A multiparameter panel method for outcome prediction following aneurysmal subarachnoid hemorrhage

Application of proteomics to cerebrovascular disease

While neurovascular diseases such as ischemic and hemorrhagic stroke are the leading causes of disability in the world, the repertoire of therapeutic interventions has remained remarkably limited. There is a dire need to develop new diagnostic, prognostic, and therapeutic options. The study of proteomics is particularly enticing for cerebrovascular diseases such as stroke, which most likely involve multiple gene interactions resulting in a wide range of clinical phenotypes. Currently, rapidly progressing neuroproteomic techniques have been employed in clinical and translational research to help identify biologically relevant pathways, to understand cerebrovascular pathophysiology, and to develop novel therapeutics and diagnostics. Future integration of proteomic with genomic, transcriptomic, and metabolomic studies will add new perspectives to better understand the complexities of neurovascular injury. Here, we review cerebrovascular proteomics research in both preclinical (animal, cell culture) and clinical (blood, urine, cerebrospinal fluid, microdialyates, tissue) studies. We will also discuss the rewards, challenges, and future directions for the application of proteomics technology to the study of various disease phenotypes. To capture the dynamic range of cerebrovascular injury and repair with a translational targeted and discovery approach, we emphasize the importance of complementing innovative proteomic technology with existing molecular biology models in preclinical studies, and the need to advance pharmacoproteomics to directly probe clinical physiology and gauge therapeutic efficacy at the bedside.

Blood glutathione S-transferase-π as a time indicator of stroke onset

Background

Ability to accurately determine time of stroke onset remains challenging. We hypothesized that an early biomarker characterized by a rapid increase in blood after stroke onset may help defining better the time window during which an acute stroke patient may be candidate for intravenous thrombolysis or other intravascular procedures.

Methods

The blood level of 29 proteins was measured by immunoassays on a prospective cohort of stroke patients (N = 103) and controls (N = 132). Mann-Whitney U tests, ROC curves and diagnostic odds ratios were applied to evaluate their clinical performances.

Results

Among the 29 molecules tested, GST-π concentration was the most significantly elevated marker in the blood of stroke patients (p<0.001). More importantly, GST-π displayed the best area under the curve (AUC, 0.79) and the best diagnostic odds ratios (10.0) for discriminating early (N = 22, <3 h of stroke onset) vs. late stroke patients (N = 81, >3 h after onset). According to goal-oriented distinct cut-offs (sensitivity(Se)-oriented: 17.7 or specificity(Sp)-oriented: 65.2 ug/L), the GST-π test obtained 91%Se/50%Sp and 50%Se/91%Sp, respectively. Moreover, GST-π showed also the highest AUC (0.83) and performances for detecting patients treated with tPA (N = 12) compared to ineligible patients (N = 103).

Conclusions

This study demonstrates that GST-π can accurately predict the time of stroke onset in over 50% of early stroke patients. The GST-π test could therefore complement current guidelines for tPA administration and potentially increase the number of patients accessing thrombolysis.

Relationship between plasma high mobility group box-1 protein levels and clinical outcomes of aneurysmal subarachnoid hemorrhage

Background

High-mobility group box 1 (HMGB1), originally described as a nuclear protein that binds to and modifies DNA, is now regarded as a central mediator of inflammation by acting as a cytokine. However, the association of HMGB1 in the peripheral blood with disease outcome and cerebrovasospasm has not been examined in patients with aneurysmal subarachnoid hemorrhage.

Methods

In this study, 303 consecutive patients were included. Upon admission, plasma HMGB1 levels were measured by ELISA. The end points were mortality after 1 year, in-hospital mortality, cerebrovasospasm and poor functional outcome (Glasgow Outcome Scale score of 1 to 3) after 1 year.

Results

Upon admission, the plasma HMGB1 level in patients was statistically significantly higher than that in healthy controls. A multivariate analysis showed that the plasma HMGB1 level was an independent predictor of poor functional outcome and mortality after 1 year, in-hospital mortality and cerebrovasospasm. A receiver operating characteristic curve showed that plasma HMGB1 level on admission statistically significantly predicted poor functional outcome and mortality after 1 year, in-hospital mortality and cerebrovasospasm of patients. The area under the curve of the HMGB1 concentration was similar to those of World Federation of Neurological Surgeons (WFNS) score and modified Fisher score for the prediction of poor functional outcome and mortality after 1 year, and in-hospital mortality, but not for the prediction of cerebrovasospasm. In a combined logistic-regression model, HMGB1 improved the area under the curve of WFNS score and modified Fisher score for the prediction of poor functional outcome after 1 year, but not for the prediction of mortality after 1 year, in-hospital mortality, or cerebrovasospasm.

Conclusions

HMGB1 level is a useful, complementary tool to predict functional outcome and mortality after aneurysmal subarachnoid hemorrhage. However, HMGB1 determination does not add to the accuracy of prediction of the clinical outcomes.

pROC: an open-source package for R and S+ to analyze and compare ROC curves

BACKGROUND

Receiver operating characteristic (ROC) curves are useful tools to evaluate classifiers in biomedical and bioinformatics applications. However, conclusions are often reached through inconsistent use or insufficient statistical analysis. To support researchers in their ROC curves analysis we developed pROC, a package for R and S+ that contains a set of tools displaying, analyzing, smoothing and comparing ROC curves in a user-friendly, object-oriented and flexible interface.

RESULTS

With data previously imported into the R or S+ environment, the pROC package builds ROC curves and includes functions for computing confidence intervals, statistical tests for comparing total or partial area under the curve or the operating points of different classifiers, and methods for smoothing ROC curves. Intermediary and final results are visualised in user-friendly interfaces. A case study based on published clinical and biomarker data shows how to perform a typical ROC analysis with pROC.

CONCLUSIONS

pROC is a package for R and S+ specifically dedicated to ROC analysis. It proposes multiple statistical tests to compare ROC curves, and in particular partial areas under the curve, allowing proper ROC interpretation. pROC is available in two versions: in the R programming language or with a graphical user interface in the S+ statistical software. It is accessible at http://expasy.org/tools/pROC/ under the GNU General Public License. It is also distributed through the CRAN and CSAN public repositories, facilitating its installation.

pROC: an open-source package for R and S+ to analyze and compare ROC curves

BACKGROUND

Receiver operating characteristic (ROC) curves are useful tools to evaluate classifiers in biomedical and bioinformatics applications. However, conclusions are often reached through inconsistent use or insufficient statistical analysis. To support researchers in their ROC curves analysis we developed pROC, a package for R and S+ that contains a set of tools displaying, analyzing, smoothing and comparing ROC curves in a user-friendly, object-oriented and flexible interface.

RESULTS

With data previously imported into the R or S+ environment, the pROC package builds ROC curves and includes functions for computing confidence intervals, statistical tests for comparing total or partial area under the curve or the operating points of different classifiers, and methods for smoothing ROC curves. Intermediary and final results are visualised in user-friendly interfaces. A case study based on published clinical and biomarker data shows how to perform a typical ROC analysis with pROC.

CONCLUSIONS

pROC is a package for R and S+ specifically dedicated to ROC analysis. It proposes multiple statistical tests to compare ROC curves, and in particular partial areas under the curve, allowing proper ROC interpretation. pROC is available in two versions: in the R programming language or with a graphical user interface in the S+ statistical software. It is accessible at http://expasy.org/tools/pROC/ under the GNU General Public License. It is also distributed through the CRAN and CSAN public repositories, facilitating its installation.