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Towards data-driven biopsychosocial classification of non-specific chronic low back pain: a pilot study.
Tagliaferri, Scott D; Owen, Patrick J; Miller, Clint T; Angelova, Maia; Fitzgibbon, Bernadette M; Wilkin, Tim; Masse-Alarie, Hugo; Van Oosterwijck, Jessica; Trudel, Guy; Connell, David; Taylor, Anna; Belavy, Daniel L.
Afiliación
  • Tagliaferri SD; Institute for Physical Activity and Nutrition, School of Exercise and Nutrition Sciences, Deakin University, Geelong, Australia. scott.tagliaferri@unimelb.edu.au.
  • Owen PJ; Orygen, 35 Poplar Rd, Parkville, VIC, 3052, Australia. scott.tagliaferri@unimelb.edu.au.
  • Miller CT; Centre of Youth Mental Health, University of Melbourne, Melbourne, VIC, Australia. scott.tagliaferri@unimelb.edu.au.
  • Angelova M; Institute for Physical Activity and Nutrition, School of Exercise and Nutrition Sciences, Deakin University, Geelong, Australia.
  • Fitzgibbon BM; Institute for Physical Activity and Nutrition, School of Exercise and Nutrition Sciences, Deakin University, Geelong, Australia.
  • Wilkin T; Data to Intelligence Research Centre, School of Information Technology, Deakin University, Geelong, Australia.
  • Masse-Alarie H; Department of Psychiatry, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC, Australia.
  • Van Oosterwijck J; Monarch Research Group, Monarch Mental Health Group, Sydney, Australia.
  • Trudel G; Data to Intelligence Research Centre, School of Information Technology, Deakin University, Geelong, Australia.
  • Connell D; Département de Réadaptation, Centre Interdisciplinaire de Recherche en Réadaptation et Integration Sociale (Cirris), Université Laval, Quebec City, Canada.
  • Taylor A; Spine, Head and Pain Research Unit Ghent, Department of Rehabilitation Sciences, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium.
  • Belavy DL; Department of Rehabilitation Sciences and Physiotherapy, Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium.
Sci Rep ; 13(1): 13112, 2023 08 12.
Article en En | MEDLINE | ID: mdl-37573418
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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Dolor de la Región Lumbar / Dolor Crónico Tipo de estudio: Observational_studies / Prevalence_studies / Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: Sci Rep Año: 2023 Tipo del documento: Article País de afiliación: Australia Pais de publicación: Reino Unido
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Dolor de la Región Lumbar / Dolor Crónico Tipo de estudio: Observational_studies / Prevalence_studies / Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: Sci Rep Año: 2023 Tipo del documento: Article País de afiliación: Australia Pais de publicación: Reino Unido