Towards data-driven biopsychosocial classification of non-specific chronic low back pain: a pilot study

Scott D. Tagliaferri, Patrick J. Owen, Clint T. Miller, Maia Angelova, Bernadette M. Fitzgibbon, Tim Wilkin, Hugo Masse-Alarie, Jessica Van Oosterwijck, Guy Trudel, David Connell, Anna Taylor, Daniel L. Belavy

Research output: Contribution to journalArticleResearchpeer-review

Abstract

The classification of non-specific chronic low back pain (CLBP) according to multidimensional data could guide clinical management; yet recent systematic reviews show this has not been attempted. This was a prospective cross-sectional study of participants with CLBP (n = 21) and age-, sex- and height-matched pain-free controls (n = 21). Nervous system, lumbar spinal tissue and psychosocial factors were collected. Dimensionality reduction was followed by fuzzy c-means clustering to determine sub-groups. Machine learning models (Support Vector Machine, k-Nearest Neighbour, Naïve Bayes and Random Forest) were used to determine the accuracy of classification to sub-groups. The primary analysis showed that four factors (cognitive function, depressive symptoms, general self-efficacy and anxiety symptoms) and two clusters (normal versus impaired psychosocial profiles) optimally classified participants. The error rates in classification models ranged from 4.2 to 14.2% when only CLBP patients were considered and increased to 24.2 to 37.5% when pain-free controls were added. This data-driven pilot study classified participants with CLBP into sub-groups, primarily based on psychosocial factors. This contributes to the literature as it was the first study to evaluate data-driven machine learning CLBP classification based on nervous system, lumbar spinal tissue and psychosocial factors. Future studies with larger sample sizes should validate these findings.

Original languageEnglish
Article number13112
Number of pages17
JournalScientific Reports
Volume13
Issue number1
DOIs
Publication statusPublished - Dec 2023

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