Do baseline characteristics predict response to treatment for low back pain? Secondary analysis of the UK BEAM dataset [ISRCTN32683578]

Underwood, Martin; Morton, Veronica; Farrin, Amanda

doi:10.1093/rheumatology/kem113

Cited by 82 publications

(70 citation statements)

References 38 publications

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“…For example, LopezLopez et al [12] reported statistical interactions between pre-intervention trait anxiety and different MT techniques, such low and high levels of anxiety were associated with varying levels of clinical outcome based on the MT technique received. A secondary analysis of the UK BEAM dataset [67] did not however find any statistical interactions when evaluating for similar relationships with pre-intervention back pain beliefs and treatment response.…”

Section: Moderating For Effectiveness Of Mtmentioning

confidence: 81%

What Effect can Manual Therapy have on a Patient’s Pain Experience?

et al. 2015

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Manual therapy (MT) is a passive, skilled movement applied by clinicians that directly or indirectly targets a variety of anatomical structures or systems, which is utilized with the intent to create beneficial changes in some aspect of the patient pain experience. Collectively, the process of MT is grounded on clinical reasoning to enhance patient management for musculoskeletal pain by influencing factors from a multidimensional perspective that have potential to positively impact clinical outcomes. The influence of biomechanical, neurophysiological, psychological and nonspecific patient factors as treatment mediators and/or moderators provides additional information related to the process and potential mechanisms by which MT may be effective. As healthcare delivery advances toward personalized approaches there is a crucial need to advance our understanding of the underlying mechanisms associated with MT effectiveness.

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Section: Moderating For Effectiveness Of Mtmentioning

confidence: 81%

What Effect can Manual Therapy have on a Patient’s Pain Experience?

et al. 2015

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“…This analysis, in common with secondary analyses of the UK BEAM data set and a trial of acupuncture by Sherman et al, have failed to find convincing data to suggest that subgroups can be identified in existing trial data (29,30). Together they have considered a range of potential moderators for 5 different treatment packages in rigorous analyses.…”

Section: Discussionmentioning

confidence: 97%

Predicting response to a cognitive–behavioral approach to treating low back pain: Secondary analysis of the BeST data set

Underwood¹,

Mistry²,

Lall³

et al. 2011

Arthritis Care & Research

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Objective. Identifying factors that predict who is likely to gain the greatest benefit from different treatments for low back pain is an important research priority. Here we report moderator analyses of the Back Skills Training Trial (BeST) that tested a cognitive-behavioral approach for low back pain. Methods. We recruited 701 participants ages >18 years with at least moderately troublesome low back pain present for >6 weeks from 56 general practices in 7 localities across England to a trial adding a group cognitive-behavioral approach to active management advice. The cognitive-behavioral package had a moderate effect on primary outcomes (Roland Morris Disability Questionnaire [RMDQ] and modified Von Korff scales). At 12-month followup, we tested for interaction between randomized groups on 2 prespecified baseline variables (troublesomeness and fear avoidance) and 10 post hoc (exploratory) variables identified from previous studies. Results. Neither troublesomeness nor fear avoidance moderated treatment effect on any of our primary outcomes. In the final model, the only moderation by baseline variables of the effect of randomization was on the RMDQ outcome. Being younger and currently working both moderated treatment effect, resulting in larger improvements as a response to treatment. Conclusion. Although BeST is one of the larger trials of back pain treatment, it is still too small to reliably detect moderation if it exists. Since the significant moderation effects were only observed for 1 outcome measure in 3 of 10 post-hoc analyses, we cannot conclude that these are true moderation effects.

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“…May and Aina 29 showed in a recent review that a centralization response occurs in 50% of patients with sub-acute LBP and 40% of patients with chronic LBP. Secondly, although the prevalence of certain psychological characteristics is higher in patients with chronic LBP, it is unclear if these characteristics are helpful in guiding treatment, 30,31 and, as such, a mechanically-based classification system may still be useful. This point is reinforced by findings that, in patients with acute, sub-acute and chronic LBP, centralization might be a stronger predictor of outcome than psychosocial factors if treatment is matched to directional preference.…”

Section: 11mentioning

confidence: 99%

Inter-rater reliability of a modified version of Delitto et al.’s classification-based system for low back pain: a pilot study

Apeldoorn

Helvoirt²,

Ostelo

et al. 2016

Journal of Manual & Manipulative Therapy

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Study design: Observational inter-rater reliability study. Objectives: To examine: (1) the inter-rater reliability of a modified version of Delitto et al.'s classificationbased algorithm for patients with low back pain; (2) the influence of different levels of familiarity with the system; and (3) the inter-rater reliability of algorithm decisions in patients who clearly fit into a subgroup (clear classifications) and those who do not (unclear classifications). Methods: Patients were examined twice on the same day by two of three participating physical therapists with different levels of familiarity with the system. Patients were classified into one of four classification groups. Raters were blind to the others' classification decision. In order to quantify the inter-rater reliability, percentages of agreement and Cohen's Kappa were calculated. Results: A total of 36 patients were included (clear classification n523; unclear classification n513). The overall rate of agreement was 53% and the Kappa value was 0.34 [95% confidence interval (CI): 0.11-0.57], which indicated only fair inter-rater reliability. Inter-rater reliability for patients with a clear classification (agreement 52%, Kappa value 0.29) was not higher than for patients with an unclear classification (agreement 54%, Kappa value 0.33). Familiarity with the system (i.e. trained with written instructions and previous research experience with the algorithm) did not improve the inter-rater reliability. Conclusion: Our pilot study challenges the inter-rater reliability of the classification procedure in clinical practice. Therefore, more knowledge is needed about factors that affect the inter-rater reliability, in order to improve the clinical applicability of the classification scheme.

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Do baseline characteristics predict response to treatment for low back pain? Secondary analysis of the UK BEAM dataset [ISRCTN32683578]

Cited by 82 publications

References 38 publications

What Effect can Manual Therapy have on a Patient’s Pain Experience?

What Effect can Manual Therapy have on a Patient’s Pain Experience?

Predicting response to a cognitive–behavioral approach to treating low back pain: Secondary analysis of the BeST data set

Inter-rater reliability of a modified version of Delitto et al.’s classification-based system for low back pain: a pilot study

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