Background and Purpose Rapid recognition of those at high-risk for malignant edema after stroke would facilitate triage for monitoring and potential surgery. Admission data may be insufficient for accurate triage decisions. We developed a risk prediction score using clinical and radiographic variables within 24 hours of ictus to better predict potentially lethal malignant edema (PLME). Methods Patients admitted with diagnosis codes of “Cerebral Edema” and “Ischemic Stroke,” NIHSS ≥ 8, and head CTs within 24 hours of stroke-onset were included. Primary outcome of PLME was defined as death with midline shift (MLS) ≥ 5mm or Decompressive Hemicraniectomy. We performed multivariate analyses on data available within 24 hours of ictus. Bootstrapping was used to internally validate the model and a risk score was constructed from the results. Results 33% of 222 patients developed PLME. The final model c-statistic was 0.76 (CI 0.68-0.82) in the derivation cohort, and 0.75 (0.72-0.77) in the bootstrapping validation sample. The EDEMA score was developed using the following independent predictors: Basal cistern effacement (=3); Glucose ≥150 (=2); No tPA or thrombectomy (=1), MLS >0-3 (=1), 3-6 (=2), 6-9 (=4); >9 (=7); No prior stroke (=1). A score over 7 was associated with 93% positive predictive value. Conclusion The EDEMA score identifies patients at high risk for PLME. While it requires external validation, this scale could help expedite triage decisions in this patient population.

Antibody-mediated encephalitis defines a class of diseases wherein antibodies directed at cellsurface receptors are associated with behavioral and cognitive disturbances. One such recently described encephalitis is due to antibodies directed at alpha-amino-3-hydroxy-5methyl-4isoxazolepropionic acid receptors (AMPAR). This entity is exceptionally rare and its clinical phenotype incompletely described. We present findings from two cases of AMPAR encephalitis that exemplify variability in the disease spectrum, and summarize findings in published cases derived from a systematic literature review. When all patients are considered together, the presence of psychiatric symptoms at presentation portended a poor outcome and was associated with the presence of a tumor. Furthermore, we provide evidence to suggest that the topography of magnetic resonance imaging abnormalities in reported cases mirrors the distribution of AMPARs in the human brain. The potential for neurological improvement following immunomodulatory therapy together with the favorable outcome reported in most cases emphasizes the importance of testing for autoantibodies against neuronal cell-surface proteins, including AMPAR, in patients with clinical and neuroimaging findings suggestive of autoimmune encephalitis. Close attention to the clinical phenotype may inform the presence of malignancy and long-term prognosis.

Therapeutic, IV.

Objectives:Topographical distribution of white matter hyperintensities (WMH) are hypothesized to vary by cerebrovascular risk factors. We used an unbiased pattern discovery approach to identify distinct WMH spatial patterns and investigate their association with different WMH etiologies.Methods:We performed a cross-sectional study on participants of the Alzheimer’s Disease Neuroimaging Initiative (ADNI) to identify spatially distinct WMH distribution patterns using voxel-based spectral clustering analysis of aligned WMH probability maps. We included all participants from the ADNI Grand Opportunity/ADNI 2 study with available baseline 2D-FLAIR MRI scans, without prior history of stroke or presence of infarction on imaging. We evaluated the associations of these WMH spatial patterns with vascular risk factors, amyloid-β PET, and imaging biomarkers of cerebral amyloid angiopathy (CAA), characterizing different forms of cerebral small vessel disease (CSVD) using multivariable regression. We also used linear regression models to investigate whether WMH spatial distribution influenced cognitive impairment.Results:We analyzed MRI scans of 1,046 ADNI participants with mixed vascular and amyloid-related risk factors (mean age 72.9, 47.7% female, 31.4% hypertensive, 48.3% with abnormal amyloid PET). We observed unbiased partitioning of WMH into five unique spatial patterns: deep frontal, periventricular, juxtacortical, parietal, and posterior. Juxtacortical WMH were independently associated with probable CAA, deep frontal WMH were associated with risk factors for arteriolosclerosis (hypertension and diabetes), and parietal WMH were associated with brain amyloid accumulation, consistent with an Alzheimer’s disease (AD) phenotype. Juxtacortical, deep frontal, and parietal WMH spatial patterns were associated with cognitive impairment. Periventricular and posterior WMH spatial patterns were unrelated to any disease phenotype or cognitive decline.Discussion:Data-driven WMH spatial patterns reflect discrete underlying etiologies including arteriolosclerosis, CAA, AD, and normal aging. Global measures of WMH volume may miss important spatial distinctions. WMH spatial signatures may serve as etiology-specific imaging markers, helping to resolve WMH heterogeneity, identify the dominant underlying pathological process, and improve prediction of clinical-relevant trajectories that influence cognitive decline.