Literature DB >> 26494768

Identifying Patients With Chronic Low Back Pain Who Respond Best to Mechanical Diagnosis and Therapy: Secondary Analysis of a Randomized Controlled Trial.

Alessandra Narciso Garcia1, Luciola da Cunha Menezes Costa2, Mark Hancock3, Leonardo Oliveira Pena Costa4.   

Abstract

BACKGROUND: "Mechanical Diagnosis and Therapy" (MDT) (also known as the McKenzie method), like other interventions for low back pain (LBP), has been found to have small effects for people with LBP. It is possible that a group of patients respond best to MDT and have larger effects. Identification of patients who respond best to MDT compared with other interventions would be an important finding.
OBJECTIVE: The purpose of the study was to investigate whether baseline characteristics of patients with chronic LBP, already classified as derangement syndrome, can identify those who respond better to MDT compared with Back School.
METHODS: This study was a secondary analysis of data from a previous trial comparing MDT with Back School in 148 patients with chronic LBP. Only patients classified at baseline assessment as being in the directional preference group (n=140) were included. The effect modifiers tested were: clear centralization versus directional preference only, baseline pain location, baseline pain intensity, and age. The primary outcome measures for this study were pain intensity and disability at the end of treatment (1 month). Treatment effect modification was evaluated by assessing the group versus predictor interaction terms from linear regression models. Interactions ≥1.0 for pain and ≥3 for disability were considered clinically important.
RESULTS: Being older met our criteria for being a potentially important effect modifier; however, the effect occurred in the opposite direction to our hypothesis. Older people had 1.27 points more benefit in pain reduction from MDT (compared with Back School) than younger participants after 1 month of treatment. LIMITATIONS: The sample (n=140) was powered to detect the main effects of treatment but not to detect the interactions of the potential treatment effect modifiers.
CONCLUSIONS: The results of the study suggest older age may be an important factor that can be considered as a treatment effect modifier for patients with chronic LBP receiving MDT. As the main trial was not powered for the investigation of subgroups, the results of this secondary analysis have to be interpreted cautiously, and replication is needed.
© 2016 American Physical Therapy Association.

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Year:  2015        PMID: 26494768     DOI: 10.2522/ptj.20150295

Source DB:  PubMed          Journal:  Phys Ther        ISSN: 0031-9023


  8 in total

Review 1.  Back Schools for chronic non-specific low back pain.

Authors:  Patrícia Parreira; Martijn W Heymans; Maurits W van Tulder; Rosmin Esmail; Bart W Koes; Nolwenn Poquet; Chung-Wei Christine Lin; Christopher G Maher
Journal:  Cochrane Database Syst Rev       Date:  2017-08-03

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.  The most common classification in the mechanical diagnosis and therapy for patients with a primary complaint of non-acute knee pain was Spinal Derangement: a retrospective chart review.

Authors:  Sanshiro Hashimoto; Masatsugu Hirokado; Hiroshi Takasaki
Journal:  J Man Manip Ther       Date:  2018-09-12

4.  Identifying Treatment Effect Modifiers in the STarT Back Trial: A Secondary Analysis.

Authors:  Jason M Beneciuk; Jonathan C Hill; Paul Campbell; Ebenezer Afolabi; Steven Z George; Kate M Dunn; Nadine E Foster
Journal:  J Pain       Date:  2016-10-17       Impact factor: 5.820

5.  Looking ahead: chronic spinal pain management.

Authors:  Gregory F Parkin-Smith; Stephanie J Davies; Lyndon G Amorin-Woods
Journal:  J Pain Res       Date:  2017-08-30       Impact factor: 3.133

6.  Rehabilitation management of low back pain - it's time to pull it all together!

Authors:  Yannick Tousignant-Laflamme; Marc Olivier Martel; Anand B Joshi; Chad E Cook
Journal:  J Pain Res       Date:  2017-10-03       Impact factor: 3.133

Review 7.  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

8.  Mastering Prognostic Tools: An Opportunity to Enhance Personalized Care and to Optimize Clinical Outcomes in Physical Therapy.

Authors:  Yannick Tousignant-Laflamme; Catherine Houle; Chad Cook; Florian Naye; Annie LeBlanc; Simon Décary
Journal:  Phys Ther       Date:  2022-05-05
  8 in total

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