The SENSOR System: Using Standardized Data Entry and Dashboards for Review of Scientific Studies on the Utility of Blood-Based Protein Biomarkers for Patients with Mild Brain Injury

BackgroundThere is no objective way of diagnosing or prognosticating acute traumatic brain injuries (TBIs). A systematic review conducted by Mondello et al. reviewed studies looking at blood based protein biomarkers in the context of acute mild traumatic brain injuries and correlation to results of computed tomography scanning. This paper provides a summary of this same literature using the SENSOR system. MethodsAn existing review written by Mondello et al. was selected to apply the previously described SENSOR system (Kamal et al.) that uses a systematic process made up of a Google Form for data intake, Google Drive for article access, and Google Sheets for the creation of the dashboard. The dashboard consisted of a map, bubble graphs, multiple score charts, and a pivot table to facilitate the presentation of data. ResultsA total of 29 entries were inputted by two team members. Sensitivities, specificities, positive predictive values (PPVs), negative predictive values (NPVs), demographics, cut-off levels, biomarker levels, and assay ranges were analyzed and presented in this study. S100B and GFAP biomarkers may provide good clinical utility, whereas UCH-L1, C-Tau, and NSE do not. DiscussionThis study determined the feasibility and reliability of multiple biomarkers (S100B, UCH-L1, GFAP, C-tau, and NSE) in predicting traumatic brain lesions on CT scans, in mTBI patients, using the SENSOR system. Many potential limitations exist for the existing literature including controlling for known confounders for mild traumatic brain injuries. ConclusionThe SENSOR system is an adaptable, dynamic, and graphical display of scientific studies that has many benefits, which may still require further validation. Certain protein biomarkers may be helpful in deciding which patients with mTBIs require CT scans, but impact on prognosis is still not clear based on the available literature.