“It’s important to understand that pain never comes in a pure form. It comes with suffering, malaise, loss of appetite, the feeling of not being well, or, in the longer term, depression, helplessness, or negative thoughts. That’s true for both body pain and migraine. So when you study pain, you have to contend with all these variables that come along with the pain.”
Carl Saab, MS, MA, PhD, is director of the Pain Science Technology and Research (STAR) lab in the Department of Biomedical Engineering at Cleveland Clinic, and an adjunct professor at Brown University. His research objective is to develop, test, and validate novel technologies and methods to diagnose and treat chronic pain by fostering collaboration between clinicians and researchers.
Here, Saab speaks with freelance writer Kayt Sukel about the challenges of developing an objective pain diagnostic – including one for migraine – the nature of pain, and his vision for the future of migraine research and treatment, which includes a promising role for artificial intelligence and machine learning.
What inspired you to study chronic pain?
I’ve always been fascinated by the relationship or perceived dichotomy between the body and the mind or soul. Pain forces us to make the transition between physiology and consciousness. It’s the result of what we believe is the activation of physiological sensors, but, when pain is reported by the subject, it stops being physiological. You cross into the world of emotions, cognition, consciousness, memory, expectations, and all of that. It forces us to think about the person in a holistic way.
Unfortunately, going back to the 1950s and ’60s, the predominant view of studying and treating pain has been purely physiological. The challenge is how to reconcile these different elements so that we can help the patients we see every day in the clinic. We need to understand that they don’t only suffer from activation of sensors, but they suffer emotionally. I think we still have a long way to go to bridge this gap in Western medicine.
One of the things you’ve been working on is an objective pain diagnostic. What are some of the biggest challenges involved?
The first challenge is the philosophical conundrum of pain. If it’s subjective, you cannot objectify it.
But, going further, there are also some deep challenges, starting with the healthcare system. When we started this work, we were told by clinicians to get rid of the pain scale, those little face charts on the wall in the emergency department or examining room. They wanted us to bring forth an objective test, like a blood test, for pain. The scientific community has tried to do it – we’ve been on the hunt for a biomarker that could be objective, standardized, and universal. Some of us were even making some progress, with accuracy levels of 80% to 85%.
At that point, the healthcare system reversed its position. It said it didn’t want to replace the pain scale; it wanted to augment it. The faces had been a standard for ages, and it would be too hard to replace them. The debate then became how to augment the scale. If we have an objective test that contradicts the smiley face, which one will the doctor choose? We haven’t resolved this issue yet.
At the core of the problem is what we define as the ground truth for pain. Is pain what the patient reports? Or is it what we can read from the person’s brain? Until the healthcare system resolves what it wants from scientists in this regard, it’s hard to go forward.
What other challenges are there?
There are also challenges on a scientific level. There isn’t one single pain circuit; there isn’t one network. There isn’t even one stimulus that will always give you the same response. There are multiple networks involved in the experience of pain, and they all influence each other in different ways. And that means I can stimulate a person with the same painful stimulus over and over again and get a different brain response every time.
Because the brain is plastic, it can and does change its response. We learn with each experience of a painful stimulus, and that learning process evolves over time. That’s why you can start with an acute pain, but after three, four, or 10 years, that pain becomes chronic. The physiology of the whole network changes. That’s another challenge.
We also need to consider individual differences. What do we mean by a brain network? What do we mean by a typical pain response? Both differ from individual to individual. The same diagnostic may not be measuring the same thing in two different people. It is all exceedingly complex and hard to nail down in a way that you can generalize a single objective measure that works for everyone.
What about differences in pain conditions? How might an objective pain diagnostic for migraine differ from one for orthopedic pain, for example? Are there different “signatures” for different diseases?
We don’t know. Part of the reason we don’t know is that people haven’t started to look carefully enough.
Migraine can occur with and without pain. When pain occurs, there are different kinds. Some people report throbbing pain, others shooting pain. Some people have visual auras that they refer to as “silent pain” because, after the episode is over, they feel fatigued as if they’ve had a lot of pain. When you ask people with migraine pain about their experience, some will even say they aren’t sure if they have pain or not but go on to tell you they feel very sick and nauseated.
When we speak of pain, we have to be careful about what condition we’re speaking about. In terms of migraine, I cannot point to a commonality in the science between it and somatic pain, and that’s been a real challenge because, too often, when we think of pain, we simply transpose what we know about body pain into the field of migraine. That may not be the right approach, and I believe it is causing some confusion in the field.
What is the path forward?
At some point, the scientists who study chronic pain, somatic pain, migraine, and headache will have to come together and go through all of the findings and see what actually translates between the conditions and why. Once we start having these discussions, these fields will be enriched by illuminating all the pain mechanisms that are similar and/or dissimilar.
It’s important to understand that pain never comes in a pure form. It comes with suffering, malaise, loss of appetite, the feeling of not being well, or, in the longer term, depression, helplessness, or negative thoughts. That’s true for both body pain and migraine. So when you study pain, you have to contend with all these variables that come along with the pain. You have to dissect them and understand how they manifest as part of the whole experience. That’s yet another challenge for the field, because, when we report our findings, we don’t often control for all these variables. There are just too many.
We see this in science but also in clinical trials. When a new drug comes on board, pharmaceutical companies try to be as careful as possible in categorizing and phenotyping patients to make sure they align with the therapy. But since pain never comes in a pure form, it is hard to know if you are treating the pain itself or something else. Maybe you are treating the depression or some other aspect of pain. Let’s face it: Many of the pain medications on the market for somatic pain are not specific to pain. They are SSRIs [selective serotonin reuptake inhibitors] for depression or seizure-attenuating drugs that happen to work for pain. In the case of migraine, we have the CGRP [calcitonin gene-related peptide] antagonists, and the science is still trying to catch up and explain how they work.
Taking the time to understand how all of these things come together and how the treatments offer relief is what is going to get us closer to developing the next blockbuster treatment.
Given those gaps, what do you hope to see in migraine research and treatment in the future?
First, on the biomarker/diagnostic side, we need ways to better assess migraine. We can’t just give patients questionnaires; we need assessments that are more objective. I’m not saying we need to objectivize pain, but we need to objectivize the state that patients are in. And we can use a lot of data from watches and Fitbits to do that. Those devices can monitor a patient’s behavior at home, how sedentary they are, and how migraine affects their function, and those data can give us a more comprehensive assessment of a patient’s lifestyle and how migraine impacts it.
The next step is to line up patients with the right drug or other treatment. I would like to better understand the mechanisms behind migraine so we can tailor therapies. We don’t have to exclusively use drugs for treatment; behavioral therapies and group therapies are also beneficial. Taking a more community-based approach to migraine is of benefit since individuals don’t live in silos; they live within families and communities.
We need more understanding of the pain that they go through and to try to tease apart all the layers and variables involved with that pain. Even if you can’t cure the sensory component of pain, you can make people’s lives significantly better by addressing other problems and treating the patients as holistic human beings.
What else do you look forward to in the field?
Another exciting development for me, given the increasing complexity of neuroscience and the increasing complexity of the data we collect about patients, is machine learning and artificial intelligence [AI]. These methods can help us identify new molecules and proteins for study, and then we can develop new targets for treatment. These algorithms are our allies in augmenting and accelerating the science of what we do.
Most patients who walk through the door have lived with migraine for the majority of their lives. There is no human clinician who can go through all of a patient’s medical files and data to get a perfect assessment of the patient’s whole story in 20 minutes or less. But AI can go through all of that information, group and stratify different aspects of it, and then give notifications to healthcare professionals about cross-references to viral infections, abdominal pain or other adjacencies to migraine. These things are often next to impossible to detect just using pen and paper. AI can identify things we might not otherwise see.
That’s what keeps me excited about the next five to 10 years – the possibility of using these tools to discover something completely out of the box with the power to move science along and, as a consequence, help patients living with migraine.
Kayt Sukel is a freelance writer based outside Houston, Texas.
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
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