Intracranial pressure (ICP) monitoring has been widely accepted in the management of traumatic brain injury. However, its use in other pathologies that affect ICP has not been advocated as strongly, especially in CNS infections. Despite the most aggressive and novel antimicrobial therapies for meningitis, the mortality rate associated with this disease is far from satisfactory. Although intracranial hypertension and subsequent death have long been known to complicate meningitis, no specific guidelines targeting ICP monitoring are available. A review of the literature was performed to understand the pathophysiology of elevated ICP in meningitis, diagnostic challenges, and clinical outcomes in the use of ICP monitoring.

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Introduction: Presence of functional leptomeningeal collaterals has been found to be one of the key determinants of good outcomes. We evaluated the effects of certain environmental and physical factors in cerebral collateral circulation. Methods: After IRB approval, data was collected from prospective institutional Neurocritical Care and Stroke Registry. Stroke patients presenting with acute anterior circulation large vessel occlusion between May 2013 and August 2018 were included. Collaterals were graded based on CT-Angiogram and CT-Perfusion images obtained on presentation, by a blinded Neuroradiologist as good (grade 3-4) and poor (grades 0-2). Ohio air pollution data (2013-2018) were obtained from the US EPA and summarized as a single mean concentration by monitor. Strokes were geocoded and linked with the closest monitor. Odds of good (vs. poor) collateral grade were compared using BMI (linear), smoking status, antiplatelet medication use, and mean PM 2.5 concentration (cubic). Sensitivity analyses assessed additional adjustment by sex and change in NIHSS (admission to discharge). Results: A total of 73 strokes were analyzed with 48% male, mean BMI of 30.4 (SD=7.2), and 22% with good collateral grade. For each kg/m 2 greater BMI, the odds of having good collaterals (vs. poor) were 11% higher (OR: 1.11; 95%CI:1.01-1.22; p =0.029). Higher PM 2.5 concentrations were suggestive of lower odds of good collaterals ( p =0.124). Antiplatelet medication use (OR: 2.10; 95%CI:0.59-7.45) and smoking status (OR: 1.35; 95%CI:0.73-13.9) were not associated with the odds of having good collateral grade. After controlling for change in NIHSS and sex, higher BMI remained associated with good collateral grade (fig.1). Conclusions: Higher BMI was independently associated with higher odds of good collateral grade; this association needs to be explored in larger population taking other factors like age and medical comorbidities into account.

Introduction: Cerebral Small Vessel Disease (CSVD) impacts the functional outcome of acute ischemic stroke (AIS) patients. The majority of the available tools to quantify its burden relies on visual categorical scales, which is labor intensive and subject to inter- and intra-reader variability. Fully automated tools are rare and mostly focus on white matter hyperintensity (WMH) as a surrogate for CSVD. We aimed to develop a novel software that simulates the clinicians’ rational to detect lacunes on clinical MRI scans of patients presenting with acute AIS or transient ischemic attack (TIA). Methods: Patients presenting with symptoms of acute AIS or TIA were prospectively recruited. Lacunes were scored on the first brain MRI collected within 24 hours of hospital admission by a board-certified neuro-intensivist/vascular neurologist according to the Neuroimaging Standards for Research into Small Vessel Disease (STRIVE) criteria. Following standard skull stripping and co-registration, automated software was developed in Matlab by calculating maximal intensity difference of co-registered voxels on FLAIR and T2 MRI sequences. Cerebrospinal fluid was removed by seed-based growing approach and lacunes were subsequently detected based on their size (3-15 mm) and morphological features. Results: 30 subjects were included (age 61.6±16.1, 30% females); 6 of which had TIA and 23 had AIS (10 acute lacunar stroke, 3 cardioembolic, 10 cryptogenic and 1 stroke of other determined etiology). There were 24 lacunes detected in 12 subjects by the human reader. The automated system accurately identified subjects with lacunes in 91.6% of the cases with 75% specificity. Negative predictive value for subjects without lacunes was 93.7%. At the lesion level, the paradigm identified 62.5% of all lacunes in all subjects with positive predictive value of 78.9%; 67.0% of the missed lacunes were in close proximity to the ventricles. Conclusions: Automated identification of lacunes is feasible on clinical MRI scans of patients with acute AIS or TIA. Lacunes closer to the ventricles are challenging and may require a separate approach.

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