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OBJECTIVE Progressive multifocal leukoencephalopathy (PML) is a serious viral infection associated with disease-modifying therapies (DMT) for multiple sclerosis (MS) including sphingosine 1-phosphate receptor (S1PR) modulators. The objective of this review was to investigate the characteristics of PML in MS patients associated with drugs of the S1PR modulator. METHODS We conducted a literature review and analysis of 24 patients from 12 publications in PubMed, SCOPUS and EMBASE. This is a descriptive analysis and study of characteristics of PML associated fingolimod and related S1PR modulator group of DMT. RESULTS A total of 24 cases of PML in MS patients treated with fingolimod were identified. Of these, 21 cases contained data regarding changes in the expanded disability status scale (EDSS). One case of PML in association with ozanimod treatment in a clinical trial was also identified. In PML cases associated with fingolimod, the mean age at the time of PML diagnosis was 50.91 ± 11.5 years. All patients were treated with fingolimod for more than 24 months. Compared to patients who improved or were stable, in terms of EDSS, after symptomatic management of PML, the non-improved groups were significantly older. There were no fatalities in either group during the reported follow-up period. CONCLUSION The incidence of PML appears to be extremely low in MS patients treated with S1PR modulators. Risk of PML increases with increase in duration of treatment with S1PR modulators like fingolimod, and increased age at the time of PML diagnosis is associated with worse prognosis.

The requirement to serve more users and provide more data at more incredible data speeds drives demand for wireless wideband communications. The Ultra-Wide Band (UWB) technology uses nanosecond-long pulses to span an extensive frequency range. The high bandwidth, low power consumption, and less fading due to multipath are attained in UWB communication technologies. This paper explains a hexagonal monopole antenna design using a high-frequency structure simulator (HFSS) and is further optimized for c better bandwidth. A hexagonal monopole antenna is designed using FR-4 epoxy metamaterial substrate. The optimum hexagonal bandwidth is attained at a span of 6.13 to 7.34 GHz frequency, and the polar gain is 1.2 dB. This paper provides the UWB antenna design; an optimized bandwidth is achieved as expected, the applications of smart marketing and a discussion of all relevant literature are carried out.

Stroke screening tools should have good diagnostic performance for early diagnosis and a proper therapeutic plan. This paper describes and compares various diagnostic tools used to identify stroke in emergency departments and prehospital setting.The meta-analysis was conducted according to the Preferred Reporting Items for a Systematic Review and Meta-analysis of Diagnostic Test Accuracy Studies (PRISMA-DTA) guidelines. The PubMed and Scopus databases were searched until December 31, 2021, for studies published on stroke screening tools. These tools' diagnostic performance (sensitivity and specificity) was pooled using a bivariate random-effects model whenever appropriate.Eleven screening tools for stroke were identified in 29 different studies. The various tools had a wide range of sensitivity and specificity in different studies. In the meta-analysis, the Cincinnati Pre-hospital Stroke Scale, Face Arm Speech Test, and Recognition of Stroke in the Emergency Room (ROSIER) had sensitivity (between 83 and 91%) but poor specificity (all below 64%). When comparing all the tools, ROSIER had the highest sensitivity 90.5%. Los Angeles Pre-hospital Stroke Screen performed best in terms of specificity 88.7% but had low sensitivity (73.9%). Melbourne Ambulance Stroke Screen had a balanced performance in terms of sensitivity (86%) and specificity (76%). Sensitivity analysis consisting of only prospective studies showed a similar range of sensitivity and specificity.All the stroke screening tools included in the review were comparable, but no clear superior screening tool could be identified. Simple screening tools like Cincinnati prehospital stroke scale (CPSS) have similar performance compared to more complex tools.

COVID-19 pandemic, caused by severe acute respiratory syndrome coronavirus 2 has been occurring in waves due to emergence of different strains. During second wave of COVID pandemic in Nepal, largely caused by Delta variant, due to rapid rise in cases, existing hospitals and health facilities were overwhelmed. As a result, telemedicine was expanded to help reduce strain on the healthcare system and meet unusually high demands. A team of physicians with the support from the local government provided hospital level care for patients at home in rural district of Dang in Nepal. This research analyzes implementation of this model of care and outcomes based on review of patient records and treatment guidelines. Within a month, from May 5 to June 13, 2021, a total of 102 patients received care at home. While most patients had mild to moderate disease, 12.8% of patients had severe disease. Telemedicine usage was highest among the 40-59 years age group, with overall recovery rate of 85.3%. The patient satisfaction survey revealed that majority, 86.5% were happy with the medical care they received. This home hospital care model has shown excellent clinical outcomes and high patient satisfaction even in resource limited setting.

Introduction: The rise of ischemic stroke among young adults has stressed the need to understand their risk profiles and outcomes better. This study aimed to examine the five-year ischemic stroke recurrence and survival probability among young patients in rural Pennsylvania. Methods: This retrospective cohort study included first-time ischemic stroke patients from the Geisinger Health System between September 2003 and May 2014. The outcomes included all-cause mortality and ischemic stroke recurrence at five years. Kaplan-Meier estimator, cumulative incidence function, Cox proportional hazards model, and Cause-specific hazard model were used to examine the association of independent variables with the outcomes. Results: A total of 4459 first-time ischemic stroke patients were included in the study, with 664 (14.9%) patients in the 18–55 age group and 3795 (85.1%) patients in the >55 age group. In the 18–55 age group, the five-year survival probability was 87.2%, and the cumulative incidence of recurrence was 8%. Patients in the 18–55 age group had significantly lower hazard for all-cause mortality (HR = 0.37, 95% CI 0.29–0.46, p < 0.001), and non-significant hazard for five-year recurrence (HR = 0.81, 95% CI 0.58–1.12, p = 0.193) compared to the >55 age group. Chronic kidney disease was found to be associated with increased mortality in the 18–55 age group. Conclusion: In our rural population, younger ischemic stroke patients were at the same risk of long-term ischemic stroke recurrence as the older ischemic stroke patients. Identifying the factors and optimizing adequate long-term secondary prevention may reduce the risk of poor outcomes among younger ischemic stroke patients.

With the expansion of electronic health records(EHR)-linked genomic data comes the development of machine learning-enable models. There is a pressing need to develop robust pipelines to evaluate performance of integrated models and minimize systemic bias. We developed a prediction model of symptomatic Clostridioides difficile infection(CDI) by integrating common EHR-based risk factors and genetic risk factors(rs2227306/IL8). Our pipeline includes 1)leveraging phenotyping algorithm to minimize temporal bias, 2)performing simulation studies to determine the predictive power in samples without genetic information, 3)propensity score matching to control for the confoundings, 4)selecting machine learning algorithms to capture complex feature interactions, 5)performing oversampling to address data imbalance, and 6) optimizing models and ensuring proper bias-variance trade-off. We evaluate the performace of including common clinical risk factors in prediction of CDI and the benefit of including genetic feature(s) into the prediction models. We emphasize the importance of building a robust integrated pipeline to avoid systemic bias and the value of genetic feature should be thoroughly evaluated in general population and subgroups.

Background: Stroke misdiagnosis is estimated to occur in 9% of all stroke patients and is associated with poor outcomes. We hypothesized that machine learning (ML) could be used to detect ischemic stroke at the point of care in emergency departments (ED). Methods: Clinical data from 13 hospitals of a large health system in Pennsylvania, United States, from September 2003 to May 2019 were used for model development. Data from June 2019 to December 2020 were prospectively collected and divided into pre- and post-COVID cohorts for validation. We simulated three clinical settings by enrollment with different inclusion/exclusion criteria and created two ML pipelines: ML applied to pre-event clinical data, and natural language processing (NLP) with ML applied to clinical notes during triaging. Misdiagnosed stroke in ED was a case study to show the discriminative power. Results: We included a total of 49,155 patient encounters (8,900 consecutive ischemic stroke patients and 40,255 controls). The best model, based on the patient’s pre-event information, was XGBoost (AUROC=0.91). Using NLP+ML pipeline, we collected 2,070 notes during the triage process and 9,607 notes (non-stroke) for modeling. For independent validation, we identified 2,989 notes for stroke and 4,303 notes for non-stroke. We identified nine models that reached a balanced performance in terms of AUROC, sensitivity, and specificity. Model performance, in terms of AUROC, ranged from 0.876 to 0.985. Model sensitivity and specificity reached 0.993 and 0.975, respectively. There was no performance difference between models tested on pre- and post-COVID. Three out of four models correctly identified a separate case series of five misdiagnosed strokes. Conclusion: Available clinical information can be used to reduce stroke misdiagnosis in real-time. In addition, this study serves as a foundation for prospective trials to establish how pre-event clinical data and triage information can be used to improve care.

Long-term mortality in ischemic stroke patients with concomitant COPD has been largely unexplored. This study aimed to compare long-term all-cause mortality in ischemic stroke patients with and without COPD.This was a retrospective cohort study of ischemic stroke patients with and without COPD in the Geisinger Neuroscience Ischemic Stroke database to examine all-cause mortality up to 3 years using Kaplan-Meier estimator and Cox proportional hazards model.Of the 6,589 ischemic stroke patients included in this study, 5,525 (83.9%) did not have COPD (group A). Group B (n=1,006) consisted of patients with COPD diagnosis by ICD-9/10-CM codes. COPD patients in Group C (n=233) were diagnosed by spirometry, and in Group D (n=175) by both ICD-9/10-CM codes and spirometry confirmation. The survival probabilities at three years in Group B, C, and D were significantly lower than in Group A. Group B (HR=1.262, 95% CI 1.122-1.42, p0.001) and group C (HR=1.251, 95% CI 1.01-1.55, p=0.041) had significantly lower hazard of mortality compared to group A. There was no significant difference in survival between COPD subtypes of chronic bronchitis and emphysema. Patients in Global Initiative for Chronic Obstructive Lung Disease (GOLD) 2 stage had an increased mortality hazard compared to the GOLD 1 stage.While ischemic stroke patients with preexisting COPD have worse long-term survival than those without COPD, the results largely depended on the definition of COPD used. These results suggest that ischemic stroke patients with COPD need more personalized medical care to decrease long-term mortality.

Introduction: The cut-point for defining the age of young ischemic stroke (IS) is clinically and epidemiologically important, yet it is arbitrary and differs across studies. In this study, we leveraged electronic health records (EHRs) and data science techniques to estimate an optimal cut-point for defining the age of young IS. Methods: Patient-level EHRs were extracted from 13 hospitals in Pennsylvania, and used in two parallel approaches. The first approach included ICD9/10, from IS patients to group comorbidities, and computed similarity scores between every patient pair. We determined the optimal age of young IS by analyzing the trend of patient similarity with respect to their clinical profile for different ages of index IS. The second approach used the IS cohort and control (without IS), and built three sets of machine-learning models—generalized linear regression (GLM), random forest (RF), and XGBoost (XGB)—to classify patients for seventeen age groups. After extracting feature importance from the models, we determined the optimal age of young IS by analyzing the pattern of comorbidity with respect to the age of index IS. Both approaches were completed separately for male and female patients. Results: The stroke cohort contained 7555 ISs, and the control included 31,067 patients. In the first approach, the optimal age of young stroke was 53.7 and 51.0 years in female and male patients, respectively. In the second approach, we created 102 models, based on three algorithms, 17 age brackets, and two sexes. The optimal age was 53 (GLM), 52 (RF), and 54 (XGB) for female, and 52 (GLM and RF) and 53 (RF) for male patients. Different age and sex groups exhibited different comorbidity patterns. Discussion: Using a data-driven approach, we determined the age of young stroke to be 54 years for women and 52 years for men in our mainly rural population, in central Pennsylvania. Future validation studies should include more diverse populations.

Sphingosine-1-phosphate (S1P) receptor modulators are a new class of oral disease-modifying therapies used for Multiple Sclerosis (MS). These are immunomodulatory drugs and can thus increase the risk of certain infections in these patients. This paper summarizes the existing data on the most common opportunistic infections associated with the drugs in this class: Varicella Zoster Virus (VZV), Herpes Simplex Virus (HSV), and Cryptococcus neoformans. A literature review and descriptive analysis of reported cases and clinical phase III study findings on the incidences of these infections were conducted using PubMed and Google Scholar. Results regarding fingolimod, siponimod, ozanimod, and ponesimod were obtained. Overall, the incidence of these infections was found to be extremely low in MS patients treated with S1P receptor modulators. Among the four drugs in this class, the incidence rates of VZV, HSV, and cryptococcal infections were either similar or slightly higher than placebo, with some infections not reported in cases of ozanimod and ponesimod. Most of these resulted in favorable outcomes, with very few disabilities or fatalities. However, this paper highlights the increasing relevance of assessing infectious risk factors to promote the early identification of serious complications related to these drugs. Opportunistic infections should be considered in the differential diagnosis of an MS relapse in the setting of disease-modifying treatment.

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&rsquo, s EHR on patients in three distinct cohorts&mdash, patients tested for Clostridioides difficile (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 C. difficile 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 &minus, 35.5 for the Cdiff, &minus, 8.3 for the IBD, and &minus, 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.

Various studies on oral anticoagulants (OAC) use among atrial fibrillation (AF) patients have shown high rates of undertreatment and the presence of sex disparity. This study used the ‘Geisinger Neuroscience Ischemic Stroke’ (GNSIS) database to examine sex differences in OAC treatment among ischemic stroke patients with the pre-event diagnosis of AF in rural Pennsylvania between 2004 and 2019. We examined sex disparities in OAC undertreatment and associated risks based on age group and ischemic stroke year. A total of 1062 patients were included in the study and 1015 patients (96%) had CHA2DS2-VASc score ≥ 2, of which 549 (54.1%) were women. Undertreatment rates were not statistically significant between men and women in the overall cohort (50.0% vs. 54.5%, p = 0.18), and male sex was not found to be a significant factor in undertreatment (OR 0.82, 95% CI 0.62–1.09, p = 0.17). The result persisted even when patients were divided into four age groups and two groups based on the study time period. The undertreatment rates in both sex groups remained consistent following the introduction of novel oral anticoagulants. In conclusion, there was no evidence of sex disparity with respect to OAC treatment, even after stratifying the cohort by age and ischemic stroke year.

Laboratory data from Electronic Health Records (EHR) are often used in prediction models where estimation bias and model performance from missingness can be mitigated using imputation methods. We demonstrate the utility of imputation in two real-world EHR-derived cohorts of ischemic stroke from Geisinger and of heart failure from Sutter Health to: (1) characterize the patterns of missingness in laboratory variables; (2) simulate two missing mechanisms, arbitrary and monotone; (3) compare cross-sectional and multi-level multivariate missing imputation algorithms applied to laboratory data; (4) assess whether incorporation of latent information, derived from comorbidity data, can improve the performance of the algorithms. The latter was based on a case study of hemoglobin A1c under a univariate missing imputation framework. Overall, the pattern of missingness in EHR laboratory variables was not at random and was highly associated with patients’ comorbidity data; and the multi-level imputation algorithm showed smaller imputation error than the cross-sectional method.

(1) Background: Stroke incidence and outcomes are influenced by socioeconomic status. There is a paucity of reported population-level studies regarding these determinants. The goal of this ecological analysis was to determine the county-level associations of social determinants of stroke hospitalization and death rates in the United States. (2) Methods: Publicly available data as of 9 April 2021, for the socioeconomic factors and outcomes, was extracted from the Centers for Disease Control and Prevention. The outcomes of interest were “all stroke hospitalization rates per 1000 Medicare beneficiaries” (SHR) and “all stroke death rates per 100,000 population” (SDR). We used a multivariate binomial generalized linear mixed model after converting the outcomes to binary based on their median values. (3) Results: A total of 3226 counties/county-equivalents of the states and territories in the US were analyzed. Heart disease prevalence (odds ratio, OR = 2.03, p < 0.001), blood pressure medication nonadherence (OR = 2.02, p < 0.001), age-adjusted obesity (OR = 1.24, p = 0.006), presence of hospitals with neurological services (OR = 1.9, p < 0.001), and female head of household (OR = 1.32, p = 0.021) were associated with high SHR while cost of care per capita for Medicare patients with heart disease (OR = 0.5, p < 0.01) and presence of hospitals (OR = 0.69, p < 0.025) were associated with low SHR. Median household income (OR = 0.6, p < 0.001) and park access (OR = 0.84, p = 0.016) were associated with low SDR while no college degree (OR = 1.21, p = 0.049) was associated with high SDR. (4) Conclusions: Several socioeconomic factors (e.g., education, income, female head of household) were found to be associated with stroke outcomes. Additional research is needed to investigate intermediate and potentially modifiable factors that can serve as targeted interventions.

BACKGROUND AND PURPOSE: Current stroke care recommendations for patient selection for mechanical thrombectomy in the extended time window demand advanced imaging to determine the stroke core volume and hypoperfusion mismatch, which may not be available at every center. We aimed to determine outcomes in patients selected for mechanical thrombectomy solely on the basis of noncontrast CT and CTA in the early (

Introduction: Stroke hospitalization and mortality are influenced by various social determinants. This ecological study aimed to determine the associations between social determinants and stroke hospitalization and outcome at county-level in the United States. Methods: County-level data were recorded from the Centers for Disease Control and Prevention as of January 7, 2020. We considered four outcomes: all-age (1) Ischemic and (2) Hemorrhagic stroke Death rates per 100,000 individuals (ID and HD respectively), and (3) Ischemic and (4) Hemorrhagic stroke Hospitalization rate per 1,000 Medicare beneficiaries (IH and HH respectively). Results: Data of 3,225 counties showed IH (12.5 ± 3.4) and ID (22.2 ± 5.1) were more frequent than HH (2.0 ± 0.4) and HD (9.8 ± 2.1). Income inequality as expressed by Gini Index was found to be 44.6% ± 3.6% and unemployment rate was 4.3% ± 1.5%. Only 29.8% of the counties had at least one hospital with neurological services. The uninsured rate was 11.0% ± 4.7% and people living within half a mile of a park was only 18.7% ± 17.6%. Age-adjusted obesity rate was 32.0% ± 4.5%. In regression models, age-adjusted obesity (OR for IH: 1.11; HH: 1.04) and number of hospitals with neurological services (IH: 1.40; HH: 1.50) showed an association with IH and HH. Age-adjusted obesity (ID: 1.16; HD: 1.11), unemployment (ID: 1.21; HD: 1.18) and income inequality (ID: 1.09; HD: 1.11) showed an association with ID and HD. Park access showed inverse associations with all four outcomes. Additionally, population per primary-care physician was associated with HH while number of pharmacy and uninsured rate were associated with ID. All associations and OR had p ≤0.04. Conclusion: Unemployment and income inequality are significantly associated with increased stroke mortality rates.

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.

Objective: To determine whether Polygenic Risk Scores (PRS) derived from MEGASTROKE is associated with ischemic stroke (IS) and its subtypes in an independent tertiary healthcare system and to identify PRS derived from gene-sets of known biological pathways associated with IS and its outcomes (mortality and recurrent stroke). Methods: Controls(n=19806/7484, age≥69/79) and cases(n=1184/951 for discovery/replication) of acute IS with European ancestry and clinical risk factors identified by leveraging the Geisinger Electronic Health Record (EHR) and chart review confirmation. All Geisinger MyCode patients with age ≥69/79, without any stroke-related diagnostic codes as low-risk control. Genetic heritability and genetic correlation between Geisinger and MEGASTROKE(EUR) using the summary statistics of GWAS by LDSC. All PRS for any stroke — AS, any ischemic stroke — AIS, large artery stroke — LAS, cardioembolic stroke — CES, and small vessel stroke — SVS constructed by PRSice-2. PRS (top or bottom quantiles) derived from 7350 gene-sets of GO Biological Process was used to stratify the IS. Results: A moderate heritability (10-20%) for Geisinger sample as well as the genetic correlation between MEGASTROKE and the Geisinger cohort was identified. Gene sets analyses highlighted the association of PRS with Gene Ontology terms (vascular endothelial growth factor, amyloid precursor protein, and atherosclerosis). The PRS LAS , PRS CES , and PRS SVS explained the most variance of the corresponding subtypes of Geisinger IS suggesting shared etiologies and corroborated Geisinger TOAST subtyping. We further evaluate PRS derived from the gene-sets of which GO terms may best differentiate the survival in IS cases with the corresponding high or low genetic burden. The result from the Cox regression analysis showed simultaneously the effect of several risk factors including PRS on mortality and recurrent stroke probabilities. Conclusions: We provide the first evidence that PRSs derived from MEGASTROKE have value in identifying shared etiologies and determining stroke subtypes.

Importance: Management of transient ischemic attack (TIA)and the concept of urgent TIA clinic have gained significant attention over the past years. Objective: To calculate the odds of subsequent ischemic stroke within 2, 7, 30, and 90 days and death within 90 days among the TIA patients who received acute care in a TIA clinic versus those admitted to a hospital. Data Sources: We performed a systematic review and meta-analysis of all the published TIA outcome studies. Included studies were obtained by searching Medline, Web of Science, Scopus, All EBM reviews series, International Clinical Trial Registry Platform (ICTRP), Trip database, and CINAHL, last updated in March 2020, irrespective of the study design, document type, or language. Study Selection: We accepted both the classic time-based definition of TIA and the new tissue-based definition and studies with a combined record of TIA and minor ischemic stroke patients. We excluded studies with a diagnosis of index TIA event after ischemic stroke occurrence, low suspicious for TIA, TIA acute care in settings other than an outpatient clinic or hospital, multicentric studies, or duplicate reports of the same database. Data Extraction and Synthesis: The study was conducted and reported according to PRISMA, MOOSE, and EQUATOR guidelines. Critical appraisal and methodological quality were conducted by ROBINS-E tool. Data were pooled under random-effects models. Results: Systematic review yielded to 19,547 reports, of which titles and abstracts of 9,113 reports were screened. The overall subsequent ischemic stroke odds ratios were estimated by meta-analyses as follows: 4 studies within 2 days with 1,829 patients (OR: 0.93, 95% CI: -0.52-2.38), 5 studies within 7 days with 1,801 patients (OR: 0.68, 95% CI: -0.73-2.08), 4 studies within 30 days with 1,768 patients (OR:1.01, 95% CI: -0.72 - 2.73), and 6 studies within 90 days with 2,319 population (OR: 0.94, 95% CI: 0.22-1.66). The 90-day risk of death by analyzing 3 studies, 1162 patients, was calculated as OR:1.29, 95% CI: 0.76-3.34. Conclusion and Relevance: Hospital management of TIA patients in the acute phase may not be associated with a lower risk of stroke and death compared with urgent outpatient management.

Introduction: 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 data extracted from different resources including electronic health records. Methods: We analyzed consecutive ischemic stroke patients captured in the Geisinger NeuroScience Ischemic Stroke (GNSIS) database. Survival in first-time ischemic stroke patients was analyzed using Kaplan-Meier estimator, stratified by different BMI categories. The predictors of mortality at one-year were assessed using a multivariate Cox proportional hazards model. Results: Among 6,703 first-time adult ischemic stroke patients, mean age was 70.2 ±13.5 years and 52% were men. Of these patients, 24% patients were non-overweight (BMI < 25), 34% were overweight (BMI 25-29.9) and 41% were obese (BMI ≥ 30). One-year survival probability was significantly higher in overweight patients (87%, 95% CI: [85.6 - 88.4], p Conclusion: Our 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.

Objective: The long-term risk of recurrent ischemic stroke, estimated to be between 17%-30%, cannot be reliably assessed. 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, 6 algorithms (Logistic Regression, Extreme Gradient Boosting, Gradient Boosting Machine, Random Forest, Support Vector Machine, Decision Tree), 4 feature selection strategies, 5 prediction windows, and 2 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 2,091 ischemic stroke patients for this study. Model AUROC 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 the selected six modeling 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 to improve outcomes.

Introduction: Several studies suggest women may be disproportionately affected by poorer stroke outcomes than men. This study aimed to investigate if women had a higher risk of all-cause mortality and recurrence after an ischemic stroke in a rural population in central Pennsylvania, United States. Methods: We analyzed consecutive ischemic stroke patients captured in our comprehensive 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 8,900 adult ischemic stroke patients (median age of 71.6 [IQR: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 to a 65.7% survival in men (p=0.003) at five years; however, the survival difference was not present after adjustment by the associated factors of 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.

The goal of this study was to better depict the short-term risk of stroke and its associated factors among SARS-CoV-2 hospitalized patients. Data Source: This multicenter, multinational observational study includes hospitalized SARS-CoV-2 patients from North and South America (United States, Canada, and Brazil), Europe (Greece, Italy, Finland, and Turkey), Asia (Lebanon, Iran, and India), and Oceania (New Zealand). Main Outcomes and Measures: The outcome was the risk of subsequent stroke (ischemic stroke, intracranial hemorrhage, cerebral venous/sinus thrombosis). The counts and clinical characteristics including laboratory findings and imaging of the patients with and without a subsequent stroke were recorded according to a predefined study protocol. Data Extraction and Synthesis: Quality, risk of bias, and heterogeneity assessments were conducted according to ROBINS-E and Cochrane Q-test. The risk of subsequent stroke was estimated through meta-analyses with random effect models. Binary logistic regression was used to determine the associated factors with the outcome measure. The study was reported according to the STROBE, MOOSE, and EQUATOR guidelines. Results: We received data from 18,311 hospitalized SARS-CoV-2 patients from 77 tertiary centers in 46 regions of 11 countries until May 1 st , 2020. A total of 17,799 patients were included in meta-analyses. Among them, 156(0.9%) patients had a stroke—123(79%) ischemic stroke, 27(17%) intracerebral/subarachnoid hemorrhage, and 6(4%) cerebral sinus thrombosis. Subsequent stroke risks calculated with meta-analyses, under low to moderate heterogeneity, were 0.5% among all centers in all countries, and 0.7% among countries with higher health expenditures. The need for mechanical ventilation (OR: 1.9, 95% CI:1.1-3.5, p = 0.03) and the presence of ischemic heart disease (OR: 2.5, 95% CI:1.4-4.7, p =0·006) were predictive of stroke. Conclusion and Relevance: The results of this multi-national study on hospitalized patients with SARS-CoV-2 infection indicated an overall stroke risk of 0.5% (pooled risk: 0.9%). The need for mechanical ventilation and the history of ischemic heart disease are the independent predictors of stroke among SARS-CoV-2 patients.

Introduction: The stroke mortality rate has gradually declined due to improved interventions and controlled risk factors. We investigated the trends in stroke risk factors and outcomes among a rural population in the United States between 2004 and 2018. Methods: We built a comprehensive stroke database called “Geisinger NeuroScience Ischemic Stroke (GNSIS)” for this study. Clinical data were extracted from multiple sources, including electronic health records and quality data. Results: Our cohort comprised of 8,561 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 rate of hypertension, diabetes, dyslipidemia, and history of stroke increased significantly over the fifteen years window. The one-year recurrence and mortality rates were 6.3% and 15.8%, respectively. Although the one-year stroke recurrence increased from 2004 to 2018 (Cochran-Armitage test Z = -3.66, p65 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 Fine-Gray competing risk model, diabetes mellitus and age 65 years, atrial fibrillation or flutter, CKD, heart failure, prior hemorrhagic and ischemic stroke, history of neoplasm, myocardial infarction, and rheumatic diseases were predictors of one-year mortality. Conclusion: Although stroke mortality has decreased, stroke recurrence and several vascular risk factors have significantly increased in our rural population between 2004-2018. Older age, atrial fibrillation or flutter, heart failure, and prior ischemic stroke were independently associated with one-year all-cause mortality while diabetes mellitus and age less than 65 years were predictors of ischemic stroke recurrence.

Objective and Design: We conducted a multinational observational study on features of consecutive acute ischemic stroke (AIS), intracranial hemorrhage (ICH), and cerebral venous or sinus thrombosis (CVST) among SARS-CoV-2 infected patients. Main Outcome Measures: We investigated the association of demographics, clinical data, geographical regions, and countries’ health expenditure among AIS patients with the risk of large vessel occlusion (LVO), stroke severity as measured by National Institute of Health stroke scale (NIHSS), and stroke subtype as measured by the TOAST criteria. Additionally, we applied unsupervised machine learning algorithms to uncover possible similarities among stroke patients. Results: Among the 136 tertiary centers of 32 countries who participated in this study, 71 centers from 17 countries had at least one eligible stroke patient. Out of 432 patients included, 323(74.8%) had AIS, 91(21.1%) ICH, and 18(4.2%) CVST. Among 23 patients with subarachnoid hemorrhage, 16(69.5%) had no evidence of aneurysm. A total of 183(42.4%) patients were women, 104(24.1%) patients were younger than 55 years, and 105(24.4%) patients had no identifiable vascular risk factors. Among 380 patients who had known interval onset of the SARS-CoV-2 and stroke, 144(37.8%) presented to the hospital with chief complaints of stroke-related symptoms, with asymptomatic or undiagnosed SARS-CoV-2 infection. Among AIS patients 44.5% had LVO; 10% had small artery occlusion according to the TOAST criteria. We observed a lower median NIHSS (8[3-17], versus 11[5-17]; p=0.02) and higher rate of mechanical thrombectomy (12.4% versus 2%; p Conclusions and Relevance: We observed a relatively high number of young, and asymptomatic SARS-CoV-2 infections among stroke patients. Traditional vascular risk factors were absent among a relatively large cohort of patients. The stroke severity was lower and rate of mechanical thrombectomy was higher among countries with middle to high-health expenditure.

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. Defense Threat Reduction Agency (DTRA) grant [HDTRA1-18-1-0008]; National Institute of Health (NIH)United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA [R56HL116832]; Bucknell University Initiative Program; Roche -Genentech Biotechnology Company; Geisinger Health Plan Quality fund This study had no specific funding. VA had financial research support from the Defense Threat Reduction Agency (DTRA) grant No. HDTRA1-18-1-0008 sub-awarded to Geisinger and funds from the National Institute of Health (NIH) grant No. R56HL116832 sub-awarded to Geisinger during the study period. RZ had financial research support from Bucknell University Initiative Program, Roche -Genentech Biotechnology Company, the Geisinger Health Plan Quality fund during the study period, and receives institutional support from Geisinger Health System. The funders had no role in study design, data collection and analysis, or preparation of the manuscript.

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.

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 CHA2DS2-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 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.

Background and Purpose: Stroke is reported as a consequence of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection in several reports. However, data are sparse regarding the details of these patients in a multinational and large scale. Methods: We conducted a multinational observational study on features of consecutive acute ischemic stroke, intracranial hemorrhage, and cerebral venous or sinus thrombosis among SARS-CoV-2–infected patients. We further investigated the risk of large vessel occlusion, stroke severity as measured by the National Institutes of Health Stroke Scale, and stroke subtype as measured by the TOAST (Trial of ORG 10172 in Acute Stroke Treatment) criteria among patients with acute ischemic stroke. In addition, we explored the neuroimaging findings, features of patients who were asymptomatic for SARS-CoV-2 infection at stroke onset, and the impact of geographic regions and countries’ health expenditure on outcomes. Results: Among the 136 tertiary centers of 32 countries who participated in this study, 71 centers from 17 countries had at least 1 eligible stroke patient. Of 432 patients included, 323 (74.8%) had acute ischemic stroke, 91 (21.1%) intracranial hemorrhage, and 18 (4.2%) cerebral venous or sinus thrombosis. A total of 183 (42.4%) patients were women, 104 (24.1%) patients were P =0.02) and higher rate of mechanical thrombectomy (12.4% versus 2%; P Conclusions: We observed a considerably higher rate of large vessel occlusions, a much lower rate of small vessel occlusion and lacunar infarction, and a considerable number of young stroke when compared with the population studies before the pandemic. The rate of mechanical thrombectomy was significantly lower in countries with lower health expenditures.

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 ( p = 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.

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.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.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.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.

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 τ2statistic 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.

Mondello, Stefania/0000-0002-8587-3614; Alizada, Orkhan/0000-0003-0942-9906; Ghorbani, Mohammad/0000-0002-7709-3095; Abedi, Vida/0000-0001-7689-933X WOS:000575454600010 PubMed ID: 32818804 Background: There is an increased attention to stroke following SARS-CoV-2. The goal of this study was to better depict the short-term risk of stroke and its associated factors among SARS-CoV-2 hospitalized patients. Methods: This multicentre, multinational observational study includes hospitalized SARS-CoV-2 patients from North and South America (United States, Canada, and Brazil), Europe (Greece, Italy, Finland, and Turkey), Asia (Lebanon, Iran, and India), and Oceania (New Zealand). The outcome was the risk of subsequent stroke. Centres were included by non-probability sampling. The counts and clinical characteristics including laboratory findings and imaging of the patients with and without a subsequent stroke were recorded according to a predefined protocol. Quality, risk of bias, and heterogeneity assessments were conducted according to ROBINS-E and Cochrane Q-test. The risk of subsequent stroke was estimated through meta-analyses with random effect models. Bivariate logistic regression was used to determine the parameters with predictive outcome value. The study was reported according to the STROBE, MOOSE, and EQUATOR guidelines. Findings: We received data from 26,175 hospitalized SARS-CoV-2 patients from 99 tertiary centres in 65 regions of 11 countries until May 1st, 2020. A total of 17,799 patients were included in meta-analyses. Among them, 156(0.9%) patients had a stroke-123(79%) ischaemic stroke, 27(17%) intracerebral/subarachnoid hemorrhage, and 6(4%) cerebral sinus thrombosis. Subsequent stroke risks calculated with meta-analyses, under low to moderate heterogeneity, were 0.5% among all centres in all countries, and 0.7% among countries with higher health expenditures. The need for mechanical ventilation (OR: 1.9, 95% CI:1.1-3.5, p = 0.03) and the presence of ischaemic heart disease (OR: 2.5, 95% CI:1.4-4.7, p = 0.006) were predictive of stroke. Interpretation: The results of this multi-national study on hospitalized patients with SARS-CoV-2 infection indicated an overall stroke risk of 0.5%(pooled risk: 0.9%). The need for mechanical ventilation and the history of ischaemic heart disease are the independent predictors of stroke among SARS-CoV-2 patients. (C) 2020 The Authors. Published by Elsevier B.V.

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. Electronic supplementary material The online version of this article (10.1007/s40615-020-00833-4) contains supplementary material, which is available to authorized users.

BackgroundThere have been outbreaks of SARS-CoV-2 in long term care facilities and recent reports of disproportionate death rates among the vulnerable population. The goal of this study was to better understand the impact of SARS-CoV-2 infection on the non-institutionalized disabled population in the United States using data from the most affected states as of April 9th, 2020.MethodsThis was an ecological study of county-level factors associated with the infection and mortality rate of SARS-CoV-2 in the non-institutionalized disabled population. We analyzed data from 369 counties from the most affected states (Michigan, New York, New Jersey, Pennsylvania, California, Louisiana, Massachusetts) in the United States using data available by April 9th, 2020. The variables include changes in mobility reported by Google, race/ethnicity, median income, education level, health insurance, and disability information from the United States Census Bureau. Bivariate regression analysis adjusted for state and median income was used to analyze the association between death rate and infection rate.ResultsThe independent sample t-test of two groups (group 1: Death rate≥3.4% [median] and group 2: Death rate < 3.4%) indicates that counties with a higher total population, a lower percentage of Black males and females, higher median income, higher education, and lower percentage of disabled population have a lower rate (< 3.4%) of SARS-CoV-2 related mortality (all p-valuesp-value:3.7E-02), and higher population with independent living difficulty (est: 0.15, 95% CI: −0.01-0.30; p-value: 6.0E-02) have a higher rate of SARS-CoV-2 related mortality. Also, the regression analysis indicates that counties with higher White disabled population (est: - 0.22, 95% CI: −0.43-(-0.02); p-value: 3.3E-02), higher population with hearing disability (est: −0.26, 95% CI: - 0.42- (-0.11); p-value:1.2E-03), and higher population with disability in the 18-34 years age group (est: −0.25, 95% CI: −0.41-(-0.09); p-value:2.4E-03) show a lower rate of SARS-CoV-2 infection.ConclusionOur results indicate that while counties with a higher percentage of non-institutionalized disabled population, especially White disabled population, show a lower infection rate, they have a higher rate of SARS-CoV-2 related mortality.

Supplemental material, sj-pdf-1-tan-10.1177_1756286420971895 for Sex disparity in long-term stroke recurrence and mortality in a rural population in the United States by Clare Lambert, Durgesh Chaudhary, Oluwaseyi Olulana, Shima Shahjouei, Venkatesh Avula, Jiang Li, Vida Abedi and Ramin Zand in Therapeutic Advances in Neurological Disorders