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Objective. To examine (1) if serological or cerebrospinal fluid (CSF) biomarkers can be used as diagnostic and/or prognostic tools in patients with spinal cord injury (SCI) and (2) if literature provides recommendations regarding timing and source of biomarker evaluation. Data Sources. A systematic literature search to identify studies reporting on diagnostic and prognostic blood and/or CSF biomarkers in SCI was conducted in PubMed/MEDLINE, CINAHL, Science Direct, The Cochrane Library, ISI Web of Science, and PEDro. Study Selection. Clinical trials, cohort, and pilot studies on patients with traumatic SCI investigating at least one blood or CSF biomarker were included. Following systematic screening, 19 articles were included in the final analysis. PRISMA guidelines were followed to conduct this review. Data Extraction. Independent extraction of articles was completed by 2 authors using predefined inclusion criteria and study quality indicators. Data Synthesis. Nineteen studies published between 2002 and April 2019 with 1596 patients were included in the systematic review. In 14 studies, blood biomarkers were measured, 4 studies investigated CSF biomarkers, and 1 study used both blood and CSF samples. Conclusions. Serum/CSF concentrations of several biomarkers (S100b, IL-6, GFAP, NSE, tau, TNF-α, IL-8, MCP-1, pNF-H, and IP-10) following SCI are highly time dependent and related to injury severity. Future studies need to validate these markers as true biomarkers and should control for secondary complications associated with SCI. A deeper understanding of secondary pathophysiological events after SCI and their effect on biomarker dynamics may improve their clinical significance as surrogate parameters in future clinical studies.

Our results demonstrate that there is a strong, linear relationship between the RoA and PPT when using the pressure algometry technique. The low slope between RoA and PPT suggests clinicians can rely on PPT assessments despite small RoA fluctuations. Future research should explore this relationship further in a clinical population and in other muscles affected by chronic myofascial pain. Advancing cost-effective, reliable, and clinically feasible tools such as algometry is important to enhancing the diagnosis and management of chronic myofascial pain.