Subgroups in low back pain – a treatment-based classification algorithm

I always feel a bit embarrassed when I partake in blatant self-promotion; however, as this blog post aims to foster discussion rather than self-promote I hope to ease my guilty conscience. So….I’m here to chat about the main study of my PhD that just came out in the April issue of Physical Therapy.[1] To start off with, I have to acknowledge some important co-authors. I was lucky enough to collaborate with the big guns in subgrouping to work on this study – Julie Fritz (Utah, US), Mark Hancock/Chris Maher/Jane Latimer (Sydney), Eric Parent (Edmonton, Canada), and Benedict Wand (Notre Dame Australia). Always nice to work with the experts in the field!

Our study aimed to evaluate a treatment-based classification algorithm for low back pain. This algorithm was created to provide a clinical decision-making paradigm to help guide treatment selection for back pain. It is being used A LOT in the States.

This algorithm was created by putting together the findings from a bunch of different studies that aimed to identify subgroups of low back pain that respond best to certain treatments (eg, manipulation,[2] stabilisation,[3] direction-specific exercise,[4,5] and traction[6]). The criteria from these individual studies that identified subgroups will henceforth be known as individual study criteria.

Now, in order for a decision making paradigm (aka the algorithm) to be useful and help guide clinical practice, we know that there are some key features that need to be present.  First, the paradigm must be comprehensive (aka put every patient we hope to classify into a subgroup). Second, the paradigm must be mutually exclusive (aka put patients in one and only one subgroup). In order to translate the individual studies[2-6] into a clinical decision making paradigm, changes had to be made to the individual study criteria. These changes included hierarchical ordering of subgroups (eg, go down the list of treatments and whichever one is ‘met’ first is the treatment the patient should get), creation of a bottom table with additional criteria (eg, to help classify all patients, even the ones that might not meet the original subgroups), and modification to the criteria themselves. Now these changes were based on assumptions – granted assumptions founded on loads of clinical and research experience – but assumptions none-the-less. Thus our study looked to evaluate those assumptions. We did this by examining 250 patients with acute/subacute low back pain and seeing what treatment subgroup(s) they met, using both the individual study criteria and the classification algorithm to classify them.

Unfortunately, we are going to be a bit like a reality tv show here and go to commercial break, just when you are about to find out who got kicked off Biggest Loser…but hopefully you’re hooked and need to stick around after the jump for the intriguing results in the next post!

Tasha Stanton

Tasha Stanton BiMTasha Stanton is a postdoctoral research fellow working with the Body in Mind Research Group both in Adelaide (at University of South Australia) and in Sydney (at Neuroscience Research Australia). Tash has done a bit of hopping around in her career, from studying physio in her undergrad, to spinal biomechanics in her Master’s, to clinical epidemiology in her PhD, and now to clinical neuroscience in her postdoc. Amazingly, there has been a common thread through all this hopping and that common thread is pain. What is pain? Why do we have it? And why doesn’t it go away?

Tash’s research interests lie in understanding the neuroscience behind pain and its clinical implications. She also really likes nifty experiments that may have no clinical value yet, but whose coolness factor tops the charts. Last, Tash is a bit mad about running, enjoying a good red with friends and organizing theme parties.

References:

[1] Stanton TR, Fritz JM, Hancock MJ, Latimer J, Maher CG, Wand BM, & Parent EC (2011). Evaluation of a treatment-based classification algorithm for low back pain: a cross-sectional study. Physical therapy, 91 (4), 496-509 PMID: 21330450

[2] Flynn T, Fritz J, Whitman J, Wainner R, Magel J, Rendeiro D, Butler B, Garber M, & Allison S (2002). A clinical prediction rule for classifying patients with low back pain who demonstrate short-term improvement with spinal manipulation. Spine, 27 (24), 2835-43 PMID: 12486357

[3] Hicks GE, Fritz JM, Delitto A, & McGill SM (2005). Preliminary development of a clinical prediction rule for determining which patients with low back pain will respond to a stabilization exercise program. Archives of physical medicine and rehabilitation, 86 (9), 1753-62 PMID: 16181938

[4] Browder DA, Childs JD, Cleland JA, & Fritz JM (2007). Effectiveness of an extension-oriented treatment approach in a subgroup of subjects with low back pain: a randomized clinical trial. Physical therapy, 87 (12) PMID: 17895350

[5] Long A, Donelson R, & Fung T (2004). Does it matter which exercise? A randomized control trial of exercise for low back pain. Spine, 29 (23), 2593-602 PMID: 15564907

[6] Fritz, J., Lindsay, W., Matheson, J., Brennan, G., Hunter, S., Moffit, S., Swalberg, A., & Rodriquez, B. (2007). Is There a Subgroup of Patients With Low Back Pain Likely to Benefit From Mechanical Traction? Spine, 32 (26) DOI: 10.1097/BRS.0b013e31815d001a

[7] Brennan GP, Fritz JM, Hunter SJ, Thackeray A, Delitto A, & Erhard RE (2006). Identifying subgroups of patients with acute/subacute “nonspecific” low back pain: results of a randomized clinical trial. Spine, 31 (6), 623-31 PMID: 16540864