Low back pain (LBP) is the leading cause of disability worldwide. Most LBP is non-specific or idiopathic, which is defined as symptoms of unknown origin without a clear specific cause or pathology. Current guidelines for clinical evaluation are based on ruling out underlying serious medical conditions, but not on addressing underlying potential contributors to pain. Although efforts have been made to identify subgroups within this population based on response to treatment, a comprehensive framework to guide assessment is still lacking. In this paper, we propose a model for a personalized mechanism-based assessment based on the available evidence that seeks to identify the underlying pathologies that may initiate and perpetuate central sensitization associated with chronic non-specific low back pain (nsLBP). We propose that central sensitization can have downstream effects on the "myofascial unit", defined as an integrated anatomical and functional structure that includes muscle fibers, fascia (including endomysium, perimysium and epimysium) and its associated innervations (free nerve endings, muscle spindles), lymphatics, and blood vessels. The tissue-level abnormalities can be perpetuated through a vicious cycle of neurogenic inflammation, impaired fascial gliding, and interstitial inflammatory stasis that manifest as the clinical findings for nsLBP. We postulate that our proposed model offers biological plausibility for the complex spectrum of clinical findings, including tissue-level abnormalities, biomechanical dysfunction and postural asymmetry, ecological and psychosocial factors, associated with nsLBP. The model suggests a multi-domain evaluation that is personalized, feasible and helps rule out specific causes for back pain guiding clinically relevant management. It may also provide a roadmap for future research to elucidate mechanisms underlying this ubiquitous and complex problem.
Keywords: biopsychosocial model; low back pain; mechanism-based clinical assessment; myofascial pain syndrome; myofascial unit; personalized diagnostics.
© 2023 Sikdar, Srbely, Shah, Assefa, Stecco, DeStefano, Imamura and Gerber.