Social Determinants of Stroke Hospitalization and Mortality in United States’ Counties

(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 \u3c 0.001), blood pressure medication nonadherence (OR = 2.02, p \u3c 0.001), age-adjusted obesity (OR = 1.24, p = 0.006), presence of hospitals with neurological services (OR = 1.9, p \u3c 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 \u3c 0.01) and presence of hospitals (OR = 0.69, p \u3c 0.025) were associated with low SHR. Median household income (OR = 0.6, p \u3c 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

Predictors of Post-Stroke Depression: A Retrospective Cohort Study

Despite reports of a high incidence and various predictors of post-stroke depression (PSD), the underdiagnosis and undertreatment rates of PSD are still high. This study aimed to examine the incidence of depression in stroke patients and identify factors associated with PSD. This was a retrospective cohort study on ischemic stroke patients from the Geisinger Neuroscience Ischemic Stroke (GNSIS) registry. The following statistical analyses were performed to predict PSD in the studied population: a Kaplan–Meier estimator and a Cox proportional hazards model. A total of 5882 patients were included in the study. The median age at the time of an ischemic stroke was 72 years and 56% were males. A total of 294 patients were diagnosed with PSD within one year of a stroke. The cumulative incidence of depression was found to be 6.4% (95% CI 5.7–7.1%) at one year for the entire cohort. Women were found to have a higher risk of PSD than men (HR for women = 1.47, 95% CI 1.18–1.85, p = 0.001). A history of prior stroke (HR = 1.58, 95% CI 1.18–2.11, p = 0.002) and myocardial infarction (HR = 1.47, 95% CI 1.05–2.06, p = 0.025) were associated with PSD. Medicaid patients had a higher risk for PSD (HR = 2.16, 95% CI 1.5–3.12, p < 0.001) than those with commercial insurance or health maintenance organization plans. Our findings showed that women, patients with a history of prior stroke or myocardial infarction, and with Medicaid insurance were more likely to develop PSD. Through an observational study on the EHR data, we confirmed that chronic stress, including financial and health-related stress, irrespective of age, significantly increased the risk for PSD

PROP1 gene mutations in a 36-year-old female presenting with psychosis

Combined pituitary hormonal deficiency (CPHD) is a rare disease that results from mutations in genes coding for transcription factors that regulate the differentiation of pituitary cells. PROP1 gene mutations are one of the etiological diagnoses of congenital panhypopituitarism, however symptoms vary depending on phenotypic expression. We present a case of psychosis in a 36-year-old female with congenital panhypopituitarism who presented with paranoia, flat affect and ideas of reference without a delirious mental state, which resolved with hormone replacement and antipsychotics. Further evaluation revealed that she had a homozygous mutation of PROP1 gene. In summary, compliance with hormonal therapy for patients with hypopituitarism appears to be effective for the prevention and treatment of acute psychosis symptoms

Role of multislice computed tomography in evaluation and management of intestinal obstruction

The aims and objectives of this study were: i) to evaluate the efficacy of computed tomography (CT) imaging in diagnosing the presence, level, degree, and cause of intestinal obstruction, and the role of CT in detecting presence of complications; ii) to assess impact of CT in decision making and management (surgical/conservative); iii) to correlate CT findings with intra operative findings whenever possible. A prospective study of 40 patients presented in outpatient/emergency department with features suggestive of intestinal obstruction. Multislice contrast enhanced computed tomography of whole abdomen was done in all patients after preliminary investigations. Whenever indicated, patients were explored. Statistical analysis was performed to determine the efficacy of multidetector computed tomography (MDCT) in diagnosing intestinal obstruction and its complications. Out of 40, 30 patients underwent exploratory laparotomy and it was found that MDCT was 85% sensitive and 70% specific in diagnosing bowel obstruction. Association between MDCT findings suggestive of obstruction and intra-operative findings turn out to be significant (P=0.003). MDCT findings were consistent with intraoperative findings in 22 out of 30 patients (73%). MDCT is sensitive and specific in determining the presence of bowel obstruction and should be recommended for patients with suspected bowel obstruction because it affects outcome in these patients

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

Background and purposeObesity 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.MethodsThis 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.ResultsAmong 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.ConclusionOur 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

Risk of stroke in hospitalized SARS-CoV-2 infected patients: A multinational study

Mondello, Stefania/0000-0002-8587-3614; Alizada, Orkhan/0000-0003-0942-9906; Ghorbani, Mohammad/0000-0002-7709-3095; Abedi, Vida/0000-0001-7689-933XWOS: 000575454600010PubMed: 32818804Background: 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

Defining the Age of Young Ischemic Stroke Using Data-Driven Approaches

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