The Levels of Glial Fibrillary Acidic Protein and Ubiquitin C-Terminal Hydrolase-L1 During the First Week After a Traumatic Brain Injury: Correlations With Clinical and Imaging Findings.

BACKGROUND: Glial fibrillary acidic protein (GFAP) and ubiquitin C-terminal hydrolase-L1 (UCH-L1) are promising biomarkers of traumatic brain injury (TBI). OBJECTIVE: We investigated the relation of the GFAP and UCH-L1 levels to the severity of TBI during the first week after injury. METHODS: Plasma UCH-L1 and GFAP were measured from 324 consecutive patients with acute TBI and 81 control subject enrolled in a 2-center prospective study. The baseline measures included initial Glasgow Coma Scale (GCS), head computed tomographic (CT) scan at admission, and blood samples for protein biomarkers that were collected at admission and on days 1, 2, 3, and 7 after injury. RESULTS: Plasma levels of GFAP and UCH-L1 during the first 2 days after the injury strongly correlated with the initial severity of TBI as assessed with GCS. Additionally, levels of UCH-L1 on the seventh day after the injury were significantly related to the admission GCS scores. At admission, both biomarkers were capable of distinguishing mass lesions from diffuse injuries in CT, and the area under the curve of the receiver-operating characteristic curve for prediction of any pathological finding in CT was 0.739 (95% confidence interval, 0.636-0.815) and 0.621 (95% confidence interval, 0.517-0.713) for GFAP and UCH-L1, respectively. CONCLUSION: These results support the prior findings of the potential role of GFAP and UCH-L1 in acute-phase diagnostics of TBI. The novel finding is that levels of GFAP and UCH-L1 correlated with the initial severity of TBI during the first 2 days after the injury, thus enabling a window for TBI diagnostics with latency. ABBREVIATIONS: AUC, area under the curveCI, confidence intervalED, emergency departmentGCS, Glasgow Coma ScaleGRAP, glial fibrillary acidic proteinIMPACT, International Mission for Prognosis and Clinical TrialROC, receiver-operating characteristicTBI, traumatic brain injuryTRACK-TBI, Transforming Research and Clinical Knowledge in Traumatic Brain InjuryUCH-L1, ubiquitin C-terminal hydrolase-L1.

Glial Fibrillary Acidic Protein and Ubiquitin C-Terminal Hydrolase-L1 as Outcome Predictors in Traumatic Brain Injury.

OBJECTIVE: Biomarkers ubiquitin C-terminal hydrolase-L1 (UCH-L1) and glial fibrillary acidic protein (GFAP) may help detect brain injury, assess its severity, and improve outcome prediction. This study aimed to evaluate the prognostic value of these biomarkers during the first days after brain injury. METHODS: Serum UCH-L1 and GFAP were measured in 324 patients with traumatic brain injury (TBI) enrolled in a prospective study. The outcome was assessed using the Glasgow Outcome Scale (GOS) or the extended version, Glasgow Outcome Scale-Extended (GOSE). RESULTS: Patients with full recovery had lower UCH-L1 concentrations on the second day and patients with favorable outcome had lower UCH-L1 concentrations during the first 2 days compared with patients with incomplete recovery and unfavorable outcome. Patients with full recovery and favorable outcome had significantly lower GFAP concentrations in the first 2 days than patients with incomplete recovery or unfavorable outcome. There was a strong negative correlation between outcome and UCH-L1 in the first 3 days and GFAP levels in the first 2 days. On arrival, both UCH-L1 and GFAP distinguished patients with GOS score 1-3 from patients with GOS score 4-5, but not patients with GOSE score 8 from patients with GOSE score 1-7. For UCH-L1 and GFAP to predict unfavorable outcome (GOS score ≤ 3), the area under the receiver operating characteristic curve was 0.727, and 0.723, respectively. Neither UCHL-1 nor GFAP was independently able to predict the outcome when age, worst Glasgow Coma Scale score, pupil reactivity, Injury Severity Score, and Marshall score were added into the multivariate logistic regression model. CONCLUSIONS: GFAP and UCH-L1 are significantly associated with outcome, but they do not add predictive power to commonly used prognostic variables in a population of patients with TBI of varying severities.

Quantitative evaluation of disease progression in a longitudinal mild cognitive impairment cohort.

Several neuropsychological tests and biomarkers of Alzheimer's disease (AD) have been validated and their evolution over time has been explored. In this study, multiple heterogeneous predictors of AD were combined using a supervised learning method called Disease State Index (DSI). The behavior of DSI values over time was examined to study disease progression quantitatively in a mild cognitive impairment (MCI) cohort. The DSI method was applied to longitudinal data from 140 MCI cases that progressed to AD and 149 MCI cases that did not progress to AD during the follow-up. The data included neuropsychological tests, brain volumes from magnetic resonance imaging, cerebrospinal fluid samples, and apolipoprotein E from the Alzheimer's Disease Neuroimaging Initiative database. Linear regression of the longitudinal DSI values (including the DSI value at the point of MCI to AD conversion) was performed for each subject having at least three DSI values available (147 non-converters, 126 converters). Converters had five times higher slopes and almost three times higher intercepts than non-converters. Two subgroups were found in the group of non-converters: one group with stable DSI values over time and another group with clearly increasing DSI values suggesting possible progression to AD in the future. The regression parameters differentiated between the converters and the non-converters with classification accuracy of 76.9% for the slopes and 74.6% for the intercepts. In conclusion, this study demonstrated that quantifying longitudinal patient data using the DSI method provides valid information for follow-up of disease progression and support for decision making.

A MATLAB toolbox for classification and visualization of heterogenous multi-scale human data using the Disease State Fingerprint method.

As the amount of data acquired from humans is constantly increasing, efficient tools are needed for extracting relevant information from this data. This paper presents a Matlab implementation of a method to classify and visually explore (highly) multi-variate patient data. The method uses the so-called Disease State Index (DSI) which measures the fit of a test subject's data to two classes present in the data (e.g. 'controls' and 'positives'). DSI values of the different variables measured from a patient can be combined and visualized in a tree-like form using the Disease State Fingerprint (DSF) method. This allows a researcher to explore and understand the relevance of the different variables in classification problems. Moreover, the method is robust with respect to missing data. After giving an introduction to the DSF and DSI methods, the paper describes the steps required to use the methods and presents a MATLAB toolbox to perform these steps. To demonstrate the methods' versatility, the paper illustrates the usage of the toolbox in a few different contexts in which personal health data is to be classified. With this implementation, a powerful and flexible tool is made available to the biomedical research community.