Literature DB >> 28153805

The Value of Prognostic Screening for Patients With Low Back Pain in Secondary Care.

Emma L Karran1, Adrian C Traeger2, James H McAuley2, Susan L Hillier3, Yun-Hom Yau4, G Lorimer Moseley5.   

Abstract

Prognostic screening in patients with low back pain (LBP) offers a practical approach to guiding clinical decisions. Whether screening is helpful in secondary care is unclear. This prospective cohort study in adults with LBP placed on outpatient clinic waiting lists, compared the performance of the short-form Orebro Musculoskeletal Pain Screening Questionnaire, the Predicting the Inception of Chronic Pain Tool, and the STarT Back Tool. We assessed predictive validity for outcome at 4-month follow-up, by calculating estimates of discrimination, calibration, and overall performance. We applied a decision curve analysis approach to describe the clinical value of screening in this setting via comparison with a 'treat-all' strategy. Complete data were available for 89% of enrolled participants (n = 195). Eighty-four percent reported 'poor outcome' at follow-up. The area under the receiver operating characteristic curve (95% confidence interval) was .66 (.54-.78) for the Orebro Musculoskeletal Pain Screening Questionnaire, .61 (.49-.73) for the Predicting the Inception of Chronic Pain Tool, and .69 (.51-.80) for the STarT Back Tool. All instruments were miscalibrated and underestimated risk. The decision curve analysis indicated that, in this setting, prognostic screening does not add value over and above a treat-all approach. The potential for LBP patients to be misclassified using screening and the high incidence of nonrecovery indicate that care decisions should be made with the assumption that all patients are 'at risk.' PERSPECTIVE: This article presents a head-to-head comparison of 3 LBP screening instruments in a secondary care setting. Early patient screening is likely to hold little clinical value in this setting and care pathways that consider all patients at risk of a poor outcome are suggested to be most appropriate.
Copyright © 2017 American Pain Society. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Low back pain; predictive validity; prognostic screening

Mesh:

Year:  2017        PMID: 28153805     DOI: 10.1016/j.jpain.2016.12.020

Source DB:  PubMed          Journal:  J Pain        ISSN: 1526-5900            Impact factor:   5.820


  13 in total

1.  Optimal Screening for Prediction of Referral and Outcome (OSPRO) for Musculoskeletal Pain Conditions: Results From the Validation Cohort.

Authors:  Steven Z George; Jason M Beneciuk; Trevor A Lentz; Samuel S Wu; Yunfeng Dai; Joel E Bialosky; Giorgio Zeppieri
Journal:  J Orthop Sports Phys Ther       Date:  2018-04-07       Impact factor: 4.751

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.  Translation and Adaptation of the French Version of the Risk Stratification Index, a Tool for Stratified Care in Chronic Low Back Pain: A Pilot Study.

Authors:  Alexandra Naïr; Chiao-I Lin; Pia-Maria Wippert
Journal:  Medicina (Kaunas)       Date:  2022-03-23       Impact factor: 2.948

4.  A quasi-randomised, controlled, feasibility trial of GLITtER (Green Light Imaging Interpretation to Enhance Recovery)-a psychoeducational intervention for adults with low back pain attending secondary care.

Authors:  Emma L Karran; Susan L Hillier; Yun-Hom Yau; James H McAuley; G Lorimer Moseley
Journal:  PeerJ       Date:  2018-02-01       Impact factor: 2.984

5.  Risk classification of patients referred to secondary care for low back pain.

Authors:  Monica Unsgaard-Tøndel; Ingunn Gunnes Kregnes; Tom I L Nilsen; Gunn Hege Marchand; Torunn Askim
Journal:  BMC Musculoskelet Disord       Date:  2018-05-24       Impact factor: 2.362

Review 6.  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

7.  Do sensorimotor cortex activity, an individual's capacity for neuroplasticity, and psychological features during an episode of acute low back pain predict outcome at 6 months: a protocol for an Australian, multisite prospective, longitudinal cohort study.

Authors:  Luke C Jenkins; Wei-Ju Chang; Valentina Buscemi; Matthew Liston; Barbara Toson; Michael Nicholas; Thomas Graven-Nielsen; Michael Ridding; Paul W Hodges; James H McAuley; Siobhan M Schabrun
Journal:  BMJ Open       Date:  2019-05-22       Impact factor: 2.692

8.  Identifying psychosocial characteristics that predict outcome to the UPLIFT programme for people with persistent back pain: protocol for a prospective cohort study.

Authors:  Hayley Thomson; Kerrie Evans; Jonathon Dearness; John Kelley; Kylie Conway; Collette Morris; Leanne Bisset; Gwendolijne Scholten-Peeters; Pim Cuijpers; Michel W Coppieters
Journal:  BMJ Open       Date:  2019-08-10       Impact factor: 2.692

Review 9.  Multidimensional screening for predicting pain problems in adults: a systematic review of screening tools and validation studies.

Authors:  Elke Veirman; Dimitri M L Van Ryckeghem; Annick De Paepe; Olivia J Kirtley; Geert Crombez
Journal:  Pain Rep       Date:  2019-09-11

10.  Personalized Treatment Suggestions: The Validity and Applicability of the Risk-Prevention-Index Social in Low Back Pain Exercise Treatments.

Authors:  Pia-Maria Wippert; Anne-Katrin Puschmann; David Drießlein; Winfried Banzer; Heidrun Beck; Marcus Schiltenwolf; Christian Schneider; Frank Mayer
Journal:  J Clin Med       Date:  2020-04-22       Impact factor: 4.241

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