Publications by authors named "Ramin Zand"

105 Publications

Outcome after acute ischemic stroke is linked to sex-specific lesion patterns.

Nat Commun 2021 06 2;12(1):3289. Epub 2021 Jun 2.

Department of Neurology, Washington University School of Medicine & Barnes-Jewish Hospital, St Louis, MO, USA.

Acute ischemic stroke affects men and women differently. In particular, women are often reported to experience higher acute stroke severity than men. We derived a low-dimensional representation of anatomical stroke lesions and designed a Bayesian hierarchical modeling framework tailored to estimate possible sex differences in lesion patterns linked to acute stroke severity (National Institute of Health Stroke Scale). This framework was developed in 555 patients (38% female). Findings were validated in an independent cohort (n = 503, 41% female). Here, we show brain lesions in regions subserving motor and language functions help explain stroke severity in both men and women, however more widespread lesion patterns are relevant in female patients. Higher stroke severity in women, but not men, is associated with left hemisphere lesions in the vicinity of the posterior circulation. Our results suggest there are sex-specific functional cerebral asymmetries that may be important for future investigations of sex-stratified approaches to management of acute ischemic stroke.
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http://dx.doi.org/10.1038/s41467-021-23492-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8172535PMC
June 2021

Serial magnetic resonance imaging findings during severe attacks of familial hemiplegic migraine type 2: a case report.

BMC Neurol 2021 Apr 21;21(1):173. Epub 2021 Apr 21.

Department of Neurology, Neuroscience Institute, Geisinger Health System, 100 N Academy Ave, PA, Danville, USA.

Background: Hemiplegic migraines represent a heterogeneous disorder with various presentations. Hemiplegic migraines are classified as sporadic or familial based on the presence of family history, but both subtypes have an underlying genetic etiology. Mutations in the ATP1A2 gene are responsible for Familial Hemiplegic type 2 (FHM2) or the sporadic hemiplegic migraine (SHM) counterpart if there is no family history of the disorder. Manifestations include migraine with aura and hemiparesis along with a variety of other symptoms likely dependent upon the specific mutation(s) present.

Case Presentation: We report the case of an adult man who presented with headache, aphasia, and right-sided weakness. Workup for stroke and various infectious agents was unremarkable during the patient's extended hospital stay. We emphasize the changes in the Magnetic Resonance Imaging (MRI) over time and the delay from onset of symptoms to MRI changes in Isotropic Diffusion Map (commonly referred to as Diffusion Weighted Imaging (DWI)) as well as Apparent Diffusion Coefficient (ADC).

Conclusions: We provide a brief review of imaging findings correlated with signs/symptoms and specific mutations in the ATP1A2 gene reported in the literature. Description of the various mutations and consequential presentations may assist neurologists in identifying cases of Hemiplegic Migraine, which may include transient changes in ADC and DWI imaging throughout the course of an attack.
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http://dx.doi.org/10.1186/s12883-021-02201-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8059280PMC
April 2021

Machine Learning-Enabled 30-Day Readmission Model for Stroke Patients.

Front Neurol 2021 31;12:638267. Epub 2021 Mar 31.

Department of Molecular and Functional Genomics, Geisinger Health System, Danville, PA, United States.

Hospital readmissions impose a substantial burden on the healthcare system. Reducing readmissions after stroke could lead to improved quality of care especially since stroke is associated with a high rate of readmission. The goal of this study is to enhance our understanding of the predictors of 30-day readmission after ischemic stroke and develop models to identify high-risk individuals for targeted interventions. We used patient-level data from electronic health records (EHR), five machine learning algorithms (random forest, gradient boosting machine, extreme gradient boosting-XGBoost, support vector machine, and logistic regression-LR), data-driven feature selection strategy, and adaptive sampling to develop 15 models of 30-day readmission after ischemic stroke. We further identified important clinical variables. We included 3,184 patients with ischemic stroke (mean age: 71 ± 13.90 years, men: 51.06%). Among the 61 clinical variables included in the model, the National Institutes of Health Stroke Scale score above 24, insert indwelling urinary catheter, hypercoagulable state, and percutaneous gastrostomy had the highest importance score. The Model's AUC (area under the curve) for predicting 30-day readmission was 0.74 (95%CI: 0.64-0.78) with PPV of 0.43 when the XGBoost algorithm was used with ROSE-sampling. The balance between specificity and sensitivity improved through the sampling strategy. The best sensitivity was achieved with LR when optimized with feature selection and ROSE-sampling (AUC: 0.64, sensitivity: 0.53, specificity: 0.69). Machine learning-based models can be designed to predict 30-day readmission after stroke using structured data from EHR. Among the algorithms analyzed, XGBoost with ROSE-sampling had the best performance in terms of AUC while LR with ROSE-sampling and feature selection had the best sensitivity. Clinical variables highly associated with 30-day readmission could be targeted for personalized interventions. Depending on healthcare systems' resources and criteria, models with optimized performance metrics can be implemented to improve outcomes.
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http://dx.doi.org/10.3389/fneur.2021.638267DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8044392PMC
March 2021

Stroke in SARS-CoV-2 Infection: A Pictorial Overview of the Pathoetiology.

Front Cardiovasc Med 2021 29;8:649922. Epub 2021 Mar 29.

Neurology Department, Neuroscience Institute, Geisinger Health System, Danville, PA, United States.

Since the early days of the pandemic, there have been several reports of cerebrovascular complications during the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Numerous studies proposed a role for SARS-CoV-2 in igniting stroke. In this review, we focused on the pathoetiology of stroke among the infected patients. We pictured the results of the SARS-CoV-2 invasion to the central nervous system (CNS) via neuronal and hematogenous routes, in addition to viral infection in peripheral tissues with extensive crosstalk with the CNS. SARS-CoV-2 infection results in pro-inflammatory cytokine and chemokine release and activation of the immune system, COVID-19-associated coagulopathy, endotheliitis and vasculitis, hypoxia, imbalance in the renin-angiotensin system, and cardiovascular complications that all may lead to the incidence of stroke. Critically ill patients, those with pre-existing comorbidities and patients taking certain medications, such as drugs with elevated risk for arrhythmia or thrombophilia, are more susceptible to a stroke after SARS-CoV-2 infection. By providing a pictorial narrative review, we illustrated these associations in detail to broaden the scope of our understanding of stroke in SARS-CoV-2-infected patients. We also discussed the role of antiplatelets and anticoagulants for stroke prevention and the need for a personalized approach among patients with SARS-CoV-2 infection.
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http://dx.doi.org/10.3389/fcvm.2021.649922DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8039152PMC
March 2021

Prediction of Long-Term Stroke Recurrence Using Machine Learning Models.

J Clin Med 2021 Mar 20;10(6). Epub 2021 Mar 20.

Geisinger Neuroscience Institute, Geisinger Health System, Danville, PA 17822, USA.

Background: The long-term risk of recurrent ischemic stroke, estimated to be between 17% and 30%, cannot be reliably assessed at an individual level. Our goal was to study whether machine-learning can be trained to predict stroke recurrence and identify key clinical variables and assess whether performance metrics can be optimized.

Methods: We used patient-level data from electronic health records, six interpretable algorithms (Logistic Regression, Extreme Gradient Boosting, Gradient Boosting Machine, Random Forest, Support Vector Machine, Decision Tree), four feature selection strategies, five prediction windows, and two sampling strategies to develop 288 models for up to 5-year stroke recurrence prediction. We further identified important clinical features and different optimization strategies.

Results: We included 2091 ischemic stroke patients. Model area under the receiver operating characteristic (AUROC) curve was stable for prediction windows of 1, 2, 3, 4, and 5 years, with the highest score for the 1-year (0.79) and the lowest score for the 5-year prediction window (0.69). A total of 21 (7%) models reached an AUROC above 0.73 while 110 (38%) models reached an AUROC greater than 0.7. Among the 53 features analyzed, age, body mass index, and laboratory-based features (such as high-density lipoprotein, hemoglobin A1c, and creatinine) had the highest overall importance scores. The balance between specificity and sensitivity improved through sampling strategies.

Conclusion: All of the selected six algorithms could be trained to predict the long-term stroke recurrence and laboratory-based variables were highly associated with stroke recurrence. The latter could be targeted for personalized interventions. Model performance metrics could be optimized, and models can be implemented in the same healthcare system as intelligent decision support for targeted intervention.
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http://dx.doi.org/10.3390/jcm10061286DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8003970PMC
March 2021

SARS-CoV-2 Is a Culprit for Some, but Not All Acute Ischemic Strokes: A Report from the Multinational COVID-19 Stroke Study Group.

J Clin Med 2021 Mar 1;10(5). Epub 2021 Mar 1.

Neurology Department, Neuroscience Institute, Geisinger Health System, Danville, PA 17822, USA.

Background: SARS-CoV-2 infected patients are suggested to have a higher incidence of thrombotic events such as acute ischemic strokes (AIS). This study aimed at exploring vascular comorbidity patterns among SARS-CoV-2 infected patients with subsequent stroke. We also investigated whether the comorbidities and their frequencies under each subclass of TOAST criteria were similar to the AIS population studies prior to the pandemic.

Methods: This is a report from the Multinational COVID-19 Stroke Study Group. We present an original dataset of SASR-CoV-2 infected patients who had a subsequent stroke recorded through our multicenter prospective study. In addition, we built a dataset of previously reported patients by conducting a systematic literature review. We demonstrated distinct subgroups by clinical risk scoring models and unsupervised machine learning algorithms, including hierarchical K-Means (ML-K) and Spectral clustering (ML-S).

Results: This study included 323 AIS patients from 71 centers in 17 countries from the original dataset and 145 patients reported in the literature. The unsupervised clustering methods suggest a distinct cohort of patients (ML-K: 36% and ML-S: 42%) with no or few comorbidities. These patients were more than 6 years younger than other subgroups and more likely were men (ML-K: 59% and ML-S: 60%). The majority of patients in this subgroup suffered from an embolic-appearing stroke on imaging (ML-K: 83% and ML-S: 85%) and had about 50% risk of large vessel occlusions (ML-K: 50% and ML-S: 53%). In addition, there were two cohorts of patients with large-artery atherosclerosis (ML-K: 30% and ML-S: 43% of patients) and cardioembolic strokes (ML-K: 34% and ML-S: 15%) with consistent comorbidity and imaging patterns. Binominal logistic regression demonstrated that ischemic heart disease (odds ratio (OR), 4.9; 95% confidence interval (CI), 1.6-14.7), atrial fibrillation (OR, 14.0; 95% CI, 4.8-40.8), and active neoplasm (OR, 7.1; 95% CI, 1.4-36.2) were associated with cardioembolic stroke.

Conclusions: Although a cohort of young and healthy men with cardioembolic and large vessel occlusions can be distinguished using both clinical sub-grouping and unsupervised clustering, stroke in other patients may be explained based on the existing comorbidities.
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http://dx.doi.org/10.3390/jcm10050931DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7957755PMC
March 2021

Genetic basis of lacunar stroke: a pooled analysis of individual patient data and genome-wide association studies.

Lancet Neurol 2021 05 25;20(5):351-361. Epub 2021 Mar 25.

Department of Laboratory Medicine, Institute of Biomedicine, Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden.

Background: The genetic basis of lacunar stroke is poorly understood, with a single locus on 16q24 identified to date. We sought to identify novel associations and provide mechanistic insights into the disease.

Methods: We did a pooled analysis of data from newly recruited patients with an MRI-confirmed diagnosis of lacunar stroke and existing genome-wide association studies (GWAS). Patients were recruited from hospitals in the UK as part of the UK DNA Lacunar Stroke studies 1 and 2 and from collaborators within the International Stroke Genetics Consortium. Cases and controls were stratified by ancestry and two meta-analyses were done: a European ancestry analysis, and a transethnic analysis that included all ancestry groups. We also did a multi-trait analysis of GWAS, in a joint analysis with a study of cerebral white matter hyperintensities (an aetiologically related radiological trait), to find additional genetic associations. We did a transcriptome-wide association study (TWAS) to detect genes for which expression is associated with lacunar stroke; identified significantly enriched pathways using multi-marker analysis of genomic annotation; and evaluated cardiovascular risk factors causally associated with the disease using mendelian randomisation.

Findings: Our meta-analysis comprised studies from Europe, the USA, and Australia, including 7338 cases and 254 798 controls, of which 2987 cases (matched with 29 540 controls) were confirmed using MRI. Five loci (ICA1L-WDR12-CARF-NBEAL1, ULK4, SPI1-SLC39A13-PSMC3-RAPSN, ZCCHC14, ZBTB14-EPB41L3) were found to be associated with lacunar stroke in the European or transethnic meta-analyses. A further seven loci (SLC25A44-PMF1-BGLAP, LOX-ZNF474-LOC100505841, FOXF2-FOXQ1, VTA1-GPR126, SH3PXD2A, HTRA1-ARMS2, COL4A2) were found to be associated in the multi-trait analysis with cerebral white matter hyperintensities (n=42 310). Two of the identified loci contain genes (COL4A2 and HTRA1) that are involved in monogenic lacunar stroke. The TWAS identified associations between the expression of six genes (SCL25A44, ULK4, CARF, FAM117B, ICA1L, NBEAL1) and lacunar stroke. Pathway analyses implicated disruption of the extracellular matrix, phosphatidylinositol 5 phosphate binding, and roundabout binding (false discovery rate <0·05). Mendelian randomisation analyses identified positive associations of elevated blood pressure, history of smoking, and type 2 diabetes with lacunar stroke.

Interpretation: Lacunar stroke has a substantial heritable component, with 12 loci now identified that could represent future treatment targets. These loci provide insights into lacunar stroke pathogenesis, highlighting disruption of the vascular extracellular matrix (COL4A2, LOX, SH3PXD2A, GPR126, HTRA1), pericyte differentiation (FOXF2, GPR126), TGF-β signalling (HTRA1), and myelination (ULK4, GPR126) in disease risk.

Funding: British Heart Foundation.
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http://dx.doi.org/10.1016/S1474-4422(21)00031-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8062914PMC
May 2021

Adherence to anticoagulant guideline for atrial fibrillation: A large care gap among stroke patients in a rural population.

J Neurol Sci 2021 May 20;424:117410. Epub 2021 Mar 20.

Neurology Department, Neuroscience Institute, Geisinger Health System, Danville, PA, USA. Electronic address:

Objective: This study aimed to investigate the prevalence and factors associated with oral anticoagulant undertreatment of atrial fibrillation (AF) among a cohort of rural patients with stroke outcomes and examine how undertreatment may influence a patient's one-year survival after stroke.

Methods: This retrospective cohort study examined ischemic stroke patients with pre-stroke AF diagnosis from September 2003 to May 2019 and divided them into proper treatment and undertreatment group. Analysis included chi-square test, variance analysis, Kruskal-Wallis test, logistic regression, Kaplan-Meier estimator, and Cox proportional-hazards model.

Results: Out of 1062 ischemic stroke patients with a pre-stroke AF diagnosis, 1015 patients had a CHADS-VASc score ≥2, and 532 (52.4%) of those were undertreated. Median time from AF diagnosis to index stroke was significantly lower among undertreated patients (1.9 years vs. 3.6 years, p < 0.001). Other thromboembolism, excluding stroke, TIA, and myocardial infarction (OR 0.41, p < 0.001), the number of encounters per year (OR 0.90, p < 0.001), and the median time between AF diagnosis and stroke event (OR 0.86, p < 0.001) were negatively associated with undertreatment. Kaplan-Meier estimator showed no statistical difference in the one-year survival probability between groups (log-rank test, p = 0.29), while the Cox-Hazard model showed that age (HR 1.05, p < 0.001) and history of congestive heart failure (HR 1.88, p < 0.001) increased the risk of mortality.

Conclusions: More than half of our rural stroke patients with a pre-index AF diagnosis were not on guideline-recommended treatment. The study highlights a large care gap and an opportunity to improve AF management.
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http://dx.doi.org/10.1016/j.jns.2021.117410DOI Listing
May 2021

Polygenic Risk Scores Augment Stroke Subtyping.

Neurol Genet 2021 Apr 9;7(2):e560. Epub 2021 Mar 9.

Department of Molecular and Functional Genomics (J.L., D.J.C., V.A.), Weis Center for Research, Geisinger Health System; Neuroscience Institute (D.P.C., A.K., C.G., R.Z.), Geisinger Health System, Danville, PA; Biocomplexity Institute (V.A.), Virginia Tech, Blacksburg, VA; and Research Institute of Neurointervention (C.G.), Paracelsus Medical University, Salzburg, Austria.

Objective: To determine whether the polygenic risk score (PRS) derived from MEGASTROKE is associated with ischemic stroke (IS) and its subtypes in an independent tertiary health care system and to identify the PRS derived from gene sets of known biological pathways associated with IS.

Methods: Controls (n = 19,806/7,484, age ≥69/79 years) and cases (n = 1,184/951 for discovery/replication) of acute IS with European ancestry and clinical risk factors were identified by leveraging the Geisinger Electronic Health Record and chart review confirmation. All Geisinger MyCode patients with age ≥69/79 years and without any stroke-related diagnostic codes were included as low risk control. Genetic heritability and genetic correlation between Geisinger and MEGASTROKE (EUR) were calculated using the summary statistics of the genome-wide association study by linkage disequilibrium score regression. All PRS for any stroke (AS), any ischemic stroke (AIS), large artery stroke (LAS), cardioembolic stroke (CES), and small vessel stroke (SVS) were constructed by PRSice-2.

Results: A moderate heritability (10%-20%) for Geisinger sample as well as the genetic correlation between MEGASTROKE and the Geisinger cohort was identified. Variation of all 5 PRS significantly explained some of the phenotypic variations of Geisinger IS, and the increased by raising the cutoff for the age of controls. PRSLAS, PRSCES, and PRSSVS derived from low-frequency common variants provided the best fit for modeling ( = 0.015 for PRSLAS). Gene sets analyses highlighted the association of PRS with Gene Ontology terms (vascular endothelial growth factor, amyloid precursor protein, and atherosclerosis). The PRSLAS, PRSCES, and PRSSVS explained the most variance of the corresponding subtypes of Geisinger IS suggesting shared etiologies and corroborated Geisinger TOAST subtyping.

Conclusions: We provide the first evidence that PRSs derived from MEGASTROKE have value in identifying shared etiologies and determining stroke subtypes.
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http://dx.doi.org/10.1212/NXG.0000000000000560DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7943221PMC
April 2021

Comparison of losartan and amlodipine effects on the outcomes of patient with COVID-19 and primary hypertension: A randomised clinical trial.

Int J Clin Pract 2021 Jun 13;75(6):e14124. Epub 2021 Mar 13.

Tuberculosis and Lung Disease Research Center, Tabriz University of Medical Sciences, Tabriz, Iran.

Background: Controversy exists regarding the drug selection in hypertension (HTN) management in patients with COVID-19. This study aimed to compare the effects of losartan and amlodipine in patients with primary HTN and COVID-19.

Methods: In this randomised clinical trial, hospitalised patients with COVID-19 and primary HTN were enrolled in the study. One arm received losartan, 25 mg, twice a day and the other arm received amlodipine, 5 mg per day for 2 weeks. The main outcomes were compare 30-day mortality rate and length of hospital stay.

Results: The mean age of patients treated with losartan (N = 41) and amlodipine (N = 39) was 67.3 ± 14.8 and 60.1 ± 17.3 years, respectively (P value = .068). The length of hospital stay in losartan and amlodipine groups was 4.57 ± 2.59 and 7.30 ± 8.70 days, respectively (P value = .085). Also, the length of ICU admission in losartan and amlodipine group was 7.13 ± 5.99 and 7.15 ± 9.95 days, respectively (P value = .994). The 30-day mortality was two and five patients in losartan and amlodipine groups, respectively (P value = .241).

Conclusions: There was no priority in losartan or amlodipine administration in COVID-19 patients with primary HTN in decreasing mortality rate, hospital and ICU length stay. Further studies need to clarify the first-line anti-HTN medications in COVID-19.
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http://dx.doi.org/10.1111/ijcp.14124DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7995089PMC
June 2021

Cerebral venous sinus thrombosis associated with COVID-19: a case series and literature review.

J Neurol 2021 Feb 22. Epub 2021 Feb 22.

Clinical Neurology Research Center, Shiraz University of Medical Sciences, P.O.Box: 7193635899, Shiraz, Iran.

Background: Since the emergence of COVID-19 pandemic, several cases of cerebral venous sinus thrombosis (CVST) have been reported in SARS-CoV-2 infected individuals.

Methods: Consecutive patients with documented SARS-CoV-2 infection, as well as clinical and radiological characteristics of CVST, were reported from three teaching hospitals in the South West, North West, and the center of Iran between June and July 2020. We also searched the abstract archives until the end of August 2020 and gathered 28 reported cases. The diagnostic criteria for SARS-CoV-2 infection were determined according to SARS-CoV-2 detection in oropharyngeal or nasopharyngeal samples in clinically suspected patients. Demographics, prominent COVID-19 symptoms, confirmatory tests for SARS-CoV-2 infection diagnosis, the interval between the diagnosis of SARS-CoV-2 infection and CVST, clinical and radiological features of CVST, therapeutic strategies, CVST outcomes, rate of hemorrhagic transformation, and mortality rate were investigated.

Results: Six patients (31-62 years-old) with confirmed CVST and SARS-CoV-2 infection were admitted to our centers. Four patients had no respiratory symptoms of SARS-CoV-2 infection. Five patients developed the clinical manifestations of CVST and SARS-CoV-2 infection simultaneously. Three patients had known predisposing factors for CVST. Despite receiving CVST and SARS-CoV-2 infection treatments, four patients died. SARS-COV-2 associated CVST patients were older (49.26 vs. 37.77 years-old), had lower female/male ratio (1.42 vs. 2.19), and higher mortality rate (35.29% vs. 6.07%) than CVST not associated with COVID-19.

Conclusions: The role of SARS-CoV-2 as a "cause" versus an "additive contributor" remains to be elucidated. Practitioners should be aware of the possibility of CVST in SARS-CoV-2 infection.
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http://dx.doi.org/10.1007/s00415-021-10450-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7897893PMC
February 2021

Trends in ischemic stroke outcomes in a rural population in the United States.

J Neurol Sci 2021 Mar 9;422:117339. Epub 2021 Feb 9.

Geisinger Neuroscience Institute, Geisinger Health System, Danville, PA, United States. Electronic address:

Introduction: The stroke mortality rate has gradually declined due to improved interventions and controlled risk factors. We investigated the associated factors and trends in recurrence and all-cause mortality in ischemic stroke patients from a rural population in the United States between 2004 and 2018.

Methods: This was a retrospective cohort study based on electronic health records (EHR) data. A comprehensive stroke database called "Geisinger NeuroScience Ischemic Stroke (GNSIS)" was built for this study. Clinical data were extracted from multiple sources, including EHR and quality data.

Results: The cohort included in the study comprised of 8561 consecutive ischemic stroke patients (mean age: 70.1 ± 13.9 years, men: 51.6%, 95.1% Caucasian). Hypertension was the most prevalent risk factor (75.2%). The one-year recurrence and all-cause mortality rates were 6.3% and 16.1%, respectively. Although the one-year stroke recurrence increased during the study period, the one-year stroke mortality rate decreased significantly. Age > 65 years, atrial fibrillation or flutter, heart failure, and prior ischemic stroke were independently associated with one-year all-cause mortality in stratified Cox proportional hazards model. In the Cause-specific hazard model, diabetes, chronic kidney disease and age < 65 years were found to be associated with one-year ischemic stroke recurrence.

Conclusion: Although all-cause mortality after stroke has decreased, stroke recurrence has significantly increased in stroke patients from rural population between 2004 and 2018. Older age, atrial fibrillation or flutter, heart failure, and prior ischemic stroke were independently associated with one-year all-cause mortality while diabetes, chronic kidney disease and age less than 65 years were predictors of ischemic stroke recurrence.
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http://dx.doi.org/10.1016/j.jns.2021.117339DOI Listing
March 2021

GDF-15: Diagnostic, prognostic, and therapeutic significance in glioblastoma multiforme.

J Cell Physiol 2021 Aug 12;236(8):5564-5581. Epub 2021 Feb 12.

Center for Mitochondrial and Epigenomic Medicine, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.

Glioblastoma multiforme (GBM) is the commonest primary malignant brain tumor and has a remarkably weak prognosis. According to the aggressive form of GBM, understanding the accurate molecular mechanism associated with GBM pathogenesis is essential. Growth differentiation factor 15 (GDF-15) belongs to transforming growth factor-β superfamily with important roles to control biological processes. It affects cancer growth and progression, drug resistance, and metastasis. It also can promote stemness in many cancers, and also can stress reactions control, bone generation, hematopoietic growth, adipose tissue performance, and body growth, and contributes to cardiovascular disorders. The role GDF-15 to develop and progress cancer is complicated and remains unclear. GDF-15 possesses tumor suppressor properties, as well as an oncogenic effect. GDF-15 antitumorigenic and protumorigenic impacts on tumor development are linked to the cancer type and stage. However, the GDF-15 signaling and mechanism have not yet been completely identified because of no recognized cognate receptor.
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http://dx.doi.org/10.1002/jcp.30289DOI Listing
August 2021

Obesity and mortality after the first ischemic stroke: Is obesity paradox real?

PLoS One 2021 10;16(2):e0246877. Epub 2021 Feb 10.

Geisinger Neuroscience Institute, Geisinger Health System, Danville, Pennsylvania, United States of America.

Background And Purpose: Obesity is an established risk factor for ischemic stroke but the association of increased body mass index (BMI) with survival after ischemic stroke remains controversial. Many studies have shown that increased BMI has a "protective" effect on survival after stroke while other studies have debunked the "obesity paradox". This study aimed at examining the relationship between BMI and all-cause mortality at one year in first-time ischemic stroke patients using a large dataset extracted from different resources including electronic health records.

Methods: This was a retrospective cohort study of consecutive ischemic stroke patients captured in our Geisinger NeuroScience Ischemic Stroke (GNSIS) database. Survival in first-time ischemic stroke patients in different BMI categories was analyzed using Kaplan Meier survival curves. The predictors of mortality at one-year were assessed using a stratified Cox proportional hazards model.

Results: Among 6,703 first-time ischemic stroke patients, overweight and obese patients were found to have statistically decreased hazard ratio (HR) compared to the non-overweight patients (overweight patients- HR = 0.61 [95% CI, 0.52-0.72]; obese patients- HR = 0.56 [95% CI, 0.48-0.67]). Predictors with a significant increase in the hazard ratio for one-year mortality were age at the ischemic stroke event, history of neoplasm, atrial fibrillation/flutter, diabetes, myocardial infarction and heart failure.

Conclusion: Our study results support the obesity paradox in ischemic stroke patients as shown by a significantly decreased hazard ratio for one-year mortality among overweight and obese patients in comparison to non-overweight patients.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0246877PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7875337PMC
February 2021

Early Detection of Septic Shock Onset Using Interpretable Machine Learners.

J Clin Med 2021 Jan 15;10(2). Epub 2021 Jan 15.

Department of Molecular and Functional Genomics, Geisinger Health System, Danville, PA 17822, USA.

Background: Developing a decision support system based on advances in machine learning is one area for strategic innovation in healthcare. Predicting a patient's progression to septic shock is an active field of translational research. The goal of this study was to develop a working model of a clinical decision support system for predicting septic shock in an acute care setting for up to 6 h from the time of admission in an integrated healthcare setting.

Method: Clinical data from Electronic Health Record (EHR), at encounter level, were used to build a predictive model for progression from sepsis to septic shock up to 6 h from the time of admission; that is, , , and from admission. Eight different machine learning algorithms (Random Forest, XGBoost, C5.0, Decision Trees, Boosted Logistic Regression, Support Vector Machine, Logistic Regression, Regularized Logistic, and Bayes Generalized Linear Model) were used for model development. Two adaptive sampling strategies were used to address the class imbalance. Data from two sources (clinical and billing codes) were used to define the case definition (septic shock) using the Centers for Medicare & Medicaid Services (CMS) Sepsis criteria. The model assessment was performed using Area under Receiving Operator Characteristics (AUROC), sensitivity, and specificity. Model predictions for each feature window (1, 3 and 6 h from admission) were consolidated.

Results: Retrospective data from April 2005 to September 2018 were extracted from the EHR, Insurance Claims, Billing, and Laboratory Systems to create a dataset for septic shock detection. The clinical criteria and billing information were used to label patients into two classes-septic shock patients and sepsis patients at three different time points from admission, creating two different case-control cohorts. Data from 45,425 unique in-patient visits were used to build 96 prediction models comparing clinical-based definition versus billing-based information as the gold standard. Of the 24 consolidated models (based on eight machine learning algorithms and three feature windows), four models reached an AUROC greater than 0.9. Overall, all the consolidated models reached an AUROC of at least 0.8820 or higher. Based on the AUROC of 0.9483, the best model was based on Random Forest, with a sensitivity of 83.9% and specificity of 88.1%. The sepsis detection window at 6 h outperformed the 1 and 3-h windows. The sepsis definition based on clinical variables had improved performance when compared to the sepsis definition based on only billing information.

Conclusion: This study corroborated that machine learning models can be developed to predict septic shock using clinical and administrative data. However, the use of clinical information to define septic shock outperformed models developed based on only administrative data. Intelligent decision support tools can be developed and integrated into the EHR and improve clinical outcomes and facilitate the optimization of resources in real-time.
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http://dx.doi.org/10.3390/jcm10020301DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7830968PMC
January 2021

COVID-19: Neuroimaging Features of a Pandemic.

J Neuroimaging 2021 03 9;31(2):228-243. Epub 2021 Jan 9.

Department of Neurology, St. Josef-Hospital Bochum, Ruhr University Bochum, Bochum, Germany.

Background And Purpose: The ongoing Coronavirus Disease 2019 (COVID-19) pandemic is caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). COVID-19 is occasionally associated with manifold diseases of the central nervous system (CNS). We sought to present the neuroimaging features of such CNS involvement. In addition, we sought to identify typical neuroimaging patterns that could indicate possible COVID-19-associated neurological manifestations.

Methods: In this systematic literature review, typical neuroimaging features of cerebrovascular diseases and inflammatory processes associated with COVID-19 were analyzed. Reports presenting individual patient data were included in further quantitative analysis with descriptive statistics.

Results: We identified 115 studies reporting a total of 954 COVID-19 patients with associated neurological manifestations and neuroimaging alterations. A total of 95 (82.6%) of the identified studies were single case reports or case series, whereas 660 (69.2%) of the reported cases included individual information and were thus included in descriptive statistical analysis. Ischemia with neuroimaging patterns of large vessel occlusion event was revealed in 59.9% of ischemic stroke patients, whereas 69.2% of patients with intracerebral hemorrhage exhibited bleeding in a location that was not associated with hypertension. Callosal and/or juxtacortical location was identified in 58.7% of cerebral microbleed positive images. Features of hemorrhagic necrotizing encephalitis were detected in 28.8% of patients with meningo-/encephalitis.

Conclusions: Manifold CNS involvement is increasingly reported in COVID-19 patients. Typical and atypical neuroimaging features have been observed in some disease entities, so that familiarity with these imaging patterns appears reasonable and may assist clinicians in the differential diagnosis of COVID-19 CNS manifestations.
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http://dx.doi.org/10.1111/jon.12819DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8014046PMC
March 2021

Sex disparity in long-term stroke recurrence and mortality in a rural population in the United States.

Ther Adv Neurol Disord 2020 18;13:1756286420971895. Epub 2020 Dec 18.

Geisinger NeuroScience Institute, Geisinger Health System, 100 North Academy Ave., Danville, PA 17822, USA.

Background: Several studies suggest women may be disproportionately affected by poorer stroke outcomes than men. This study aims to investigate whether women have a higher risk of all-cause mortality and recurrence after an ischemic stroke than men in a rural population in central Pennsylvania, United States.

Methods: We analyzed consecutive ischemic stroke patients captured in the Geisinger NeuroScience Ischemic Stroke research database from 2004 to 2019. Kaplan-Meier (KM) estimator curves stratified by gender and age were used to plot survival probabilities and Cox Proportional Hazards Ratios were used to analyze outcomes of all-cause mortality and the composite outcome of ischemic stroke recurrence or death. Fine-Gray Competing Risk models were used for the outcome of recurrent ischemic stroke, with death as the competing risk. Two models were generated; Model 1 was adjusted by data-driven associated health factors, and Model 2 was adjusted by traditional vascular risk factors.

Results: Among 8900 adult ischemic stroke patients [median age of 71.6 (interquartile range: 61.1-81.2) years and 48% women], women had a higher crude all-cause mortality. The KM curves demonstrated a 63.3% survival in women compared with a 65.7% survival in men ( = 0.003) at 5 years; however, the survival difference was not present after controlling for covariates, including age, atrial fibrillation or flutter, myocardial infarction, diabetes mellitus, dyslipidemia, heart failure, chronic lung diseases, rheumatic disease, chronic kidney disease, neoplasm, peripheral vascular disease, past ischemic stroke, past hemorrhagic stroke, and depression. There was no adjusted or unadjusted sex difference in terms of recurrent ischemic stroke or composite outcome.

Conclusion: Sex was not an independent risk factor for all-cause mortality and ischemic stroke recurrence in the rural population in central Pennsylvania.
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http://dx.doi.org/10.1177/1756286420971895DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7750897PMC
December 2020

Increasing the Density of Laboratory Measures for Machine Learning Applications.

J Clin Med 2020 Dec 30;10(1). Epub 2020 Dec 30.

Geisinger Medical Center, Neuroscience Institute, Danville, PA 17822, USA.

Background: The imputation of missingness is a key step in Electronic Health Records (EHR) mining, as it can significantly affect the conclusions derived from the downstream analysis in translational medicine. The missingness of laboratory values in EHR is not at random, yet imputation techniques tend to disregard this key distinction. Consequently, the development of an adaptive imputation strategy designed specifically for EHR is an important step in improving the data imbalance and enhancing the predictive power of modeling tools for healthcare applications.

Method: We analyzed the laboratory measures derived from Geisinger's EHR on patients in three distinct cohorts-patients tested for (Cdiff) infection, patients with a diagnosis of inflammatory bowel disease (IBD), and patients with a diagnosis of hip or knee osteoarthritis (OA). We extracted Logical Observation Identifiers Names and Codes (LOINC) from which we excluded those with 75% or more missingness. The comorbidities, primary or secondary diagnosis, as well as active problem lists, were also extracted. The adaptive imputation strategy was designed based on a hybrid approach. The comorbidity patterns of patients were transformed into latent patterns and then clustered. Imputation was performed on a cluster of patients for each cohort independently to show the generalizability of the method. The results were compared with imputation applied to the complete dataset without incorporating the information from comorbidity patterns.

Results: We analyzed a total of 67,445 patients (11,230 IBD patients, 10,000 OA patients, and 46,215 patients tested for infection). We extracted 495 LOINC and 11,230 diagnosis codes for the IBD cohort, 8160 diagnosis codes for the Cdiff cohort, and 2042 diagnosis codes for the OA cohort based on the primary/secondary diagnosis and active problem list in the EHR. Overall, the most improvement from this strategy was observed when the laboratory measures had a higher level of missingness. The best root mean square error (RMSE) difference for each dataset was recorded as -35.5 for the Cdiff, -8.3 for the IBD, and -11.3 for the OA dataset.

Conclusions: An adaptive imputation strategy designed specifically for EHR that uses complementary information from the clinical profile of the patient can be used to improve the imputation of missing laboratory values, especially when laboratory codes with high levels of missingness are included in the analysis.
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http://dx.doi.org/10.3390/jcm10010103DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7795258PMC
December 2020

COVID-19 and cerebrovascular diseases: a comprehensive overview.

Ther Adv Neurol Disord 2020 8;13:1756286420978004. Epub 2020 Dec 8.

Fourth Department of Internal Medicine, 'Attikon' University Hospital, National and Kapodistrian University of Athens, Athens, Greece.

Neurological manifestations are not uncommon during infection with the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). A clear association has been reported between cerebrovascular disease and coronavirus disease 2019 (COVID-19). However, whether this association is causal or incidental is still unknown. In this narrative review, we sought to present the possible pathophysiological mechanisms linking COVID-19 and cerebrovascular disease, describe the stroke syndromes and their prognosis and discuss several clinical, radiological, and laboratory characteristics that may aid in the prompt recognition of cerebrovascular disease during COVID-19. A systematic literature search was conducted, and relevant information was abstracted. Angiotensin-converting enzyme-2 receptor dysregulation, uncontrollable immune reaction and inflammation, coagulopathy, COVID-19-associated cardiac injury with subsequent cardio-embolism, complications due to critical illness and prolonged hospitalization can all contribute as potential etiopathogenic mechanisms leading to diverse cerebrovascular clinical manifestations. Acute ischemic stroke, intracerebral hemorrhage, and cerebral venous sinus thrombosis have been described in case reports and cohorts of COVID-19 patients with a prevalence ranging between 0.5% and 5%. SARS-CoV-2-positive stroke patients have higher mortality rates, worse functional outcomes at discharge and longer duration of hospitalization as compared with SARS-CoV-2-negative stroke patients in different cohort studies. Specific demographic, clinical, laboratory and radiological characteristics may be used as 'red flags' to alarm clinicians in recognizing COVID-19-related stroke.
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http://dx.doi.org/10.1177/1756286420978004DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7727052PMC
December 2020

The Impact of SARS-CoV-2 on Stroke Epidemiology and Care: A Meta-Analysis.

Ann Neurol 2021 02 9;89(2):380-388. Epub 2020 Dec 9.

Second Department of Neurology, Attikon Hospital, School of Medicine, National and Kapodistrian University of Athens, Athens, Greece.

Objective: Emerging data indicate an increased risk of cerebrovascular events with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and highlight the potential impact of coronavirus disease (COVID-19) on the management and outcomes of acute stroke. We conducted a systematic review and meta-analysis to evaluate the aforementioned considerations.

Methods: We performed a meta-analysis of observational cohort studies reporting on the occurrence and/or outcomes of patients with cerebrovascular events in association with their SARS-CoV-2 infection status. We used a random-effects model. Summary estimates were reported as odds ratios (ORs) and corresponding 95% confidence intervals (CIs).

Results: We identified 18 cohort studies including 67,845 patients. Among patients with SARS-CoV-2, 1.3% (95% CI = 0.9-1.6%, I = 87%) were hospitalized for cerebrovascular events, 1.1% (95% CI = 0.8-1.3%, I = 85%) for ischemic stroke, and 0.2% (95% CI = 0.1-0.3%, I = 64%) for hemorrhagic stroke. Compared to noninfected contemporary or historical controls, patients with SARS-CoV-2 infection had increased odds of ischemic stroke (OR = 3.58, 95% CI = 1.43-8.92, I = 43%) and cryptogenic stroke (OR = 3.98, 95% CI = 1.62-9.77, I = 0%). Diabetes mellitus was found to be more prevalent among SARS-CoV-2 stroke patients compared to noninfected historical controls (OR = 1.39, 95% CI = 1.00-1.94, I = 0%). SARS-CoV-2 infection status was not associated with the likelihood of receiving intravenous thrombolysis (OR = 1.42, 95% CI = 0.65-3.10, I = 0%) or endovascular thrombectomy (OR = 0.78, 95% CI = 0.35-1.74, I = 0%) among hospitalized ischemic stroke patients during the COVID-19 pandemic. Odds of in-hospital mortality were higher among SARS-CoV-2 stroke patients compared to noninfected contemporary or historical stroke patients (OR = 5.60, 95% CI = 3.19-9.80, I = 45%).

Interpretation: SARS-CoV-2 appears to be associated with an increased risk of ischemic stroke, and potentially cryptogenic stroke in particular. It may also be related to an increased mortality risk. ANN NEUROL 2021;89:380-388.
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http://dx.doi.org/10.1002/ana.25967DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7753413PMC
February 2021

Cysteine-Altering Variants Are a Risk Factor for Stroke in the Elderly Population.

Stroke 2020 12 9;51(12):3562-3569. Epub 2020 Nov 9.

Neuroscience Institute, Geisinger, Danville, PA (A.K., C.J.G., R.Z.).

Background And Purpose: Cysteine altering variants, which have previously been exclusively associated with the rare hereditary small vessel disease cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy, have a population frequency of 1:300 worldwide. Using a large population database, and taking genotype as a starting point, we aimed to determine whether individuals harboring a cysteine altering variant have a higher load of small vessel disease markers on brain magnetic resonance imaging than controls, as well as a higher risk of stroke and cognitive impairment.

Methods: A cross-sectional study using integrated clinical, neuroimaging, and whole-exome sequencing data of 92 456 participants from the Geisinger DiscovEHR initiative cohort. The case group consisted of individuals harboring a cysteine altering variant (n=118). The control group consisted of randomly selected age- and sex-matched individuals who did not have any nonsynonymous variants in (n=184). Medical records including brain magnetic resonance imagings were evaluated for clinical and neuroimaging findings associated with small vessel disease. Group comparisons were done using Fisher exact test and ordinal logistic regression models. Risk of stroke was assessed using Cox regression.

Results: Of the 118 cases, 39.0% were men, mean age 58.1±16.9 years; 12.6% had a history of stroke, compared with 4.9% of controls. The risk of stroke was significantly increased after age 65 years (hazard ratio, 6.0 [95% CI, 1.4-26.3]). Dementia, mild cognitive impairment, migraine with aura and depression were equally prevalent in cases and controls. Twenty-nine cases (25%) and 45 controls (24%) had an available brain magnetic resonance imaging. After age 65 years, cases had a higher white matter lesion burden and more lacunes. A severe small vessel disease phenotype compatible with cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy was rarely seen.

Conclusions: Cysteine altering variants are an important contributor to the risk of stroke, lacunes, and white matter hyperintensities in the elderly population.
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http://dx.doi.org/10.1161/STROKEAHA.120.030343DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7678653PMC
December 2020

Replication of Top Loci From COL4A1/2 Associated With White Matter Hyperintensity Burden in Patients With Ischemic Stroke.

Stroke 2020 12 5;51(12):3751-3755. Epub 2020 Nov 5.

Department of Neurosurgery (C.J.G.), Neuroscience Institute, Geisinger, Danville, PA.

Background And Purpose: The purpose of this study was to replicate the top loci associated with white matter hyperintensity (WMH) phenotypes identified by large genome-wide association studies and the loci identified from the previous candidate gene studies.

Methods: A total of 946 Geisinger MyCode patients with acute ischemic stroke with validated European ancestry and magnetic resonance imaging data were included in this study. Log-transformed WMH volume, as a quantitative trait, was calculated by a fully automated quantification process. The genome-wide association studies was carried out by a linear mixed regression model (GEMMA). A candidate-single nucleotide polymorphism analysis by including known single nucleotide polymorphisms, reported from a meta-analysis and several large GWAS for WMH, was conducted in all cases and binary converted extreme cases.

Results: No genome-wide significantly associated variants were identified. In a candidate-single nucleotide polymorphism study, rs9515201 () and rs3744028 (), 2 known genetic loci, showed nominal or trend of association with the WMH volume (β=0.13 and =0.001 for rs9515201; β=0.094 and =0.094 for rs3744028), and replicated in a subset of extreme cases versus controls (odds ratio=1.78, =7.74×10 for rs9515201; odds ratio=1.53, =0.047 for rs3744028, respectively). MTHFR677 cytosine/thymine (rs1801133) also showed an association with the binary WMH with odds ratio=1.47 for T allele (=0.019).

Conclusions: Replication of COL4A1/2 associated with WMH reassures that the genetic risk factors for monogenic and polygenic ischemic stroke are shared at gene level.
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http://dx.doi.org/10.1161/STROKEAHA.120.030260DOI Listing
December 2020

Cerebral venous sinus thrombosis associated with SARS-CoV-2; a multinational case series.

J Neurol Sci 2020 12 14;419:117183. Epub 2020 Oct 14.

Neurology Department, Neuroscience Institute, Geisinger Health System, PA, USA; Neurology Department, University of Tennessee Health Science Center, TN, USA. Electronic address:

Background: SARS-CoV-2 induced coagulopathy can lead to thrombotic complications such as stroke. Cerebral venous sinus thrombosis (CVST) is a less common type of stroke which might be triggered by COVID-19. We present a series of CVST cases with SARS-CoV-2 infection.

Methods: In a multinational retrospective study, we collected all cases of CVST in SARS-CoV-2 infected patients admitted to nine tertiary stroke centers from the beginning of the pandemic to June 30th, 2020. We compared the demographics, clinical and radiological characteristics, risk factors, and outcome of these patients with a control group of non-SARS-CoV-2 infected CVST patients in the same seasonal period of the years 2012-2016 from the country where the majority of cases were recruited.

Results: A total of 13 patients fulfilled the inclusion criteria (62% women, mean age 50.9 ± 11.2 years). Six patients were discharged with good outcomes (mRS ≤ 2) and three patients died in hospital. Compared to the control group, the SARS-CoV-2 infected patients were significantly older (50.9 versus 36.7 years, p < 0.001), had a lower rate of identified CVST risk factors (23.1% versus 84.2%, p < 0.001), had more frequent cortical vein involvement (38.5% versus 10.5%, p: 0.025), and a non-significant higher rate of in-hospital mortality (23.1% versus 5.3%, p: 0.073).

Conclusion: CVST should be considered as potential comorbidity in SARS-CoV-2 infected patients presenting with neurological symptoms. Our data suggest that compared to non-SARS-CoV-2 infected patients, CVST occurs in older patients, with lower rates of known CVST risk factors and might lead to a poorer outcome in the SARS-CoV-2 infected group.
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http://dx.doi.org/10.1016/j.jns.2020.117183DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7556283PMC
December 2020

Stroke Care Trends During COVID-19 Pandemic in Zanjan Province, Iran. From the CASCADE Initiative: Statistical Analysis Plan and Preliminary Results.

J Stroke Cerebrovasc Dis 2020 Dec 16;29(12):105321. Epub 2020 Sep 16.

Westchester Medical Center Health Network, Director of Neurocritical Care and Emergency Neurological Services, Valhalla, NY, USA; Westchester Medical Center Health Network, New York Medical College, Valhalla, NY, USA.

Background: The emergence of the COVID-19 pandemic has significantly impacted global healthcare systems and this may affect stroke care and outcomes. This study examines the changes in stroke epidemiology and care during the COVID-19 pandemic in Zanjan Province, Iran.

Methods: This study is part of the CASCADE international initiative. From February 18, 2019, to July 18, 2020, we followed ischemic and hemorrhagic stroke hospitalization rates and outcomes in Valiasr Hospital, Zanjan, Iran. We used a Bayesian hierarchical model and an interrupted time series analysis (ITS) to identify changes in stroke hospitalization rate, baseline stroke severity [measured by the National Institutes of Health Stroke Scale (NIHSS)], disability [measured by the modified Rankin Scale (mRS)], presentation time (last seen normal to hospital presentation), thrombolytic therapy rate, median door-to-needle time, length of hospital stay, and in-hospital mortality. We compared in-hospital mortality between study periods using Cox-regression model.

Results: During the study period, 1,026 stroke patients were hospitalized. Stroke hospitalization rates per 100,000 population decreased from 68.09 before the pandemic to 44.50 during the pandemic, with a significant decline in both Bayesian [Beta: -1.034; Standard Error (SE): 0.22, 95% CrI: -1.48, -0.59] and ITS analysis (estimate: -1.03, SE = 0.24, p < 0.0001). Furthermore, we observed lower admission rates for patients with mild (NIHSS < 5) ischemic stroke (p < 0.0001). Although, the presentation time and door-to-needle time did not change during the pandemic, a lower proportion of patients received thrombolysis (-10.1%; p = 0.004). We did not see significant changes in admission rate to the stroke unit and in-hospital mortality rate; however, disability at discharge increased (p < 0.0001).

Conclusion: In Zanjan, Iran, the COVID-19 pandemic has significantly impacted stroke outcomes and altered the delivery of stroke care. Observed lower admission rates for milder stroke may possibly be due to fear of exposure related to COVID-19. The decrease in patients treated with thrombolysis and the increased disability at discharge may indicate changes in the delivery of stroke care and increased pressure on existing stroke acute and subacute services. The results of this research will contribute to a similar analysis of the larger CASCADE dataset in order to confirm findings at a global scale and improve measures to ensure the best quality of care for stroke patients during the COVID-19 pandemic.
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http://dx.doi.org/10.1016/j.jstrokecerebrovasdis.2020.105321DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7494258PMC
December 2020

A 5-Decade Analysis of Incidence Trends of Ischemic Stroke After Transient Ischemic Attack: A Systematic Review and Meta-analysis.

JAMA Neurol 2021 01;78(1):77-87

Department of Neurology, Geisinger Neuroscience Institute, Geisinger Health System, Danville, Pennsylvania.

Importance: Management of transient ischemic attack (TIA) has gained significant attention during the past 25 years after several landmark studies indicated the high incidence of a subsequent stroke.

Objective: To calculate the pooled event rate of subsequent ischemic stroke within 2, 7, 30, and 90 days of a TIA and compare this incidence among the population with TIA recruited before 1999 (group A), from 1999 to 2007 (group B), and after 2007 (group C).

Data Sources: All published studies of TIA outcomes were obtained by searching PubMed from 1996, to the last update on January 31, 2020, irrespective of the study design, document type, or language.

Study Selection: Of 11 516 identified citations, 175 articles were relevant to this review. Both the classic time-based definition of TIA and the new tissue-based definition were accepted. Studies with a combined record of patients with TIA and ischemic stroke, without clinical evaluation for the index TIA, with diagnosis of index TIA event after ischemic stroke occurrence, with low suspicion for TIA, or duplicate reports of the same database were excluded.

Data Extraction And Synthesis: The study was conducted and reported according to the PRISMA, MOOSE, and EQUATOR guidelines. Critical appraisal and methodological quality assessment used the Quality in Prognosis Studies tool. Publication bias was visualized by funnel plots and measured by the Begg-Mazumdar rank correlation Kendall τ2 statistic and Egger bias test. Data were pooled using double arcsine transformations, DerSimonian-Laird estimator, and random-effects models.

Main Outcomes And Measures: The proportion of the early ischemic stroke after TIA within 4 evaluation intervals (2, 7, 30, and 90 days) was considered as effect size.

Results: Systematic review yielded 68 unique studies with 223 866 unique patients from 1971 to 2019. The meta-analysis included 206 455 patients (58% women) during a span of 4 decades. The overall subsequent ischemic stroke incidence rates were estimated as 2.4% (95% CI, 1.8%-3.2%) within 2 days, 3.8% (95% CI, 2.5%-5.4%) within 7 days, 4.1% (95% CI, 2.4%-6.3%) within 30 days, and 4.7% (95% CI, 3.3%-6.4%) within 90 days. There was a recurrence risk of 3.4% among group A in comparison with 2.1% in group B or 2.1% in group C within 2 days; 5.5% in group A vs 2.9% in group B or 3.2% in group C within 7 days; 6.3% in group A vs 2.9% in group B or 3.4% in group C within 30 days, and 7.4% in group A vs 3.9% in group B or 3.9% in group C within 90 days.

Conclusions And Relevance: These findings suggest that TIA continues to be associated with a high risk of early stroke; however, the rate of post-TIA stroke might have decreased slightly during the past 2 decades.
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http://dx.doi.org/10.1001/jamaneurol.2020.3627DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7551236PMC
January 2021

CADASIL vs. Multiple Sclerosis: Is It Misdiagnosis or Concomitant? A Case Series.

Front Neurol 2020 4;11:860. Epub 2020 Sep 4.

Geisinger Neuroscience Institute, Geisinger Health System, Danville, PA, United States.

Cerebral autosomal dominant arteriopathy and subcortical infarct leukoencephalopathy (CADASIL) is the most common form of hereditary stroke caused by a mutation in the gene located on the short arm of chromosome 19. A small number of published reports describe CADASIL patients who were initially diagnosed as multiple sclerosis. Although it was previously indicated that there was no association between mutations and multiple sclerosis, the involvement of autoimmune mechanisms among patients with CADASIL has been hypothesized. Case 1 is a middle-aged woman with initial diagnoses of multiple sclerosis (MS) and myelitis that continued to progress despite treatment with disease-modifying agents. She had occasional migraines, transient blurred vision, and multiple lacunar infarcts. She continued treatment for about 15 years with no significant alleviation and progressive changes on brain MRI; genetic testing was ordered which showed mutation, and diagnosis was changed to CADASIL with subsequent revision of treatment course. However, the presence of myelitis in this patient is unusual and may raise the question of a concurrent autoimmune process. Case 2 is a woman presenting with vertigo and paresthesia and diagnosed with MS based on an initial brain MRI showing biventricular white matter hyperintensities; however, she was not started on any disease-modifying agents. Her symptoms were reevaluated by a neurologist, and genetic testing was performed for . Case 3 is a young woman with a history of migraines who initially presented with numbness and gait ataxia which later progressed to speech difficulty and memory loss. A diagnosis of MS was established which was later changed to CADASIL. Since CADASIL is a rare disease, it is imperative to raise awareness of its unique clinical condition as well as variation in its clinical presentations. It is crucial that the overlapping symptoms between MS and CADASIL be thoroughly examined to avoid misdiagnosis and treatment complications. The involvement of autoimmune mechanisms in CADASIL and the role of gene mutations in provoking an autoimmune process should be further investigated.
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http://dx.doi.org/10.3389/fneur.2020.00860DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7500095PMC
September 2020

Dyspneic and non-dyspneic (silent) hypoxemia in COVID-19: Possible neurological mechanism.

Clin Neurol Neurosurg 2020 11 9;198:106217. Epub 2020 Sep 9.

Tuberculosis and Lung Disease Research Center, Tabriz University of Medical Sciences, Tabriz, Iran. Electronic address:

SARS-CoV-2 mainly invades respiratory epithelial cells by adhesion to angiotensin-converting enzyme 2 (ACE-2) and thus, infected patients may develop mild to severe inflammatory responses and acute lung injury. Afferent impulses that result from the stimulation of pulmonary mechano-chemoreceptors, peripheral and central chemoreceptors by inflammatory cytokines are conducted to the brainstem. Integration and processing of these input signals occur within the central nervous system, especially in the limbic system and sensorimotor cortex, and importantly feedback regulation exists between O, CO and blood pH. Despite the intensity of hypoxemia in COVID-19, the intensity of dyspnea sensation is inappropriate to the degree of hypoxemia in some patients (silent hypoxemia). We hypothesize that SARS-CoV-2 may cause neuronal damage in the corticolimbic network and subsequently alter the perception of dyspnea and the control of respiration. SARS-CoV-2 neuronal infection may change the secretion of numerous endogenous neuropeptides or neurotransmitters that distribute through large areas of the nervous system to produce cellular and perceptual effects. SARS-CoV-2 mainly enter to CNS via direct (neuronal and hematologic route) and indirect route. We theorize that SARS-CoV-2 infection-induced neuronal cell damage and may change the balance of endogenous neuropeptides or neurotransmitters that distribute through large areas of the nervous system to produce cellular and perceptual effects. Thus, SARS-CoV-2-associated neuronal damage may influence the control of respiration by interacting in neuromodulation. This would open up possible lines of study for the progress in the central mechanism of COVID-19-induced hypoxia. Future research is desirable to confirm or disprove such a hypothesis.
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http://dx.doi.org/10.1016/j.clineuro.2020.106217DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7480672PMC
November 2020

Using artificial intelligence for improving stroke diagnosis in emergency departments: a practical framework.

Ther Adv Neurol Disord 2020 25;13:1756286420938962. Epub 2020 Aug 25.

Neuroscience Institute, Geisinger Health System, Stroke Program, Geisinger Northeast Region, GRA Stroke Task Force, American Heart Association, Department of Neurosciences, 100 N Academy Ave, Danville, PA 17822-2101, USA.

Stroke is the fifth leading cause of death in the United States and a major cause of severe disability worldwide. Yet, recognizing the signs of stroke in an acute setting is still challenging and leads to loss of opportunity to intervene, given the narrow therapeutic window. A decision support system using artificial intelligence (AI) and clinical data from electronic health records combined with patients' presenting symptoms can be designed to support emergency department providers in stroke diagnosis and subsequently reduce the treatment delay. In this article, we present a practical framework to develop a decision support system using AI by reflecting on the various stages, which could eventually improve patient care and outcome. We also discuss the technical, operational, and ethical challenges of the process.
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http://dx.doi.org/10.1177/1756286420938962DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7453441PMC
August 2020

Racial, Economic, and Health Inequality and COVID-19 Infection in the United States.

J Racial Ethn Health Disparities 2021 06 1;8(3):732-742. Epub 2020 Sep 1.

Geisinger Neuroscience Institute, Geisinger Health System, Danville, PA, USA.

Objectives: There is preliminary evidence of racial and social economic disparities in the population infected by and dying from COVID-19. The goal of this study is to report the associations of COVID-19 with respect to race, health, and economic inequality in the United States.

Methods: We performed an ecological study of the associations between infection and mortality rate of COVID-19 and demographic, socioeconomic, and mobility variables from 369 counties (total population, 102,178,117 [median, 73,447; IQR, 30,761-256,098]) from the seven most affected states (Michigan, New York, New Jersey, Pennsylvania, California, Louisiana, Massachusetts).

Results: The risk factors for infection and mortality are different. Our analysis shows that counties with more diverse demographics, higher population, education, income levels, and lower disability rates were at a higher risk of COVID-19 infection. However, counties with higher proportion with disability and poverty rates had a higher death rate. African Americans were more vulnerable to COVID-19 than other ethnic groups (1981 African American infected cases versus 658 Whites per million). Data on mobility changes corroborate the impact of social distancing.

Conclusion: Our study provides evidence of racial, economic, and health inequality in the population infected by and dying from COVID-19. These observations might be due to the workforce of essential services, poverty, and access to care. Counties in more urban areas are probably better equipped at providing care. The lower rate of infection, but a higher death rate in counties with higher poverty and disability could be due to lower levels of mobility, but a higher rate of comorbidities and health care access.
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http://dx.doi.org/10.1007/s40615-020-00833-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7462354PMC
June 2021