Literature DB >> 29602747

The predictive ability of the STarT Back Tool was limited in people with chronic low back pain: a prospective cohort study.

Michelle Kendell1, Darren Beales1, Peter O'Sullivan1, Martin Rabey1, Jonathan Hill2, Anne Smith1.   

Abstract

QUESTIONS: In people with chronic non-specific low back pain (LBP), what is the predictive and discriminative validity of the STarT Back Tool (SBT) for pain intensity, self-reported LBP-related disability, and global self-perceived change at 1-year follow-up? What is the profile of the SBT risk subgroups with respect to demographic variables, pain intensity, self-reported LBP-related disability, and psychological measures?
DESIGN: Prospective cohort study. PARTICIPANTS: A total of 290 adults with dominant axial LBP of≥3months' duration recruited from the general community, and private physiotherapy, psychology, and pain-management clinics in Western Australia. OUTCOME MEASURES: The 1-year follow-up measures were pain intensity, LBP-related disability, and global self-perceived change.
RESULTS: Outcomes were collected on 264 participants. The SBT categorised 82 participants (28%) as low risk, 116 (40%) as medium risk, and 92 (32%) as high risk. The risk subgroups differed significantly (p<0.05) on baseline pain, disability, and psychological scores. The SBT's predictive ability was strongest for disability: RR was 2.30 (95% CI 1.28 to 4.10) in the medium-risk group and 2.86 (95% CI 1.60 to 5.11) in the high-risk group. The SBT's predictive ability was weaker for pain: RR was 1.25 (95% CI 1.04 to 1.51) in the medium-risk group and 1.26 (95% CI 1.03 to 1.52) in the high-risk group. For the SBT total score, the AUC was 0.71 (95% CI 0.64 to 0.77) for disability and 0.63 (95% CI 0.55 to 0.71) for pain.
CONCLUSION: This was the first large study to investigate the SBT in a population exclusively with chronic LBP. The SBT provided an acceptable indication of 1-year disability, had poor predictive and discriminative ability for future pain, and was unable to predict or discriminate global perceived change. In this cohort with chronic non-specific LBP, the SBT's predictive and discriminative abilities were restricted to disability at 1year. [Kendell M, Beales D, O'Sullivan P, Rabey M, Hill J, Smith A (2018) The predictive ability of the STarT Back Tool was limited in people with chronic low back pain: a prospective cohort study. Journal of Physiotherapy 64: 107-113].
Copyright © 2018 Australian Physiotherapy Association. Published by Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Decision support techniques; Low pack pain; Prognosis; Validation studies

Mesh:

Year:  2018        PMID: 29602747     DOI: 10.1016/j.jphys.2018.02.009

Source DB:  PubMed          Journal:  J Physiother        ISSN: 1836-9561            Impact factor:   7.000


  9 in total

1.  Individual recovery expectations and prognosis of outcomes in non-specific low back pain: prognostic factor review.

Authors:  Jill A Hayden; Maria N Wilson; Richard D Riley; Ross Iles; Tamar Pincus; Rachel Ogilvie
Journal:  Cochrane Database Syst Rev       Date:  2019-11-25

Review 2.  Artificial intelligence to improve back pain outcomes and lessons learnt from clinical classification approaches: three systematic reviews.

Authors:  Scott D Tagliaferri; Maia Angelova; Xiaohui Zhao; Patrick J Owen; Clint T Miller; Tim Wilkin; Daniel L Belavy
Journal:  NPJ Digit Med       Date:  2020-07-09

3.  Validation of the Subgroups for Targeted Treatment for Back (STarT Back) screening tool at a tertiary care centre.

Authors:  Susan Robarts; Helen Razmjou; Albert Yee; Joel Finkelstein
Journal:  Can J Surg       Date:  2022-05-17       Impact factor: 2.840

4.  Use of the STarT Back Screening Tool in patients with chronic low back pain receiving physical therapy interventions.

Authors:  Flávia Cordeiro Medeiros; Evelyn Cassia Salomão; Leonardo Oliveira Pena Costa; Diego Galace de Freitas; Thiago Yukio Fukuda; Renan Lima Monteiro; Marco Aurélio Nemitalla Added; Alessandra Narciso Garcia; Lucíola da Cunha Menezes Costa
Journal:  Braz J Phys Ther       Date:  2020-07-29       Impact factor: 3.377

Review 5.  Artificial intelligence to improve back pain outcomes and lessons learnt from clinical classification approaches: three systematic reviews.

Authors:  Scott D Tagliaferri; Maia Angelova; Xiaohui Zhao; Patrick J Owen; Clint T Miller; Tim Wilkin; Daniel L Belavy
Journal:  NPJ Digit Med       Date:  2020-07-09

6.  Feasibility and long-term efficacy of a proactive health program in the treatment of chronic back pain: a randomized controlled trial.

Authors:  A Hüppe; C Zeuner; S Karstens; M Hochheim; M Wunderlich; H Raspe
Journal:  BMC Health Serv Res       Date:  2019-10-21       Impact factor: 2.655

Review 7.  Psychosocial Predictors of Pain and Disability Outcomes in People with Chronic Low Back Pain Treated Conservatively by Guideline-Based Intervention: A Systematic Review.

Authors:  Ahmed S Alhowimel; Mazyad A Alotaibi; Aqeel M Alenazi; Bader A Alqahtani; Mansour A Alshehri; Dalyah Alamam; Faris A Alodaibi
Journal:  J Multidiscip Healthc       Date:  2021-12-30

8.  Predictors of response following standardized education and self-management recommendations for low back pain stratified by dominant pain location.

Authors:  Anthony V Perruccio; Jessica T Y Wong; Elizabeth M Badley; J Denise Power; Calvin Yip; Y Raja Rampersaud
Journal:  N Am Spine Soc J       Date:  2021-11-07

9.  Addition of MoodGYM to physical treatments for chronic low back pain: A randomized controlled trial.

Authors:  M John Petrozzi; Andrew Leaver; Paulo H Ferreira; Sidney M Rubinstein; Mairwen K Jones; Martin G Mackey
Journal:  Chiropr Man Therap       Date:  2019-10-25
  9 in total

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