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Responders Are Different
January 5, 2016 by Dr Matthew D. Long
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January 5, 2016 by Dr Matthew D. Long
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Responders Are Different
January 5, 2016 by Dr Matthew D. Long
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It is tempting to think that manipulation has a role to play in the management of most cases of spinal pain. However, any clinician with even a modicum of experience would likely admit that there are some individuals who just don’t seem to respond - no matter what technique we may try. Of course, there are many varied causes of spine pain, and it makes sense that different mechanisms would benefit from different treatment approaches. But how do we know ahead of time whether someone will be a ‘responder’ or not?
The concept of ‘responders’ is relatively new in health care, but it is an idea that is fast gaining traction. Simply put, some individuals are geared to benefit more from a particular intervention than others (surgery, drugs, diet, exercise etc), most likely as a product of their specific diagnosis, and/or their genetic makeup. For example, a recent paper in the journal Cephalalgia (1) looked at how the genetic underpinnings of a migraineur determined their responsiveness to triptan drugs, one of the most common frontline interventions for this disease. Interestingly they found,
The concept of ‘responders’ is relatively new in health care, but it is an idea that is fast gaining traction. Simply put, some individuals are geared to benefit more from a particular intervention than others (surgery, drugs, diet, exercise etc), most likely as a product of their specific diagnosis, and/or their genetic makeup. For example, a recent paper in the journal Cephalalgia (1) looked at how the genetic underpinnings of a migraineur determined their responsiveness to triptan drugs, one of the most common frontline interventions for this disease. Interestingly they found,
"In summary, we found that the presence of the single risk allele, rs2651899 in the PRDM16 gene, was associated with an enhanced effect from triptans in all migraine and especially in MA (migraine with aura)… Furthermore, this study shows that the response to triptans can be predicted based on the number of common risk alleles for migraine: The higher the number of risk alleles in an individual, the higher the chance of effect."
Such knowledge is extremely valuable and allows clinicians to target their treatments more effectively. It is also of great use in designing comparative outcomes research. After all, the big challenge is to find a homogeneous group of test subjects with which to compare treatments. In truth, there is no such uniform group, which makes it difficult to assess the effectiveness of different treatments (since we are not confident that our patients even have the same condition). This is one of the reasons that science seems to have a hard time proving that any treatment works.
We could be forgiven for thinking that most studies simply conclude that the treatment under investigation ‘might' offer a modest benefit over doing nothing, and it is rare that any treatment proves itself as truly superior to placebo. For this reason it is encouraging to see that science now seems to be unravelling the important differences between individuals that account for these variations in treatment response. If we can select (diagnose) our patients more accurately, then we can optimise our treatment accordingly. Furthermore, there is a growing body of evidence to suggest that even the placebo response is strongly influenced by genetics (2), most likely via the dopamine and opioid pathway controls. For this reason researchers are seeking to use genomic biomarkers to identify those with a strong placebo tendency and exclude them from randomised controlled trials, so as to better detect specific treatment effects.
While great strides have been made in the genetic profiling of responders for many health conditions, there is no such test available to inform chiropractors about spinal pain patients. Most of us rely upon our diagnostic skills, and our clinical experience, to help us make this judgment. While such experience is clearly important, the human tendency towards ‘confirmation bias’ (emphasising new information that confirms what we already believe, and minimising anything that contradicts our belief) means that our clinical guesswork is often inaccurate.
We could be forgiven for thinking that most studies simply conclude that the treatment under investigation ‘might' offer a modest benefit over doing nothing, and it is rare that any treatment proves itself as truly superior to placebo. For this reason it is encouraging to see that science now seems to be unravelling the important differences between individuals that account for these variations in treatment response. If we can select (diagnose) our patients more accurately, then we can optimise our treatment accordingly. Furthermore, there is a growing body of evidence to suggest that even the placebo response is strongly influenced by genetics (2), most likely via the dopamine and opioid pathway controls. For this reason researchers are seeking to use genomic biomarkers to identify those with a strong placebo tendency and exclude them from randomised controlled trials, so as to better detect specific treatment effects.
While great strides have been made in the genetic profiling of responders for many health conditions, there is no such test available to inform chiropractors about spinal pain patients. Most of us rely upon our diagnostic skills, and our clinical experience, to help us make this judgment. While such experience is clearly important, the human tendency towards ‘confirmation bias’ (emphasising new information that confirms what we already believe, and minimising anything that contradicts our belief) means that our clinical guesswork is often inaccurate.
'If the only tool in your toolbox is a hammer, everything looks like a nail.'
For this reason researchers have tried to develop clinical prediction rules, to help us narrow down our patient selection and find those individuals who are more likely to benefit from a specific treatment. In essence, a clinical prediction rule is a checklist that defines what a responder should look like - based upon the results of past research and answering the questions, “What are the characteristics of those subjects who did do well? What did these people have in common?” While the concept sounds promising, thus far most clinical prediction rules for spine pain and manipulation response have been a little disappointing (3,4,5,6). However, a recent study entitled 'Do Participants With Low Back Pain Who Respond to Spinal Manipulative Therapy Differ Biomechanically From Nonresponders, Untreated Controls or Asymptomatic Controls?’ (7) adds another intriguing dimension to the topic of clinical prediction. In essence, they measured various biological parameters before and after 3 sessions of spinal manipulation and noted significant physiological changes in the responders - but not in those who did not improve.
The study by Wong et al sought to quantify whether spinal manipulation changed anything within the spinal tissues of those individuals who do respond well. To do so, they assembled a group of patients suffering from lower back pain and measured spinal stiffness, lumbar multifidus thickness, and fluid diffusion within the intervertebral discs (ADC). A control group of asymptomatic individuals was also studied. After 3 treatment sessions in a one week period the following was found;
The study by Wong et al sought to quantify whether spinal manipulation changed anything within the spinal tissues of those individuals who do respond well. To do so, they assembled a group of patients suffering from lower back pain and measured spinal stiffness, lumbar multifidus thickness, and fluid diffusion within the intervertebral discs (ADC). A control group of asymptomatic individuals was also studied. After 3 treatment sessions in a one week period the following was found;
1. Responders to spinal manipulative therapy for low back pain are characterized by an immediate and sustainable decrease in spinal stiffness and an increase in lumbar multifidus muscle thickness ratio.
2. In comparison, spinal manipulative therapy non-responders and asymptomatic controls showed no change in spinal stiffness or lumbar multifidus contraction ratio.
3. Immediate enhancement of lumbar disc diffusion was observed after the first spinal manipulative therapy in participants who reported improved back pain–related disability at 1 week.
The authors concluded;
"This suggests that SMT is not a broad-spectrum therapy but one that impacts factors within SMT responders that are not present in all persons with LBP. Given the observed correlations between our biomechanical outcome measures (spinal stiffness, LM thickness ratio, ADC), we would suggest that the differential response of participants with LBP to SMT is based on a biomechanical mechanism. Although the mechanism remains unknown, it is biologically plausible that decreases in spinal stiffness may permit increased disc diffusion and increased segmental motion enabling increased LM thickness ratios. The increased disc diffusion may also improve IVD health and yields favorable clinical outcomes. Although our data do not prove the existence of LBP subgroups, they do reinforce that LBP is a heterogeneous condition and that in the short term, SMT does not equally affect those who experience LBP."
So what does this mean?
Well, it does support the notion that manipulation has effects that are not simply due to analgesia or patient expectation. It also suggests that responders are somehow different - perhaps due to their genetics, or perhaps because of their diagnostic subgroup. Whatever the case, we should be mindful of the fact that not all patients are created equal, and we still don’t have the tools available to make this distinction with any accuracy.
Something to think about...
Dr Matthew D. Long
BSc (Syd) M.Chiro (Macq)
Well, it does support the notion that manipulation has effects that are not simply due to analgesia or patient expectation. It also suggests that responders are somehow different - perhaps due to their genetics, or perhaps because of their diagnostic subgroup. Whatever the case, we should be mindful of the fact that not all patients are created equal, and we still don’t have the tools available to make this distinction with any accuracy.
Something to think about...
Dr Matthew D. Long
BSc (Syd) M.Chiro (Macq)
References:
1. Christensen, A. F., Esserlind, A.-L., Werge, T., Stefansson, H., Stefánsson, K., & Olesen, J. (2015). The influence of genetic constitution on migraine drug responses. Cephalalgia. http://doi.org/10.1177/0333102415610874
2. Hall, K. T., Loscalzo, J., & Kaptchuk, T. J. (2015). Genetics and the placebo effect: the placebome. Trends in Molecular Medicine, 21(5), 285–294. http://doi.org/10.1016/j.molmed.2015.02.009
3. Dougherty, P. E., Karuza, J., Savino, D., & Katz, P. (2014). Evaluation of a modified clinical prediction rule for use with spinal manipulative therapy in patients with chronic low back pain: a randomized clinical trial. Chiropractic & Manual Therapies, 22(1), 41. http://doi.org/10.1186/s12998-014-0041-8
4. Haskins, R., Osmotherly, P. G., & Rivett, D. A. (2015). Diagnostic Clinical Prediction Rules for Specific Subtypes of Low Back Pain: A Systematic Review. The Journal of Orthopaedic and Sports Physical Therapy, 45(2), 61–76. http://doi.org/10.2519/jospt.2015.5723
5. Kongsted, A., Andersen, C. H., Hansen, M. M., & Hestbaek, L. (2015). Prediction of outcome in patients with low back pain – A prospective cohort study comparing clinicians' predictions with those of the Start Back Tool. Manual Therapy, 1–8. http://doi.org/10.1016/j.math.2015.06.008
6. Vavrek, D., Haas, M., Neradilek, M. B., & Polissar, N. (2015). Prediction of pain outcomes in a randomized controlled trial of dose–response of spinal manipulation for the care of chronic low back pain. BMC Musculoskelet Disord, 16(1), 2163–13. http://doi.org/10.1186/s12891-015-0632-0
7. Wong, A. Y. L., Parent, E. C., Dhillon, S. S., Prasad, N., & Kawchuk, G. N. (2015). Do Participants With Low Back Pain Who Respond to Spinal Manipulative Therapy Differ Biomechanically From Nonresponders, Untreated Controls or Asymptomatic Controls? Spine, 40(17), 1329–1337. http://doi.org/10.1097/BRS.0000000000000981
1. Christensen, A. F., Esserlind, A.-L., Werge, T., Stefansson, H., Stefánsson, K., & Olesen, J. (2015). The influence of genetic constitution on migraine drug responses. Cephalalgia. http://doi.org/10.1177/0333102415610874
2. Hall, K. T., Loscalzo, J., & Kaptchuk, T. J. (2015). Genetics and the placebo effect: the placebome. Trends in Molecular Medicine, 21(5), 285–294. http://doi.org/10.1016/j.molmed.2015.02.009
3. Dougherty, P. E., Karuza, J., Savino, D., & Katz, P. (2014). Evaluation of a modified clinical prediction rule for use with spinal manipulative therapy in patients with chronic low back pain: a randomized clinical trial. Chiropractic & Manual Therapies, 22(1), 41. http://doi.org/10.1186/s12998-014-0041-8
4. Haskins, R., Osmotherly, P. G., & Rivett, D. A. (2015). Diagnostic Clinical Prediction Rules for Specific Subtypes of Low Back Pain: A Systematic Review. The Journal of Orthopaedic and Sports Physical Therapy, 45(2), 61–76. http://doi.org/10.2519/jospt.2015.5723
5. Kongsted, A., Andersen, C. H., Hansen, M. M., & Hestbaek, L. (2015). Prediction of outcome in patients with low back pain – A prospective cohort study comparing clinicians' predictions with those of the Start Back Tool. Manual Therapy, 1–8. http://doi.org/10.1016/j.math.2015.06.008
6. Vavrek, D., Haas, M., Neradilek, M. B., & Polissar, N. (2015). Prediction of pain outcomes in a randomized controlled trial of dose–response of spinal manipulation for the care of chronic low back pain. BMC Musculoskelet Disord, 16(1), 2163–13. http://doi.org/10.1186/s12891-015-0632-0
7. Wong, A. Y. L., Parent, E. C., Dhillon, S. S., Prasad, N., & Kawchuk, G. N. (2015). Do Participants With Low Back Pain Who Respond to Spinal Manipulative Therapy Differ Biomechanically From Nonresponders, Untreated Controls or Asymptomatic Controls? Spine, 40(17), 1329–1337. http://doi.org/10.1097/BRS.0000000000000981