

10}, Zhi Wang¹¹, Philippe Phan^{5

6}

Affiliations

¹ Telfer School of Management, University of Ottawa, Ottawa, ON, Canada.
² Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada.
³ Department of Mechanical Engineering, Faculty of Engineering, University of Ottawa, Ottawa, ON, Canada.
⁴ School of Electrical Engineering and Computer Science, Faculty of Engineering, University of Ottawa, Ottawa, ON, Canada.
⁵ Division of Orthopedic Surgery, Ottawa Hospital Research Institute (OHRI), Ottawa, ON, Canada.
⁶ Department of Surgery, Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada.
⁷ Hôpital du Sacré-Cœur de Montréal, Montreal, QC, Canada.
⁸ Faculty of Medicine, University of Montreal, Montreal, QC, Canada.
⁹ Division of Neurosurgery, The Ottawa Hospital, Ottawa, ON, Canada.
¹⁰ Department of Cellular and Molecular Medicine, Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada.
¹¹ Department of Orthopedic Surgery, University of Montreal Health Center, Montreal, QC, Canada.

Abstract

Background: Conducting clinical trials for traumatic spinal cord injury (tSCI) presents challenges due to patient heterogeneity. Identifying clinically similar subgroups using patient demographics and baseline injury characteristics could lead to better patient-centered care and integrated care delivery.

Purpose: We sought to (1) apply an unsupervised machine learning approach of cluster analysis to identify subgroups of tSCI patients using patient demographics and injury characteristics at baseline, (2) to find clinical similarity within subgroups using etiological variables and outcome variables, and (3) to create multi-dimensional labels for categorizing patients.

Study design: Retrospective analysis using prospectively collected data from a large national multicenter SCI registry.

Methods: A method of spectral clustering was used to identify patient subgroups based on the following baseline variables collected since admission until rehabilitation: location of the injury, severity of the injury, Functional Independence Measure (FIM) motor, and demographic data (age, and body mass index). The FIM motor score, the FIM motor score change, and the total length of stay were assessed on the subgroups as outcome variables at discharge to establish the clinical similarity of the patients within derived subgroups. Furthermore, we discussed the relevance of the identified subgroups based on the etiological variables (energy and mechanism of injury) and compared them with the literature. Our study also employed a qualitative approach to systematically describe the identified subgroups, crafting multi-dimensional labels to highlight distinguishing factors and patient-focused insights.

Results: Data on 334 tSCI patients from the Rick Hansen Spinal Cord Injury Registry was analyzed. Five significantly different subgroups were identified (p-value ≤0.05) based on baseline variables. Outcome variables at discharge superimposed on these subgroups had statistically different values between them (p-value ≤0.05) and supported the notion of clinical similarity of patients within each subgroup.

Conclusion: Utilizing cluster analysis, we identified five clinically similar subgroups of tSCI patients at baseline, yielding statistically significant inter-group differences in clinical outcomes. These subgroups offer a novel, data-driven categorization of tSCI patients which aligns with their demographics and injury characteristics. As it also correlates with traditional tSCI classifications, this categorization could lead to improved personalized patient-centered care.

Keywords: cluster analysis; data-driven method; patient categorization; patient-centric approach; traumatic spinal cord injury.

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. The Rick Hansen Spinal Cord Injury Registry and this work are supported by funding from the Praxis Spinal Cord Institute through the Government of Canada and the Province of British Columbia. For more information about RHSCIR, please visit www.praxisinstitute.org. The authors would like to acknowledge the support of the Natural Sciences and Engineering Research Council of Canada (NSERC), (funding reference numbers: NSERC GR000540 and NSERC 482728-2016-CREATE) through the Government of Canada. For more information about NSERC, please visit www.nserc-crsng.gc.ca.