Musculoskeletal Pain - Assessment, Prediction and Treatment

David Walton, James Elliott

• Publisher: Handspring Publishing
• Year: 2020
• ISBN:
  9781912085514
• Edition: 1
• Topic: Medizin
• Subtopic: Physiotherapie, Physikalische & Rehabilitative Medizin

Musculoskeletal Pain - Assessment, Prediction and Treatment presents a common sense approach to interpreting and applying existing clinical knowledge and new research to help clinicians make sense of the complex phenomena of acute and chronic post-traumatic musculoskeletal pain. Built upon the Assess, Predict, Treat framework, the authors offer a method to help clinicians better understand their patients' pain. They present evidence-based decision tools to predict the natural and clinical course of common conditions, such as neck and low back pain, and they then synthesize that information into a logical, integrated treatment approach, which respects the individuality of the patient, the experiences of the clinician, and the value of evidence-informed practice.

David Walton and James Elliott are leaders in the field of post-traumatic pain and recovery. Their work provides a valuable framework to facilitate novice clinicians in their transition towards experts and helps mid- and late-stage clinicians better interpret, synthesize, and discuss complex information on pain with the goal of optimised outcomes for patients.

1

A Pragmatic Approach to Seeing the Invisible

Introduction to this book

This book has been written as an introductory guide to a new framework for thinking about pain and its related sequelae. We have collated several decades’ worth of collective work in the fields of pain measurement, mechanisms, and management as clinicians, educators and researchers, to arrive at this framework. The first few chapters will introduce the Assess, Predict, Treat (APT) paradigms, including the concepts of the radar plot and triangulation, and provide readers who are new to the field with a bit of a ‘crash course’ on pain and some of the controversies and ambiguities surrounding it. The second half of this book will delve deeper into each of the primary pain drivers of our radar plot concept. Each of these chapters is structured similarly, starting with a description of the mechanism itself and some background into how each may influence a patient’s experience or report of pain. These are followed by suggestions for how to identify each domain as a likely/not likely pain driver (Assess), including a table that roughly indicates the shift in certainty that a domain is/is not an important driver for that patient. Then we present current evidence that can be used to predict either the natural course, or the outcomes of treatment in a patient who presents with that domain as a strong driver (Predict). Finally, we close each of those chapters with discussion on current evidence, or when that does not exist, recommendations from our own experiences, as ways to improve the experiences of pain for a patient with that domain as a primary driver (Treat). Of note, the intervention sections are often the least rich, largely because, despite decades of research, the evidence is simply not strong in a lot of these areas. We would argue that part of the reason for that is that prior researchers have not attempted to recruit patients according to a structured phenotyping framework like we are proposing here (i.e. instead all patients are often taken as though they are considered equal). One of our hopes by presenting the APT framework is that researchers and clinicians can use them to make more informed (or ‘apt’ – get it?) decisions about people in pain, and in doing so will lead to research with greater clinical impact.

With that, we start by exploring the construct of pain and the challenges associated with research and clinical intervention for a largely invisible experience.

Pain as a latent construct

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. As a result, and in the interest of providing context for several of the following sections, 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. For those interested, some of our favorite and seminal texts, which have shaped our own clinical curiosities and research on the mysterious phenomenon of pain, are listed in Box 1.1.

Box 1.1 Selected additional readings

Butler, D., Moseley, L., 2013. Explain Pain, 2nd edition, NOI Group.

Caudill, M., 2016. Managing Pain Before It Manages You, 4th edition , The Guilford Press.

Jackson, M., 2003. Pain: The Science and Culture of Why We Hurt, Vintage Canada.

Melzack, R., Wall, P.D., 1996. The Challenge of Pain, 2nd edition, Penguin Books.

Moseley, L., 2008. Painful Yarns, Dancing Giraffe Press.

Turk, D., Melzack, R., 2010. The Handbook of Pain Assessment, 3rd edition, The Guilford Press.

To understand pain, and to understand the value of subsequent concepts like triangulation and phenotyping (described in later chapters), we must first appreciate pain as a latent construct; it cannot be directly observed. At best the experience of another person’s pain can be inferred from different but related variables or phenomena. That is to say, it would be (nearly) impossible for you to know precisely how much pain someone is experiencing by virtue of looking at them, studying their x-ray images, or reading about their past medical history. However, each of those sources of information (asking the patient, looking at diagnostic images, understanding past history) together will give you a sense of what the patient might be feeling. We will always be trying to make sense of a patient’s experience through the lens of our own experiences with pain, life, and diagnostic procedures, meaning there is currently no objective, measurable, gold-standard diagnostic marker or markers of pain any more than there are objective markers of love, happiness, guilt, religious fervor, etc. All of those phenomena may be estimated or inferred based on your knowledge of the person, their personal and cultural beliefs and values, their prior experiences, current behaviors, and broader contextual factors. Such estimations are imperfect at best. Someone slowly swaying back and forth with their eyes closed may indicate a person listening to a favorite song, entranced in a deep meditative state, or experiencing a bout of syncope – same behaviors but very different experiences. Similarly, physiological markers cannot provide concrete infallible evidence of someone’s current experience: dilated pupils, elevated heart rate, and rapid breathing could indicate a person in a state of abject fear as easily as it could be a person in the throes of sexual passion.

Even advancing measurement technologies, such as functional magnetic resonance imaging (fMRI, Box 1.2), are an inexact estimation of whether someone is in pain, let alone how much pain they are in. This is true in most cases, though recent research has indicated that by combining fMRI data with computerized machine learning, it may be possible to train an artificial intelligence (AI) to predict the intensity of a person’s pain experience if the AI is first fed hundreds of scans of that person’s brain activity under ‘control’ (known pain intensity) conditions. Armed with such a background library of a person’s unique neurosignatures of pain (activation patterns unique to you), the AI may then become fairly accurate in predicting the current experiences by virtue of an existing library of past experiences. But without such a rich library of data to draw upon, fMRI or other related neural scanning techniques are imperfect at best, and potentially dangerously inaccurate at worst. Therefore, despite decades of research searching for a concrete, objective and unbiased ‘pain-o-meter’, we find ourselves forced to continue to accept the eloquent declaration of Margo McCaffery in 1968: “Pain is whatever the experiencing person says it is, existing whenever the experiencing person says it does”. Despite a lot of hype and a bit of promise, we’ve yet to find a better solution than that.

Box 1.2 Functional magnetic resonance imaging

Functional magnetic resonance imaging (fMRI) is a research tool that uses long-duration serial images of regional cerebral blood flow (rCBF) in an awake or sleeping person or animal. The theory posits that brain regions that are neurologically (electrically) active are also more metabolically active and consume more oxygen. As such, the body’s circulatory system responds by constricting blood flow to inactive areas so it can shunt additional blood (and hence oxygen) to the active areas. Assuming the person is otherwise perfectly still, changes in fMRI water signal are interpreted as increased cortical perfusion in that region, hence more activity. This is a technique that provides excellent spatial resolution (can pinpoint activity to very discrete brain regions), but because changes in circulatory dynamics take some time, offers poor temporal resolution (a delay between actual brain activity and the first indication of changed regional perfusion on fMRI). It is a powerful tool when in the right hands and used under very stringent protocols. When protocols are lax however, it may lead researchers to believe that a dead Atlantic salmon is interpreting pictures of human emotions (Bennett et al., 2009).

All of this poses a challenge to clinicians wanting to benchmark their clinical outcomes in treating patients with pain, in that there is no gold standard against which to compare. The same could be said for other latent constructs such as self-rated disability, distress, fear or anxiety; all of which can go ‘hand-in-hand’ with reported levels of pain. Such a conundrum also exists for the medicolegal system, that for decades has wrestled with how best to determine the verifiability of a complainant’s reports of pain and suffering in the absence of any objective markers. A common legal scenario, either within or beyond the courtroom, is one in which both sets of attorneys nominate their respective medical ‘experts’ who, after having reviewed hours-to-days’ worth of reports and records, each provide their best opinion as to whether it is more (or less) likely than not that the complainant presents with genuine pain. In most cases those opinions differ considerably, leaving a largely uninformed judge or jury to decide whose opinion best supports or refutes the ‘but-for’ test, which considers whether the plaintiff’s complaints or injuries would not have occurred ‘but-for’ the defendant’s negligent act. Often, court decisions and their impacts on people’s lives come down to which side’s expert the jury likes best.

This also poses several problems for patients who are suffering from what is often considered an invisible experience of pain. Pain, especially chronic pain, is somewhat unique as a medical condition in that it is one of the few conditions that leaves sufferers genuinely disappointed when a diagnostic test comes back negative. We’ve seen this scenario play out countless times in our clinical practices, when a long-suffering patient presents for a visit, clearly dejected that their last set of x-rays or MRI showed ‘nothing’ to explain their persistent pain. It is plausible such disappointment is largely driven by their interpretation of being the targets of skepticism when describing their pain to clinicians, employers, or even family members. The desire for an observable explanatory sign, something they can point to and say ‘See! I told you this wasn’t right!’ is powerful in most global cultures (Rhodes et al., 1999). This lack of clear structural pathology also appears to cause considerable tension and cognitive dissonance for healthcare providers, many of whom were trained in a biomedical model that prioritized identification of problematic tissues. The experience of chronic pain, and the stigma experienced by those who express it, has been described in several research studies and appears to compound the suffering of those who live in pain and struggle to find legitimacy (Slade et al., 2009; Cohen et al., 2011). Some studies have even found evidence that patients begin to doubt themselves and their ability to distinguish between normal and abnormal states of health after so many experiences with providers who, according to the patient, question or do not believe they are in pain. The public discourse around the opioid crisis that started in earnest near the end of 2016 only added to the problems of people in pain. Not only had they long been the target of skepticism from cynical healthcare providers who saw them as ‘complainers’, but suddenly they now found themselves labeled as ‘drug seekers’ or even addicts any time they requested pharmaceutical management for their pain.

The search for concrete objective pain markers has therefore been a priority area of research for years. For example, the United States Department of Defense holds a large granting competition each year to fund research specifically focused on identifying biomarkers of pain. Despite some advancements in the field, driven largely by fMRI (Fig. 1.1), genomics, transcriptomics, proteomics and big data analytical techniques, it still seems we are far from accessible, and affordable, objective biomarkers of pain. And the field has yet to reconcile the fact that the search for all such markers continues to be inextricably linked with comparisons against the person’s subjective report of pain – that is, the verbal report of pain remains as close to a ‘gold standard’ as we currently have even with all of the caveats, limitations, and biases inherent in verbal self-report. We do not see how the field will ever reconcile this tension – that despite technology that is exponentially advancing, the study of pain will always rely on what the patient (or research subject) says they are experiencing. Here we lean on the words of Harvard psychologist Daniel Gilbert, author of Stumbling on Happiness, when he says:

Figure 1.1

Example of structural, diffusion, and functional brain images. The axial structural image was acquired using a 3-D magnetization-prepared rapid gradient-echo, T1-weighted, gradient-echo sequence and can provide morphometric properties of the gray matter. The diffusion example shows a 3-D tractography map using the right ventral posterolateral nucleus of the thalamus as a seed. The axial functional images show average group activation from an acute thermal pain stimulus applied to the lower back, and group average connectivity to the bilateral posterior cingulate cortices (light blue). A, Anterior; I, inferior; L, left; P, posterior; R, right; S, superior. (Reprinted with permission from Crawford, R.J., Fortin, M., Weber, K.A. 2nd, et al., 2019. Are magnetic resonance imaging technologies crucial to our understanding of spinal conditions? Journal of Orthopaedic & Sports Physical Therapy 49:320–9. ©Journal of Orthopaedic & Sports Physical Therapy®)

If we want to know how a person feels, we must begin by acknowledging the fact that there is one and only one observer stationed at the critical point of view. She may not always remember what she felt before, and she may not always be aware of what she is feeling right now. We may be puzzled by her reports, skeptical of her memory, and worried about her ability to use language as we do. But when all our hand wringing is over, we must admit that she is the only person who has even the slightest chance of describing ‘the view from in here’, which is why her claims serve as the gold standard against which all other measures are measured. (Gilbert, 2006, pp. 72–73)

If we do know anything about pain, it’s that the experience and communication thereof is tremendously complex. It has become clear over the past 20 years or so that pain cannot be reduced to a single structural abnormality. A growing collection of research studies has found that abnormalities on diagn...

Table of contents

• Cover
• Title Page
• Copyright
• Contents
• Dedication
• About the Authors
• Preface
• Foreword by Guy Simoneau
• Foreword by Wellington K. Hsu
• 1. A Pragmatic Approach to Seeing the Invisible
• 2. The Assess, Predict, Treat Framework
• 3. A New Framework for Clinical Assessment of Musculoskeletal Pain
• 4. Classifications of Patients that Matter when Interpreting Pain
• 5. Understanding Prognosis, or ‘How to Predict the Future’
• 6. Creating Your ‘Go-To’ Toolbox
• 7. The Physiological Nociceptive Domain
• 8. The Neuropathic Domain
• 9. The Central Nociplastic Domain
• 10. The Cognitive Domain
• 11. The Emotional Domain
• 12. The Socioenvironmental Domain
• 13. The Sensorimotor Dysintegration Domain
• 14. Case Examples
• Index