Network Modeling in Knee Osteoarthritis

Living with chronic musculoskeletal pain (cMSKP) is a complex, uniquely individual, day-to-day experience(1). Although the biopsychosocial nature of chronic pain is well documented, current management of cMSKP is focused on treating specific diagnoses and/or regional symptoms. There is no guidance on interpreting the relative contributions of many different biopsychosocial factors to the burden of pain for each individual. Thus, people living with cMSKP, many with co-occurring conditions, often receive fragmented healthcare, endure inappropriate diagnoses, and undergo expensive and invasive interventions, without long-term improvement.

Our goal in this proposal is to understand modifiable factors that determine inter-individual differences in the pain and functional trajectory of cMSKP. The investigators hypothesize that inter-individual differences emerge from a dynamic interplay of symptoms across multiple domains, including psychosocial, biomechanical and physiological(2). Conventional approaches study these domains separately. The investigators propose to study them as an interconnected system using time-varying network models. A network model(3) consists of nodes (variables representing system components) and edges (pairwise conditional relationships). The investigators will study whether clinically-relevant transitions in pain and functional states are determined by patterns of network connectivity, and analyze time-varying networks to identify critical pathways and personalized pain management strategies.

The investigators will use knee osteoarthritis (KOA) as a case study to test our hypothesis. KOA is a leading cause of pain-related disability(4). There is strong evidence that pain and pain-related functional limitations are not directly related to the severity of knee pathology(5-11) and psychosocial factors are an important contributor(7,10). To understand how psychosocial, biomechanical and physiological factors contribute to inter-individual differences in the lived experience of knee pain, the investigators will collect data from a prospective longitudinal observational study of adults with KOA recruited from community-based physical therapy (PT) clinics. Repeated measures performed every 3 months for a year will include patient narratives, questionnaires, clinical and functional exams, allostatic load measures, and quantitative sensory testing. Ecological momentary assessments will describe pain level, catastrophizing, function, and brief narrative reports for each day (in 5 waves), and pain and biomechanical function for each week. The investigators will develop patient-specific models to assess how pain management (PT for knee pain, or PT after TKA), and co-occuring conditions (knee pain alone or knee pain co-occuring with hip and/or low back pain) impacts individualized experiences of pain. Our specific aims will investigate:

Aim 1: Network Representations of Lived Experience: The investigators will utilize large language models (LLMs) to analyze patient narratives and create patient-specific networks utilizing their own knowledge and insight about their pain experience and overall health. The investigators will (1) Refine a protocol for conducting narrative interviews to solicit standardized, comprehensive narratives; (2) Create an annotation schema to map patient narratives to a structured, semantic representation informed by the International Classification of Function (ICF); (3) Develop and evaluate LLM-based models for automatically generating interpretable, fine-grained representations of patient pain narratives; (4) Validate these representations with clinicians and patients and (5) Connect patient narratives over time to identify change with changing clinical states.

Aim 2: Biomechanical Networks of Physical Function: The investigators will analyze the sit-to-stand (STS) task, a key predictor of function, to develop whole-body biomechanical networks and probabilistic models to determine the relationships along the whole-body kinetic chain. For our primary analysis, participants will record videos of themselves performing the STS task at home following a standardized protocol, which will be analyzed using open-source methods. Our working hypothesis is that clinically relevant transitions in pain and functional state can be predicted from week-to-week changes in task performance. As a secondary analysis to identify promising PT interventions, the investigators will probabilistically generate virtual cohorts of patients performing STS tasks based on the movement patterns from the patient-collected videos and then perturb model parameters to mimic clinically identified deficits/treatment targets (e.g., muscle strength of the gluteus medius). This secondary analysis will generate testable hypotheses for patient-specific PT interventions.

Aim 3: Longitudinal Analysis of Time-varying Networks: The investigators will analyze longitudinal assessments and daily time series data to understand clinically-relevant transitions in pain and functional state. The primary analysis for this aim will identify state changes from multivariate time series data and validate them against changes from patient narratives and electronic health records. Secondary analyses will compare changes in network topologies to state changes. The investigators will investigate how network models reveal patient-specific dynamic interplay between allostatic load(12), physical function, central sensitization and pain catastrophizing.

The investigators approach using multi-domain time-varying network models represents a paradigm change to analyze and interpret data in cMSKP using a complex systems perspective. The investigators long-term objective is to develop clinically-feasible decision tools that enable whole-person assessment and management of cMSKP and guide personalized management strategies.

1. McBeth J, Jones K. Epidemiology of chronic musculoskeletal pain. Best Pract Res Clin Rheumatol. 2007 Jun;21(3):403-25. 2. Fillingim RB. Individual differences in pain: understanding the mosaic that makes pain personal. PAIN. 2017 Apr;158:S11. 3. Borsboom D, Deserno MK, Rhemtulla M, Epskamp S, Fried EI, McNally RJ, et al. Network analysis of multivariate data in psychological science. Nat Rev Methods Primer. 2021 Aug 19;1(1):1-18. 4. Gill TK, Mittinty MM, March LM, Steinmetz JD, Culbreth GT, Cross M, et al. Global, regional, and national burden of other musculoskeletal disorders, 1990-2020, and projections to 2050: a systematic analysis of the Global Burden of Disease Study 2021. Lancet Rheumatol. 2023 Nov 1;5(11):e670-82. 5. Puolakka PA, Rorarius MG, Roviola M, Puolakka TJ, Nordhausen K, Lindgren L. Persistent pain following knee arthroplasty. Eur J Anaesthesiol EJA. 2010 May;27(5):455. 6. Wylde V, Hewlett S, Learmonth ID, Dieppe P. Persistent pain after joint replacement: Prevalence, sensory qualities, and postoperative determinants. PAIN. 2011 Mar;152(3):566. 7. Forsythe ME, Dunbar MJ, Hennigar AW, Sullivan MJ, Gross M. Prospective Relation between Catastrophizing and Residual Pain following Knee Arthroplasty: Two-Year Follow-Up. Pain Res Manag. 2008;13(4):730951. 8. Baker PN, Meulen JH van der, Lewsey J, Gregg PJ. The role of pain and function in determining patient satisfaction after total knee replacement: DATA FROM THE NATIONAL JOINT REGISTRY FOR ENGLAND AND WALES. J Bone Joint Surg Br. 2007 Jul 1;89-B(7):893-900. 9. Brander VA, Stulberg SD, Adams AD, Harden RN, Bruehl S, Stanos SP, et al. Ranawat Award Paper: Predicting Total Knee Replacement Pain: A Prospective, Observational Study. Clin Orthop Relat Res. 2003 Nov;416:27. 10. Lewis GN, Rice DA, McNair PJ, Kluger M. Predictors of persistent pain after total knee arthroplasty: a systematic review and meta-analysis. \[cited 2024 Dec 11\]; Available from: https://dx.doi.org/10.1093/bja/aeu441 11. Son KM, Hong JI, Kim DH, Jang DG, Crema MD, Kim HA. Absence of pain in subjects with advanced radiographic knee osteoarthritis. BMC Musculoskelet Disord. 2020 Sep 29;21:640. 12. Liang Y, Booker C. Allostatic load and chronic pain: a prospective finding from the national survey of midlife development in the United States, 2004-2014. BMC Public Health. 2024 Feb 9;24(1):416.

• WOMAC [Срок оценки: Every 3 months for 1 year]
• WHOQOL [Срок оценки: Every 3 months for 1 year]
• Allostatic load [Срок оценки: Every 3 months for 1 year]