Clinical predictive modelling of post-surgical recovery in individuals with cervical radiculopathy: a machine learning approach

Research output: Contribution to journalArticlepeer-review

Authors

  • Bernard X W Liew
  • Anneli Peolsson
  • David Rugamer
  • Johanna Wibault
  • Hakan Löfgren
  • Asa Dedering
  • Peter Zsigmond

External organisations

  • School of Sport, Rehabilitation and Exercise Sciences, University of Essex, Colchester, Essex, United Kingdom.
  • Division of Cardiovascular Medicine, Linköping University, Linköping, Sweden.
  • Department of Mathematics and Statistics
  • Neuro-Orthopedic Center
  • Karolinska University Hospital and Karolinska Institutet, Stockholm
  • Department of Neurosurgery, Linköping University Hospital, Linköping, Sweden.
  • Centre of Precision Rehabilitation for Spinal Pain (CPR Spine), School of Sport, Exercise and Rehabilitation Sciences, College of Life and Environmental Sciences, University of Birmingham, Birmingham, United Kingdom.
  • School of Sport
  • Biosciences, College of Life and Environmental Sciences, University of Exeter

Abstract

Prognostic models play an important role in the clinical management of cervical radiculopathy (CR). No study has compared the performance of modern machine learning techniques, against more traditional stepwise regression techniques, when developing prognostic models in individuals with CR. We analysed a prospective cohort dataset of 201 individuals with CR. Four modelling techniques (stepwise regression, least absolute shrinkage and selection operator [LASSO], boosting, and multivariate adaptive regression splines [MuARS]) were each used to form a prognostic model for each of four outcomes obtained at a 12 month follow-up (disability-neck disability index [NDI]), quality of life (EQ5D), present neck pain intensity, and present arm pain intensity). For all four outcomes, the differences in mean performance between all four models were small (difference of NDI < 1 point; EQ5D < 0.1 point; neck and arm pain < 2 points). Given that the predictive accuracy of all four modelling methods were clinically similar, the optimal modelling method may be selected based on the parsimony of predictors. Some of the most parsimonious models were achieved using MuARS, a non-linear technique. Modern machine learning methods may be used to probe relationships along different regions of the predictor space.

Details

Original languageEnglish
Article number16782
Pages (from-to)16782
JournalScientific Reports
Volume10
Issue number1
Publication statusPublished - 8 Oct 2020

ASJC Scopus subject areas