The study of pain has matured into a well-recognized academic discipline over the past century, gaining particular steam over the past 50 years. Hundreds of texts and thousands of peer-reviewed papers on the subject have been published, spanning basic sciences to clinical translation.
Accordingly, we need not reiterate what several authors before us have said. Instead, we will summarize the phenomenon of a pain experience in general, and as it pertains to musculoskeletal pain and associated disability on a patient-by-patient basis more specifically.
When we say pain is a “latent construct” in the title to this essay, we are referring to the idea that pain, like love, jealousy, anger, or depression, cannot be directly observed or quantified. We can see behaviors that appear consistent with those experiences, but even then, what we think of as consistent with someone in love or in pain is very likely different between people. So instead clinicians are forced to rely on a series of well-crafted questions (a history) and recognized clinical tests to attempt to approximate the true experience.
So by saying pain is a latent construct, we are saying that we can observe things that are influenced by the real experience of pain, but we cannot observe the pain directly.
To that end, there have been many prior methods and protocols intended to help clinicians understand or “subgroup” the type of pain a patient is experiencing. These are usually distinct classifications, like nociceptive, neuropathic, or central nociplastic. However, as we describe in our book “Musculoskeletal Pain: Assessment, Prediction Treatment,” we have proposed a different type of protocol for “phenotyping” rather than distinct and isolated classifications of pain.
The concept of Assess, Predict, and Treat (APT) doesn’t sound particularly innovative at first mention. In fact, it should seem like a common-sense approach to managing your patient’s reported complaints or observed “problems,” which is exactly what it is.
Conduct a sound, comprehensive, critically informed and clinically relevant assessment of the patient (including documented history, subjective narrative, consider results from a wide variety of available clinical tools and objective clinical tests); use those findings to predict the likelihood that the patient will: a) improve on their own, or b) respond to a particular treatment; and then treat them according to your assessment and prediction. [Editor’s note: massage therapists cannot by law diagnose any medical condition and may refer a client to an appropriately credential medical professional when necessary.]
However, while it seems like common sense, our experience is that many clinicians find this reasoning process and application difficult. Perhaps this is not surprising, especially as we contrast what is increasingly known about the experiences of pain and disability (and the biopsychosocial complexities thereof) and what the more biomedical approaches commonly prioritize in healthcare training programs.
In order to properly implement an APT framework, clinicians must first possess considerable knowledge on the choice, application and interpretation of clinical assessment tools (including effective interview skills, use and interpretation of standardized self-report tools, and clinician-administered clinical tests), be able to combine and use those findings to mentally construct a multidimensional profile of the patient, identify important patterns in their presentation, have an up-to-date working knowledge on the prognostic and theranostic utility of a variety of clinical variables (assuming the evidence exists), and then be able to match treatment decisions to what is often a very complex clinical picture.
Even the most seasoned clinicians have difficulty applying these principles in a coherent and logical way, often falling back on early-career training, heuristics, and intuition when choosing the “best course” of action for their patients.
The APT framework has been developed to make these processes more manageable for the busy clinician. It won’t take all of the intuition or “gut feeling” type of work out of a clinical encounter, nor will we ignore the value of that previous experience or clinician intuition.
Further, no clinical framework will be right 100% of the time and your own reasoning will remain an important component of any clinical interaction and decision. By choosing and applying a select set of meaningful clinical evaluation techniques, using the results to create a visual representation of the patient’s profile, and comparing that to what is currently known about prognosis, theranosis, and treatment options, APT can make a messy and complex picture more interpretable and lead to treatment decisions that are rational, justified, and easily adaptable to the patient’s response (or lack thereof).
It is not a new method of classification or subgrouping of patients into distinct categories. In fact, the concept of creating a multidimensional profile represents a departure from the practice of creating distinct clinical subgroups that was a primary focus of research in the musculoskeletal pain field through much of the 2000s and early 2010s. We believe a departure is necessary as subgrouping assumes humans can and do fall neatly into distinct categories, but as clinicians will know, this is rarely the simple case.
Many classifications in the ”clinical prediction rule” types of approaches involved identifying the presence (and sometimes magnitude) of three, four, or more signs or symptoms to consider a patient “positive” on the scale. Anecdotally this is problematic as many patients may satisfy some of the criteria but not all, leaving clinicians with more confusion than clarity about how to defend their treatment choices.
Moreover, as readers get more comfortable in a critical realist thinking paradigm, it should be noted that the creation of subgroups is dependent on the variables that a particular research group chose to capture (how that research group chose to sample the “empirical reality”). As a result, the rules of classification algorithms are necessarily limited to those variables that group of researchers thought were important while excluding all others.
Here again, treatment decisions must be made on the basis of a combination of evidence, your own clinical experience/intuition, and the values and expectations of the patient in front of you.
Copyright© Handspring Publishing 2020; reproduced with permission.
Dave Walton, PhD, is an associate professor with the School of Physical Therapy at Western University in Ontario, Canada. Other affiliations include associate scientist with the Lawson Health Research Institute, director of the Pain and Quality of Life Integrative Research Lab, and a member of the international steering committee for the 2018 Global Year for Excellence in Pain Education.
Jim Elliott, PhD, is the acting executive director of the Kolling Research Institute and a professor in the Faculty of Medicine and Health at the University of Sydney and the Northern Sydney Local Health District. He is an adjunct professor in the Feinberg School of Medicine at Northwestern University in Chicago, USA and serves as an advisory board member for the journal Spine.
As a special offer to MASSAGE Magazine readers, order “Musculoskeletal Pain – Assessment, Prediction and Treatment: A Pragmatic Approach” direct from Handspring Publishing and save 20 percent off the list price. Order direct and use discount code MMMPAP21. Offer expires Oct. 31, 2021. Free shipping to US and UK addresses.
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