Imagine an auditorium filled with individuals of varying ages, genders and races. They have different medical histories, educational backgrounds and incomes. But they have one thing in common: chronic lower back pain (cLBP).
How do you treat these individuals?
Clinicians agree that there is no single answer.
Despite the fact that lower back pain affects an estimated 619 million people and is a leading cause of disability worldwide, the chronic nature of the disease and the changing course of an individual’s experience of pain over time are poorly understood. Not all lower back pain has the same intensity and duration. And the pain may be caused by a wide range of factors and respond to different treatments with unpredictable varied efficacies.
Unprecedented Collaboration
The multi-faceted nature of the disease requires multi-faceted approaches, with experts from diverse fields viewing the disease through different lens.
Starting in 2019, researchers from various departments within SHRS began collaborating with colleagues from the School of Medicine and the Swanson School of Engineering in the Lower Back Pain: Biological, Biomechanical, Behavioral Phenotypes (LB3P) observational study, part of the National Institutes of Health (NIH) Back Pain Consortium (BACPAC).
The word phenotype is a scientific term for a cluster of characteristics that suggest a clinical pattern or profile. Saying that someone simply has chronic low back pain is not helpful for determining which treatment is best for that patient. This research is designed to find specific clinical patterns or profiles (phenotypes) that will help inform decision making about which treatment or treatments work best for which phenotype.
Working collaboratively, they followed 1,007 participants to observe what phenotypes, or patterns, contribute to an individual’s experience of low back pain in the hopes of being able to predict the best treatment options.
While medical doctors, surgeons, psychologists, biologists and biomedical engineers brought biological, behavioral and biomechanical expertise to the study, the team from SHRS added important clinical and biostatistical value.
“The SHRS team was really the boots on the ground,” says Michael Schneider, professor and director of the Doctor of Chiropractic program. “For the most part, we are the providers who treat patients with chronic low back pain.”
Schneider, along with Sara Piva, professor, Physical Therapy Department interim chair and director of the Physical Therapy Clinical and Translational Research Center; Charity Patterson, director of the SHRS Data Center; and Leming Zhou, associate professor and director of the Master of Science in Health Informatics program, Department of Health Information Management, served as co-investigators on the project.
Good Research Is a Team Sport
Today, thanks to a new $20 million grant from the National Institute of Arthritis and Musculoskeletal and Skin Diseases, the same team of researchers is building on their past work in a new study known as the Holistic Pain Phenotyping (H2P) that will bring them even closer to understanding the whole patient experience of pain in their joints, muscle and connective tissue.
The goal of both the original and the current study is to find the right treatment for the right patient at the right time.
“This team is committed to working together without barriers to find the most effective treatments for pain,” says Gwendolyn Sowa, chair, Department of Physical Medicine and Rehabilitation and co-principal investigator (PI).
Schneider believes this type of interprofessional collaboration could only happen at Pitt. “Pitt has extraordinary people with extensive experience who are willing to put their personal interests aside and come together to tackle a problem that each of us sees in our individual practices. It’s very unique,” says Schneider.
Researchers are divided into core groups that approach cLBP from different perspectives, comprehensively amassing an unprecedented amount of data. They collect biosamples and biomarkers, and examine movement, behavioral experiences and social determinants of health before categorizing the results into clusters of phenotypes.
They meet several times each month, either in-person or virtually, to discuss strategies, analyze data and share insights. They have come to trust one another implicitly and see each other’s contributions in a new light. What they have learned is different from any research they could possibly conduct if they stayed in their own silos.
“I have not seen this level of professional respect and enthusiasm around collaboration anywhere else,” adds Sowa.
For example, Carol Greco, associate professor emerita of psychiatry in Pitt’s School of Medicine and internationally renowned researcher, is part of the behavioral core, along with Schneider. She weighs in on psychosocial factors that improve pain and functioning in persons with chronic disease. In the biomechanical core, Kevin Bell, assistant professor in the Department of Bioengineering in the Swanson School of Engineering, brings his knowledge of wearable devices that measure joint motions and activity of individuals with cLBP to complement his colleague Assistant Professor William Anderst’s novel kinematics assessments from the Department of Orthopaedic Surgery.
Patterson serves as co-director of the informatics core for both cLBP studies along with Gina Pugliano McKernan, assistant professor, Department of Physical Medicine and Rehabilitation. Together they oversee data governance, monitoring, sharing and statistical analysis.
“Pitt fosters interdisciplinary collaboration through a culture that actively encourages and celebrates cross-disciplinary work,” says Bell. “But what stands out most is the shared commitment of my colleagues. Each brings world-class expertise and a genuine openness to learning from one another. That spirit of collaboration is what makes this work possible and what will ultimately lead to breakthroughs in care.”
The results are (almost) in! Data from the LB3P study is currently being analyzed even as the new H2P study begins. Patterson, McKernan and Zhou are using artificial intelligence and machine learning models to analyze thousands of variables. “We are using both traditional clustering methods and novel methods to best understand the diverse sample of our low back pain participants,” explains McKernan.
“In addition to identifying phenotypes, we are looking into identifying novel biomarkers that relate to the person’s experience of pain and functional outcomes in response to treatments. It’s a very exciting time for us,” says Sowa.
Groundbreaking Research
Nam Vo, professor, Department of Orthopaedic Surgery and the study’s co-PI, says LB3P was a landmark study in terms of data collection. “Never before has anyone phenotyped such a large group of participants. And now we have the opportunity to let that data inform us in a new study that follows those same participants for another five years,” explains Vo.
Piva, who led a team that recruited and retained participants for both studies, emphasizes the significance of this continuity.
“The first five years of observation allowed us to identify phenoytpe clusters,” says Piva. “Over the next five years we will see how their pain has evolves as well as how it relates to other painful conditions.”
Results of the original study will be published later this year, but Schneider says there appears to be four distinct clusters that characterize patients. “Now we continue to follow them to see what type of treatments work best for people in each cluster.”
“All of this is leading up to precision medicine—the ability to use a person’s unique biological and physical makeup, as well as their psychological dispositions,” continues Schneider. “It will take more time and additional funding, but it will eventually allow us to prescribe the most effective treatment for different individuals. And that’s what every one of us is hoping to do.”
