Funded Projects

Chronic pain is a debilitating medical condition that affects roughly 50 million people in the United States. Current diagnostics and treatments rely primarily on subjective metrics and do not target the unique biological, psychological, and social factors that contribute to an individual’s pain. This project is part of the NIH Back Pain Consortium (BACPAC) program, a patient-centered effort to address the need for effective and personalized therapies for chronic low back pain. This research will enhance patient diversity within the BACPAC research participant population.

The University of California, San Francisco, as part of the Back Pain Consortium (BACPAC) Research Program, has established a Core Center for Patient-centric Mechanistic Phenotyping in Chronic Low Back Pain (REACH). The main goal of REACH is to define different subtypes (phenotypes) of chronic low back pain as well as to identify underlying pain mechanisms that can lead to effective, personalized treatments for patients across all population subgroups. To achieve this goal, REACH is, or will be, participating in several clinical trials, and it is imperative that the patients participating in these trials reflect the diversity of the U.S. population. Therefore, this project seeks to adapt methods that have successfully improved minority participation in other settings as well as to develop and deploy digital strategies that can promote recruitment and engagement of patients from marginalized populations.

The NIH Back Pain Consortium (BACPAC) Research Program brings together leading centers with expertise in studying and treating chronic low back pain to advance understanding of the mechanisms that underlie the condition and to identify novel treatment strategies. BACPAC is undertaking a multisite precision medicine clinical trial taking into account patient-specific information to understand which patients with chronic low back pain respond best to various nonopioid, evidence-based treatments. The trial seeks to enroll a racially, ethnically, and socioeconomically diverse patient population to ensure that the results are applicable to all Americans with chronic low back pain. This project aims to develop comprehensive recruitment and retention plans for study sites that can recruit from historically underrepresented populations in clinical research (e.g., Black and Hispanic populations) and to provide dedicated financial resources to engage patients from these populations using tailored, culturally appropriate strategies.

Chronic overlapping pain conditions represent up to half of all chronic pain cases and can be more debilitating than other forms of chronic pain. These conditions include but are not limited to the following: temporomandibular disorders, fibromyalgia, irritable bowel syndrome, vulvodynia, interstitial cystitis/painful bladder syndrome, painful endometriosis, chronic tension type headache, migraine headache, chronic low back pain, and chronic fatigue syndrome. Common neurobiological mechanisms have been suspected to account for the overlap between these conditions, but until recently it has been difficult to efficiently classify each condition within individual patients. A digital classification tool for clinicians has been developed for this purpose, but access to the tool remains limited. Here we propose converting this chronic overlapping pain conditions classification tool into a common web-based application format.

There are numerous non-pharmacological interventions for chronic low back pain, yet no treatment is invariably effective for all. Understanding patient characteristics that predict differential responses to these non-pharmacological interventions will allow for tailored treatments to maximize positive patient impact. This supplement supports a training experience for an individual in clinical pain research, including exploring differential response to psychotherapeutic interventions. The aim of the project is to provide an extensive systematic literature review examining baseline phenotypic factors that predict differential responsiveness to the some of the most commonly used psychotherapeutic interventions for chronic low back pain.

Despite the significance of spine disorders, there are few reliable methods to determine appropriate patient care and evaluate intervention effectiveness. The research and tool development take the critical next step in the clinical translation of faster, quantitative magnetic resonance imaging (MR) of patients with lower back pain. The multidisciplinary Technology Research Site (Tech Site) of BACPAC will develop Phase IV (i.e., technology optimization) technologies and/or methods (TTMs) to leverage two key technical advancements: development of machine learning-based, faster MR acquisition methods and machine learning for image segmentation and extraction of objective disease related features from images. The team will develop, validate, and deploy end-to-end deep learning-based technologies (TTMs) for accelerated image reconstruction, tissue segmentation, and detection of spinal degeneration to facilitate automated, robust assessment of structure-function relationships between spine characteristics, neurocognitive pain response, and patient-reported outcomes.

Chronic low back pain (cLBP) is recurrent and often nonresponsive to conservative treatments. Biomechanists, physical therapists, and surgeons each utilize a variety of tools and techniques to assess and interpret qualitative movement changes to understand potential mechanical and neurological sources of low back pain and as critical elements in their treatment paradigm. However, objectively characterizing and communicating this information is currently impossible, since clinically feasible (i.e., cost-effective, objective, and accurate) tools and quantitative benchmarks do not exist. This research addresses the challenge to improve cLBP outcomes through the use of unique, inexpensive, screen-printable, elastomer-based, nanocomposite, piezoresponsive sensors, which will be integrated into a SPInal Nanosensor Environment (SPINE) sense system to measure lumbar kinematics and provide an objective, quantitative platform for diagnosis, monitoring, and follow-up assessment of cLBP.

The BACPAC Research Program’s Data Integration, Algorithm Development, and Operations Management Center (DAC) will bring cohesion to research performed by the participating Mechanistic Research Centers, Technology Research Sites, and Phase 2 Clinical Trials Centers. DAC Investigators will share their vision and provide scientific leadership and organizational support to the BACPAC Consortium. The research plan consists of supporting design and conduct of clinical trials with precision interventions that focus on identifying the best treatments for individual patients. The DAC will enhance collaboration and research progress with experienced leadership, innovative design and analysis methodologies, comprehensive research operations support, a state-of-the-art data management and integration system, and superior administrative support. This integrated structure will set the stage for technology assessments, solicitation of patient input and utilities, and the evaluation of high-impact interventions through the innovative design and sound execution of clinical trials, leading to effective personalized treatment approaches for patients with chronic lower back pain.

Current diagnostics and treatments of chronic low back pain (cLBP) rely primarily on subjective metrics and do not target all the multidimensional biopsychosocial mechanisms. This multidisciplinary effort will develop and validate a digital health platform and provide meaningful data-driven metrics that enable an integrated approach to clinical evaluation and treatment of cLBP. This platform will facilitate the use of quantitative spinal motion metrics (function), patient-reported outcomes, and patient preference information to enable deep patient phenotyping and inform clinical decision making on personalized treatments in order to improve outcomes. This effort will involve software and hardware development to enable data collection, analysis, and visualization in clinical settings. The outcome of this project will be a digital health platform with data to support regulatory submission for clinical use. At the end of this effort, the researchers will have a validated tool for integration in clinical research studies supported by the BACPAC Consortium.

The University of Pittsburgh Low Back Pain: Biological, Biomechanical, and Behavioral Phenotypes (LB3P) Mechanistic Research Center (MRC) will to perform in-depth phenotyping of patients with chronic low back pain (cLBP), using a multimodal approach to characterize patients and provide insight into the phenotypes associated with experience of cLBP to direct targeted and improved treatments. The LB3P MRC will be formed of three Research Cores, three support cores, and one research project. This approach will leverage and integrate distinctive resources at the University of Pittsburgh laboratories to deliver quantified biomechanical, biological, and behavioral characteristics; functional assessments; and patient-reported outcomes, coupled with advanced data analytics using a novel Network Phenotyping Strategy (NPS). By eliminating isolated and disconnected approaches to treatment and focusing on personalized patient-centric approaches, this approach will yield improved outcomes and patient satisfaction.

The chronic low back pain (cLBP) subgroup with comorbid depression or anxiety disorders, known as high negative affect (NA), needs better non-opioid, comprehensive pain treatment options. Data shows that the combination of antidepressants (AD) and fear avoidance physical therapy is more efficacious at improving pain, function, depression, and anxiety in cLBP patients with high NA than each treatment alone or a control condition. Research also finds that an enhanced fear avoidance rehabilitation protocol (EFAR; fear avoidance-based physical therapy, pain education, and motivational messaging) further improves outcomes. To address the unmet needs of cLBP patients with high NA, this study will test in a randomized trial whether the combination of AD and EFAR is more effective than each treatment alone at relieving pain, improving function, combating depression, and preventing opioid misuse. This multimodal combination approach of pharmacotherapy and behavioral therapy is novel to the field and has the potential to shift current treatment paradigms.

This project will examine the association between end plate pathology and chronic low back pain (cLBP) and improve patient selection by developing and translating new imaging tools, technologies, and/or methods (iTTM) that provide accurate, noninvasive measures of end plate pathologies. A search for clinically relevant biomarkers of end plate pathology will focus on novel imaging measures of end plate bone marrow lesion (BML) severity with IDEAL MRI and cartilage endplate (CEP) fibrosis/damage with UTE MRI, assess interactions with paraspinal muscles, and identify metrics that associate with pain, disability, and degeneration. The research will refine imaging and post-processing methodologies by leveraging and expanding existing cross-sectional cohorts and then deploy and validate the new end plate iTTM to other BACPAC sites to test the most promising metrics’ clinical utility. These studies will provide validated iTTM that are useful for addressing the end plates pathology’s role in cLBP, identifying sub-phenotypes, discovering pain mechanisms, uncovering treatment targets, and selecting patients.