Multidimensional Prognostic Modelling in People with Chronic Axial Low Back Pain

Abstract

Copyright © 2017 Wolters Kluwer Health, Inc. All rights reserved. Objectives: To derive prognostic models for people with chronic low back pain (CLBP) (n=294) based upon an extensive array of potentially prognostic multidimensional factors. Materials and Methods: This study entered multidimensional data (demographics, pain characteristics, pain responses to movement, behaviors associated with pain, pain sensitivity, psychological, social, health, lifestyle) at baseline, and interventions undertaken, into prognostic models for pain intensity, disability, global rating of change and bothersomeness at 1-year. Results: The prognostic model for higher pain intensity (explaining 23.2% of the variance) included higher baseline pain intensity and punishing spousal interactions, and lower years in education, while participating in exercise was prognostic of lower pain intensity. The model for higher disability (33.6% of the variance) included higher baseline disability, longer forward bending time, psychological principal component scores representing negative pain-related cognitions and punishing spousal interactions; while exercising was prognostic of lower disability. The odds of reporting global rating of change much/very much improved were increased by participating in exercise, having leg pain as well as CLBP and having greater chronic pain acceptance. The receiver operating characteristic area under the curve was 0.72 indicating acceptable discrimination. The odds of reporting very/extremely bothersome CLBP were increased by having higher baseline pain intensity, longer forward bending time and receiving injection(s); while higher age, more years in education and having leg pain decreased the odds (receiver operating characteristic area under the curve, 0.80; acceptable discrimination). Discussion: The variance explained by prognostic models was similar to previous reports, despite an extensive array of multidimensional baseline variables. This highlights the inherent multidimensional complexity of CLBP.