Background and Purpose-More than 47 million individuals in the United States meet the criteria for the metabolic syndrome. The relation between the metabolic syndrome and stroke risk in multiethnic populations has not been well characterized. Methods-As part of the Northern Manhattan Study, 3298 stroke-free community residents were prospectively followed up for a mean of 6.4 years. The metabolic syndrome was defined according to guidelines established by the National Cholesterol Education Program Adult Treatment Panel III. Cox proportional-hazards models were used to calculate hazard ratios (HRs) and 95% CIs for ischemic stroke and vascular events (ischemic stroke, myocardial infarction, or vascular death). The etiologic fraction estimates the proportion of events attributable to the metabolic syndrome. Results-More than 44% of the cohort had the metabolic syndrome (48% of women vs 38% of men, PϽ0.0001), which was more prevalent among Hispanics (50%) than whites (39%) or blacks (37%). The metabolic syndrome was associated with increased risk of stroke (HRϭ1.5; 95% CI, 1.1 to 2.2) and vascular events (HRϭ1.6; 95% CI, 1.3 to 2.0) after adjustment for sociodemographic and risk factors. The effect of the metabolic syndrome on stroke risk was greater among women (HRϭ2.0; 95% CI, 1.3 to 3.1) than men (HRϭ1.1; 95% CI, 0.6 to 1.9) and among Hispanics (HRϭ2.0; 95% CI, 1.2 to 3.4) compared with blacks and whites. The etiologic fraction estimates suggest that elimination of the metabolic syndrome would result in a 19% reduction in overall stroke, a 30% reduction of stroke in women; and a 35% reduction of stroke among Hispanics. Conclusions-The metabolic syndrome is an important risk factor for ischemic stroke, with differential effects by sex and race/ethnicity. (Stroke. 2008;39:30-35.)

Background: Holmes tremor (HT) arises from disruption of the cerebellothalamocortical pathways. A lesion can interrupt the projection at any point, resulting in this tremor. We describe a case of HT due to the rare artery of Percheron infarct and its successful treatment using deep brain stimulation. Case report: A 62-year-old woman with a right medial cerebral peduncle and bilateral thalamic stroke developed HT. Ventral intermediate nucleus (Vim) zona incerta (ZI) deep brain stimulation (DBS) surgery was performed, with improvement in her tremor. Discussion: Our case supports the theory that the more caudal ZI target in combination with Vim is beneficial in treating poorly DBS-responsive tremors such as HT.

Stroke is the second leading cause of death in the world and top cause of disability in the US.The plurality of electronic health records (EHR) provides an opportunity to study this disease in situ. Doing so requires accurately identifying stroke patients from medical records. So-called "EHR phenotyping" algorithms, however, are difficult and time consuming to create and often must rely on incomplete information. There is an opportunity to use machine learning to speed up and ease the process of cohort and feature identification. We systematically compared and evaluated the ability of several machine learning algorithms to automatically phenotype acute ischemic stroke patients. We found that these algorithms can achieve high performance (e.g. average AUROC=0.955%) with little to no manual feature curation, and other performance evaluators differentiate each model's ability to generalize. We also found that commonly available data such as diagnosis codes can be used as noisy proxies for training when a reference panel of stroke patients is unavailable. Additionally, we find some limitations when the algorithms are used to place patients into stroke risk classes. We used these models to identify unidentified stroke patients from our patient population of 6.4 million and find expected rates of stroke across the population.Stroke is a highly heterogeneous and complex disease that is a leading cause of death and 2 severe disability for millions of survivors worldwide. 1 It is characterized by an acute focal loss 3 of neurological function and is primarily caused by loss of blood flow to a specific area of the 4 brain. There are many identifiable risk factors for stroke, which include various metabolic, 5 cardiovascular, and coagulative diseases, medications, lifestyle, and demographics. Triggers 6 2 such as pollution, infection, and inflammatory disorders, further complicate the etiology of 7 the disease. 2 Most of the unidentified risk, up to 40%, is thought to be genetic. 3 Accurate de-8 termination of the etiology of disease is essential for risk stratification and optimal treatment, 9 but this can be difficult as up to 35% of strokes are of undetermined cause. 4,5 10 Traditionally, identifying a stroke patient requires the integration of multiple facets of data 11 including medical notes, labs, imaging reports, and medical experience by neurologists. This 12 requires time consuming manual review. Stroke diagnoses have also been missed or falsely 13 assigned 6 . Stroke is often coded in outpatient follow up, so a hospital EHR may not have 14 the ICD9 or 10 data to identify stroke with high sensitivity. Given the incompleteness of 15 identifying patients using stroke-specific ICD9 codes and the availability of structured multi-16 modal data in EHR settings, there is a need to move beyond laborious manual chart review 17 and to automate identification of stroke patients with commonly accessible EHR data. 18Phenotyping algorithms must address two tasks: curating features to define the pheno-19 type, and identifying ca...

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Introduction: By virtue of their infection, HIV+ patients are subject to chronic inflammation, and show an increased risk for stroke when compared to uninfected controls. We hypothesized that HIV infection would be associated with increased atherosclerotic plaque vulnerability. Methods: Large brain arteries from 162 autopsied individuals (84 with HIV) were stained for metalloproteinase (MMP)-2, MMP-3, MMP-9, Caspase-3, and tumor necrosis factor (TNF)-α, factors reported to increase plaque vulnerability. We measured intensity of staining for each protein in the fibrous cap and scored each as 0 (most intense staining not in cap) or 1 (most intense staining in cap). We then added the ratings from the five stains and created a vulnerability score ranging from 0-5. CD3+ cells (lymphocytes) and CD68+ cells (macrophages) were also rated using semi-quantitative scores. Rating was blind to HIV status. We constructed multilevel models to obtain the β estimates and their 95% confidence intervals (β, 95%CI), adjusting for demographic characteristics, vascular risk factors, and HIV-related immune variables. Results: For the entire sample, the plaque vulnerability score was associated with higher degree of luminal stenosis (0.04, 0.02-0.06) and larger arterial size (0.5 per mm, 0.2-0.8), but not with HIV (-0.65, -2.24-0.95). However, in a subset of individuals with atherosclerosis (n=21), there was a statistical interaction (P<0.04) between HIV and vulnerability score. In a stratified model in HIV+ cases only, a CD4 count of > 200 cells/ul at death was associated with higher vulnerability score (1.42, 0.08-2.76) compared with subjects who remained immunosuppressed. Predictors of vulnerability score among HIV+ subjects included higher CD3 score (1.12, 0.15-2.09), older age (0.18 per year, 0.12-0.25), diabetes (3.00, 1.69-4.24), lower CD4 nadir (-0.01, -0.02to -0.007), antiretroviral use at death (-2.08, -3.84 to -0.32). Conclusions: Plaque vulnerability is positively associated with a higher blood CD4 count, lower CD4 nadir and higher lymphocytic inflammation in brain arteries among HIV+ cases. Investigating the role of HIV-related chronic inflammation and plaque vulnerability may help us better understand the higher risk of stroke among those with HIV.