Multicenter comparison using two AI stroke CT perfusion software packages for determining thrombectomy eligibility.

BACKGROUND: Stroke AI platforms assess infarcted core and potentially salvageable tissue (penumbra) to identify patients suitable for mechanical thrombectomy. Few studies have compared outputs of these platforms, and none have been multicenter or considered NIHSS or scanner/protocol differences. Our objective was to compare volume estimates and thrombectomy eligibility from two widely used CT perfusion (CTP) packages, Viz.ai and RAPID.AI, in a large multicenter cohort. METHODS: We analyzed CTP data of acute stroke patients with large vessel occlusion (LVO) from four institutions. Core and penumbra volumes were estimated by each software and DEFUSE-3 thrombectomy eligibility assessed. Results between software packages were compared and categorized by NIHSS score, scanner manufacturer/model, and institution. RESULTS: Primary analysis of 362 cases found statistically significant differences in both software's volume estimations, with subgroup analysis showing these differences were driven by results from a single scanner model, the Canon Aquilion One. Viz.ai provided larger estimates with mean differences of 8cc and 18cc for core and penumbra, respectively (p<0.001). NIHSS subgroup analysis also showed systematically larger Viz.ai volumes (p<0.001). Despite volume differences, a significant difference in thrombectomy eligibility was not found. Additional subgroup analysis showed significant differences in penumbra volume for the Phillips Ingenuity scanner, and thrombectomy eligibility for the Canon Aquilion One scanner at one center (7 % increased eligibility with Viz.ai, p=0.03). CONCLUSIONS: Despite systematic differences in core and penumbra volume estimates between Viz.ai and RAPID.AI, DEFUSE-3 eligibility was not statistically different in primary or NIHSS subgroup analysis. A DEFUSE-3 eligibility difference, however, was seen on one scanner at one institution, suggesting scanner model and local CTP protocols can influence performance and cause discrepancies in thrombectomy eligibility. We thus recommend centers discuss optimal scanning protocols with software vendors and scanner manufacturers to maximize CTP accuracy.

Evaluating the Safety of Ultra-Low-Dose Estrogen Contraception in Sickle Cell Trait With Focus on Cerebral Venous Sinus Thrombosis.

Ultra-low-dose combination estrogen-progestin contraceptive pills (OCP) have been marketed as being safer to use than previously higher estrogen-containing OCPs. While multiple large studies have shown a dose-dependent association between estrogen and deep vein thrombosis, there remains sparse guidance or data as to whether patients with sickle cell trait should avoid estrogen-containing OCPs regardless of the dosage. We present a case of a 22-year-old female with a history of sickle cell trait who had recently been started on an ultra-low-dose norethindrone-ethinyl estradiol-iron (1-20 mcg) that presented with headache, nausea, vomiting, and obtunded. Initial neuroimaging was significant for an extensive superior sagittal sinus thrombosis with extension into the confluence of dural venous sinuses, right transverse sinus, right sigmoid sinus, and right internal jugular vein which ultimately required systemic anti-coagulation. Her symptoms largely resolved within four days after starting anti-coagulation. She was discharged on day six to complete a six-month course of oral anti-coagulation. At her neurology follow-up three months later, the patient reported resolution of all symptoms. This study evaluates the safety of ultra-low-dose estrogen-containing contraceptive pills in the sickle cell trait population with special focus on cerebral sinus thrombosis.

Coexistent Ipsilateral Internal Carotid Artery Occlusion and Cerebral Venous Thrombosis in Hepatitis C.

A 58-year-old male, known to have hepatitis C virus (HCV), presented with intermittent headaches and left-sided sensorimotor symptoms. There were no focal neurological deficits on examination. Electrocardiogram was unremarkable. Computed tomography angiography head and neck displayed extracranial right internal carotid artery occlusion. Magnetic resonance imaging showed right cortical vein thrombosis, with hemorrhagic infarction. Echocardiography with bubble study was unremarkable. Hypercoagulable workup was significant for protein S deficiency. He was treated with warfarin for 6 months. Repeat protein S levels remained low 9 months later. The coexistence of arterial and venous thrombotic events gives rise to a limited differential. In this case, it may be related to chronic HCV infection. The underlying pathogenesis is not clear; however, it is possible the patient had chronic high-grade internal carotid artery stenosis, which occluded leading to his presenting symptoms. The cortical vein thrombosis is likely an incidental finding here. The extent by which HCV contributed to the cerebral thrombosis and carotid artery occlusion in our case is not clear; however, the hypercoagulable and atherosclerotic properties of the virus cannot be disregarded. The virus can promote carotid atherosclerosis and cerebral venous thrombosis as well as other venous and arterial thromboembolic events. Furthermore, HCV is associated with impaired venous flow and procoagulant properties, which can fuel a hypercoagulable state. Also of note cirrhosis is associated with protein S deficiency. We recommend considering an underlying hypercoagulable state including both arterial and venous thrombosis in HCV infection.

A fully automated method for quantifying and localizing white matter hyperintensities on MR images.

White matter hyperintensities (WMH), commonly found on T2-weighted FLAIR brain MR images in the elderly, are associated with a number of neuropsychiatric disorders, including vascular dementia, Alzheimer's disease, and late-life depression. Previous MRI studies of WMHs have primarily relied on the subjective and global (i.e., full-brain) ratings of WMH grade. In the current study we implement and validate an automated method for quantifying and localizing WMHs. We adapt a fuzzy-connected algorithm to automate the segmentation of WMHs and use a demons-based image registration to automate the anatomic localization of the WMHs using the Johns Hopkins University White Matter Atlas. The method is validated using the brain MR images acquired from eleven elderly subjects with late-onset late-life depression (LLD) and eight elderly controls. This dataset was chosen because LLD subjects are known to have significant WMH burden. The volumes of WMH identified in our automated method are compared with the accepted gold standard (manual ratings). A significant correlation of the automated method and the manual ratings is found (P<0.0001), thus demonstrating similar WMH quantifications of both methods. As has been shown in other studies (e.g. [Taylor, W.D., MacFall, J.R., Steffens, D.C., Payne, M.E., Provenzale, J.M., Krishnan, K.R., 2003. Localization of age-associated white matter hyperintensities in late-life depression. Progress in Neuro-Psychopharmacology and Biological Psychiatry. 27 (3), 539-544.]), we found there was a significantly greater WMH burden in the LLD subjects versus the controls for both the manual and automated method. The effect size was greater for the automated method, suggesting that it is a more specific measure. Additionally, we describe the anatomic localization of the WMHs in LLD subjects as well as in the control subjects, and detect the regions of interest (ROIs) specific for the WMH burden of LLD patients. Given the emergence of large NeuroImage databases, techniques, such as that described here, will allow for a better understanding of the relationship between WMHs and neuropsychiatric disorders.

Impact of oral health in patients with multiple sclerosis and epilepsy: a survey in a neurology clinic.

This study investigated the impact of oral health on neurological disorders using the Oral Health Impact Profile (OHIP). A total of 460 subjects completed the OHIP, including 141 control subjects who did not have any neurological conditions. Of the 319 subjects with a neurological diagnosis who were enrolled in the study, 31% had multiple sclerosis (MS), 34% had epilepsy, and 34% had other neurological conditions. Compared to the control group, mean age (p = .001), education (p = .003), and household income levels (p ≤ .001) were statistically significantly lower among subjects with epilepsy than in the other two groups. The majority of the study populations were Caucasian and the percentage was highest in those with MS (87%). Patients with any neurologic diagnosis had greater physical pain and disability than controls. Adjusting for demographic variables, the impact of physical disability was statistically significantly higher in patients with any neurological diagnosis (including MS and epilepsy) (OR = 4.49). In multinomial regression, the strongest association of physical disability impact was noted in patients with epilepsy (OR = 5.17). The physical disability domain of the OHIP is more commonly associated with a neurological diagnosis, including MS, and the association is strongest in patients with diagnosis of epilepsy.