Systematic model of peripheral inflammation after subarachnoid hemorrhage.

OBJECTIVE: To investigate inflammatory processes after aneurysmal subarachnoid hemorrhage (aSAH) with network models. METHODS: This is a retrospective observational study of serum samples from 45 participants with aSAH analyzed at multiple predetermined time points: <24 hours, 24 to 48 hours, 3 to 5 days, and 6 to 8 days after aSAH. Concentrations of cytokines were measured with a 41-plex human immunoassay kit, and the Pearson correlation coefficients between all possible cytokine pairs were computed. Systematic network models were constructed on the basis of correlations between cytokine pairs for all participants and across injury severity. Trends of individual cytokines and correlations between them were examined simultaneously. RESULTS: Network models revealed that systematic inflammatory activity peaks at 24 to 48 hours after the bleed. Individual cytokine levels changed significantly over time, exhibiting increasing, decreasing, and peaking trends. Platelet-derived growth factor (PDGF)-AA, PDGF-AB/BB, soluble CD40 ligand, and tumor necrosis factor-α (TNF-α) increased over time. Colony-stimulating factor (CSF) 3, interleukin (IL)-13, and FMS-like tyrosine kinase 3 ligand decreased over time. IL-6, IL-5, and IL-15 peaked and decreased. Some cytokines with insignificant trends show high correlations with other cytokines and vice versa. Many correlated cytokine clusters, including a platelet-derived factor cluster and an endothelial growth factor cluster, were observed at all times. Participants with higher clinical severity at admission had elevated levels of several proinflammatory and anti-inflammatory cytokines, including IL-6, CCL2, CCL11, CSF3, IL-8, IL-10, CX3CL1, and TNF-α, compared to those with lower clinical severity. CONCLUSIONS: Combining reductionist and systematic techniques may lead to a better understanding of the underlying complexities of the inflammatory reaction after aSAH.

A simple prediction score system for malignant brain edema progression in large hemispheric infarction.

Malignant brain edema (MBE) due to hemispheric infarction can result in brain herniation, poor outcomes, and death; outcome may be improved if certain interventions, such as decompressive craniectomy, are performed early. We sought to generate a prediction score to easily identify those patients at high risk for MBE. 121 patients with large hemispheric infarction (LHI) (2011 to 2014) were included. Patients were divided into two groups: those who developed MBE and those who did not. Independent predictors of MBE were identified by logistic regression and a score was developed. Four factors were independently associated with MBE: baseline National Institutes of Health Stroke Scale (NIHSS) score (p = 0.048), Alberta Stroke Program Early Computed Tomography Score (ASPECTS) (p = 0.007), collateral score (CS) (p<0.001) and revascularization failure (p = 0.013). Points were assigned for each factor as follows: NIHSS ≤ 8 (= 0), 9-17 (= 1), ≥ 18 (= 2); ASPECTS≤ 7 (= 1), >8 (= 0); CS<2 (= 1), ≥2 (= 0); revascularization failure (= 1),success (= 0). The MBE Score (MBES) represents the sum of these individual points. Of 26 patients with a MBES of 0 to 1, none developed MBE. All patients with a MBES of 6 developed MBE. Both MBE development and functional outcomes were strongly associated with the MBES (p = 0.007 and 0.002, respectively). The MBE score is a simple reliable tool for the prediction of MBE.

Quantification of Cerebral Edema After Subarachnoid Hemorrhage.

BACKGROUND: Global cerebral edema (GCE) is a manifestation of early brain injury (EBI) after subarachnoid hemorrhage (SAH) and is an independent risk factor for poor outcome. The lack of a quantitative method to measure GCE limits the study of its pathophysiology. The goal of this study is to develop a quantitative surrogate marker that represents GCE after SAH. METHODS: Patients with spontaneous SAH were enrolled into a prospective observational database. Initial CT scans were graded for GCE using established qualitative criteria. Selective sulcal volume (SSV) was defined as total mL of sulcal volumes on axial CT slices above the most cranial section of the lateral ventricles to the last visible section. Using a semiautomatic threshold approach, sulcal regions were traced out with manual adjustments when necessary. The volume of sulci in each slice was calculated and multiplied by the slice thickness and number of slices to calculate the SSV. All volumetric analysis was performed using Medical Image Processing, Analysis and Visualization Version 7.0.1 (MIPAV). RESULTS: A total of 109 subjects were included in our analysis. Mean selective sulcal volumes (SSV) differed between subjects with and without GCE 4.5 and 21.2 mL (P < 0.001). When separated into quartiles, the odds of qualitative GCE increases as SSV decreases. Compared to the highest SSV quartile, smaller SSV was associated with worse clinical outcomes. CONCLUSION: GCE can be quantified using volumetric analysis of SSV measurements on routine CT scans. Smaller SSV on admission is predictive of worse clinical outcomes. SSV may be an important marker of EBI after SAH.