| Autor: |
Chen H; Department of Radiology, Neuroradiology Division, Quantitative Sciences Unit, Stanford University, Stanford, California, USA., Ding VY; Department of Medicine, Quantitative Sciences Unit, Stanford University, Stanford, California, USA., Zhu G; Department of Radiology, Neuroradiology Division, Quantitative Sciences Unit, Stanford University, Stanford, California, USA., Jiang B; Department of Radiology, Neuroradiology Division, Quantitative Sciences Unit, Stanford University, Stanford, California, USA., Li Y; Department of Radiology, Neuroradiology Division, Quantitative Sciences Unit, Stanford University, Stanford, California, USA., Boothroyd D; Department of Medicine, Quantitative Sciences Unit, Stanford University, Stanford, California, USA., Rezaii PG; Department of Radiology, Neuroradiology Division, Quantitative Sciences Unit, Stanford University, Stanford, California, USA., Bet AM; Department of Radiology, Neuroradiology Division, Quantitative Sciences Unit, Stanford University, Stanford, California, USA., Paulino AD; Banyan Biomarkers Inc., San Diego, California, USA., Weber A; Banyan Biomarkers Inc., San Diego, California, USA., Glushakova OY; University of Virginia Cancer Center, Charlottesville, Virginia, USA., Hayes RL; Department of Neurosurgery, Virginia Commonwealth University, Richmond, Virginia, USA., Wintermark M; Department of Radiology, Neuroradiology Division, Quantitative Sciences Unit, Stanford University, Stanford, California, USA. |
| Abstrakt: |
The objective of this work was to analyze the relationships between traumatic brain injury (TBI) on computed tomographic (CT) imaging and blood concentration of glial fibrillary acidic protein (GFAP), ubiquitin C-terminal hydrolase-L1 (UCH-L1), and S100B. This prospective cohort study involved 644 TBI patients referred to Stanford Hospital's Emergency Department between November 2015 and April 2017. Plasma and serum samples of 462 patients were analyzed for levels of GFAP, UCH-L1, and S100B. Glial neuronal ratio (GNR) was calculated as the ratio between GFAP and UCH-L1 concentrations. Admission head CT scans were reviewed for TBI imaging common data elements, and performance of biomarkers for identifying TBI was assessed via area under the receiver operating characteristic curve (ROC). We also dichotomized biomarkers at established thresholds and estimated standard measures of classification accuracy. We assessed the ability of GFAP, UCH-L1, and GNR to discriminate small and large/diffuse lesions based on CT imaging using an ROC analysis. In our cohort of mostly mild TBI patients, GFAP was significantly more accurate in detecting all types of acute brain injuries than UCH-L1 in terms of area under the curve (AUC) values ( p < 0.001), and also compared with S100B ( p < 0.001). UCH-L1 and S100B had similar performance (comparable AUC values, p = 0.342). Sensitivity exceeded 0.8 for each biomarker across all different types of TBI injuries, and no significant differences were observed by type of injury. There was a significant difference between GFAP and GNR in distinguishing between small lesions and large/diffuse lesions in all injuries ( p = 0.004, p = 0.007). In conclusion, GFAP, UCH-L1, and S100B show high sensitivity and negative predictive values for all types of TBI lesions on head CT. A combination of negative blood biomarkers (GFAP and UCH-L1) in a patient suspected of TBI may be used to safely obviate the need for a head CT scan. GFAP is a promising indicator to discriminate between small and large/diffuse TBI lesions. |
