Biobehavioral Assessments in BACPAC: Recommendations, Rationale, and Methods

The Biobehavioral Working Group of BACPAC was charged to evaluate a range of psychosocial, psychophysical, and behavioral domains relevant to chronic low back pain, and recommend specific assessment tools and procedures to harmonize biobehavioral data collection across the consortium. Primary references and sources for measure selection were the Initiative on Methods, Measurement, and Pain Assessment in Clinical Trials, the Minimum Data Set from the National Institutes of Health (NIH) Research Task Force on Standards for Chronic Low Back Pain, the Patient-Reported Outcomes Measurement Information System, and NeuroQOL. The questionnaire's recommendations supplemented the NIH HEAL Common Data Elements and BACPAC Minimum Data Set. Five domains were identified for inclusion: Pain Characteristics and Qualities; Pain-Related Psychosocial/Behavioral Factors; General Psychosocial Factors; Lifestyle Choices; and Social Determinants of Health/Social Factors. The Working Group identified best practices for required and optional Quantitative Sensory Testing of psychophysical pain processing for use in BACPAC projects.

Biomechanical Phenotyping of Chronic Low Back Pain: Protocol for BACPAC

OBJECTIVE

Biomechanics represents the common final output through which all biopsychosocial constructs of back pain must pass, making it a rich target for phenotyping. To exploit this feature, several sites within the NIH Back Pain Consortium (BACPAC) have developed biomechanics measurement and phenotyping tools. The overall aims of this article were to: 1) provide a narrative review of biomechanics as a phenotyping tool; 2) describe the diverse array of tools and outcome measures that exist within BACPAC; and 3) highlight how leveraging these technologies with the other data collected within BACPAC could elucidate the relationship between biomechanics and other metrics used to characterize low back pain (LBP).

METHODS

The narrative review highlights how biomechanical outcomes can discriminate between those with and without LBP, as well as among levels of severity of LBP. It also addresses how biomechanical outcomes track with functional improvements in LBP. Additionally, we present the clinical use case for biomechanical outcome measures that can be met via emerging technologies.

RESULTS

To answer the need for measuring biomechanical performance, our "Results" section describes the spectrum of technologies that have been developed and are being used within BACPAC.

CONCLUSION AND FUTURE DIRECTIONS

The outcome measures collected by these technologies will be an integral part of longitudinal and cross-sectional studies conducted in BACPAC. Linking these measures with other biopsychosocial data collected within BACPAC increases our potential to use biomechanics as a tool for understanding the mechanisms of LBP, phenotyping unique LBP subgroups, and matching these individuals with an appropriate treatment paradigm.

The BACPAC Research Program Data Harmonization: Rationale for Data Elements and Standards

OBJECTIVE

One aim of the Back Pain Consortium (BACPAC) Research Program is to develop an integrated model of chronic low back pain that is informed by combined data from translational research and clinical trials. We describe efforts to maximize data harmonization and accessibility to facilitate Consortium-wide analyses.

METHODS

Consortium-wide working groups established harmonized data elements to be collected in all studies and developed standards for tabular and non-tabular data (e.g., imaging and omics). The BACPAC Data Portal was developed to facilitate research collaboration across the Consortium.

RESULTS

Clinical experts developed the BACPAC Minimum Dataset with required domains and outcome measures to be collected using questionnaires across projects. Other non-required domain-specific measures are collected by multiple studies. To optimize cross-study analyses, a modified data standard was developed based on the Clinical Data Interchange Standards Consortium Study Data Tabulation Model to harmonize data structures and facilitate integration of baseline characteristics, participant-reported outcomes, chronic low back pain treatments, clinical exam, functional performance, psychosocial characteristics, quantitative sensory testing, imaging and biomechanical data. Standards to accommodate the unique features of chronic low back pain data were adopted. Research units submit standardized study data to the BACPAC Data Portal, developed as a secure cloud-based central data repository and computing infrastructure for researchers to access and conduct analyses on data collected by or acquired for BACPAC.

CONCLUSIONS

BACPAC harmonization efforts and data standards serve as an innovative model for data integration that could be used as a framework for other consortia with multiple, decentralized research programs.

Toward the Identification of Distinct Phenotypes: Research Protocol for the Low Back Pain Biological, Biomechanical, and Behavioral (LB3P) Cohort Study and the BACPAC Mechanistic Research Center at the University of Pittsburgh

As a member of the Back Pain Consortium (BACPAC), the University of Pittsburgh Mechanistic Research Center's research goal is to phenotype chronic low back pain using biological, biomechanical, and behavioral domains using a prospective, observational cohort study. Data will be collected from 1,000 participants with chronic low back pain according to BACPAC-wide harmonized and study-specific protocols. Participation lasts 12 months with one required in person baseline visit, an optional second in person visit for advanced biomechanical assessment, and electronic follow ups at months 1, 2, 3, 4, 5, 6, 9, and 12 to assess low back pain status and response to prescribed treatments. Behavioral data analysis includes a battery of patient-reported outcomes, social determinants of health, quantitative sensory testing, and physical activity. Biological data analysis includes omics generated from blood, saliva, and spine tissue. Biomechanical data analysis includes a physical examination, lumbopelvic kinematics, and intervertebral kinematics. The statistical analysis includes traditional unsupervised machine learning approaches to categorize participants into groups and determine the variables that differentiate patients. Additional analysis includes the creation of a series of decision rules based on baseline measures and treatment pathways as inputs to predict clinical outcomes. The characteristics identified will contribute to future studies to assist clinicians in designing a personalized, optimal treatment approach for each patient.

Protocol for Biospecimen Collection and Analysis within the BACPAC Research Program

The Biospecimen Collection and Processing Working Group of the NIH HEAL Initiative BACPAC Research Program was charged with identifying molecular biomarkers of interest to chronic low back pain (cLBP). Having identified biomarkers of interest, the Working Group worked with the New York University Grossman School of Medicine, Center for Biospecimen Research and Development-funded by the Early Phase Pain Investigation Clinical Network Data Coordinating Center-to harmonize consortium-wide and site-specific efforts for biospecimen collection and analysis. Biospecimen collected are saliva, blood (whole, plasma, serum), urine, stool, and spine tissue (paraspinal muscle, ligamentum flavum, vertebral bone, facet cartilage, disc endplate, annulus fibrosus, or nucleus pulposus). The omics data acquisition and analyses derived from the biospecimen include genomics and epigenetics from DNA, proteomics from protein, transcriptomics from RNA, and microbiomics from 16S rRNA. These analyses contribute to the overarching goal of BACPAC to phenotype cLBP and will guide future efforts for precision medicine treatment.

Effect of Social Determinants of Health Interventions on Adults Living with Disabilities: A Scoping Review

OBJECTIVE

To investigate social determinants of health (SDoH) interventions on individual health outcomes, population health, and cost for persons in the United States over age 18 living with disabilities and receiving long-term services and supports (LTSS) in noninstitutional settings.

DATA SOURCES

A review using Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines was conducted of literature from PubMed, PsycINFO, REHABDATA, and Web of Science Core Collection published between January 1997 and July 2020.

STUDY SELECTION

Search terms were based on the primary SDoH domains identified by the Centers for Medicare and Medicaid's Accountable Health Communities Model. A total of 5082 abstracts were screened based on identification criteria of persons age 18 and above living in non-institutional, community-based settings receiving LTSS.

DATA EXTRACTION

During Level 2 review, articles were reviewed based on population focus, type of LTSS (personal assistance services, home care, adult day care, home modification, durable medical equipment, community transition services, caregiver supports and/or prevention services related to home- and community-based care), SDoH intervention and association with health outcomes, population health and/or cost. A total of 1037 abstracts underwent Level 2 review, yielding 131 publications or 1.3% for full review.

DATA SYNTHESIS

Studies (n=33) designed a priori to test outcomes of interventions were rated according to Grading Recommendations Assessment Development and Evaluation (GRADE) criteria. Qualifying articles that did not include interventions (n=98) were included in our summary of the literature but were not assessed by GRADE.

CONCLUSIONS

The preponderance of research surrounding SDoH and health outcomes has focused on older adults living with disabilities, and most interventions scored low or very low using GRADE criteria. Evidence is limited to the extent SDoH interventions are measured against outcomes for persons of all ages living with disabilities. Robust evaluation of models that feature SDoH interventions in partnership with community-based organizations is recommended as home and community-based care infrastructure expands in response to the American Rescue Plan Act of 2021.