Objective:The SARS-Cov2 virus is protean in its manifestations, affecting nearly every organ system. However, nervous system involvement and its impact on disease outcome are poorly characterized. The objective of the study is to determine if neurological syndromes are associated with increased risk of inpatient mortality.Methods:581 hospitalized patients with confirmed SARS-Cov2 infection, neurological involvement and brain-imaging were compared to hospitalized non-neurological COVID-19 patients. Four patterns of neurological manifestations were identified –acute stroke, new or recrudescent seizures, altered mentation with normal imaging, and neuro-COVID-19 complex. Factors present on admission were analyzed as potential predictors of in-hospital mortality, including sociodemographic variables, pre-existing comorbidities, vital-signs, laboratory values, and pattern of neurological manifestations. Significant predictors were incorporated into a disease-severity score. Patients with neurological manifestations were matched with patients of the same age and disease severity to assess the risk of death.Results:4711 patients with confirmed SARS-Cov2 infection were admitted to one medical system in New York City during a 6-week period. Of these, 581 (12%) had neurological issues of sufficient concern to warrant neuro-imaging. These patients were compared to 1743 non-neurological COVID-19 patients matched for age and disease-severity admitted during the same period. Patients with altered mentation (n=258, p =0.04, OR 1.39, CI 1.04 – 1.86) or radiologically confirmed stroke (n=55, p = 0.001, OR 3.1, CI 1.65-5.92) had a higher risk of mortality than age and severity-matched controls.Conclusions:The incidence of altered mentation or stroke on admission predicts a modest but significantly higher risk of in-hospital mortality independent of disease severity. While other biomarker factors also predict mortality, measures to identify and treat such patients may be important in reducing overall mortality of COVID-19.

Background and Purpose: We sought to determine if biomarkers of inflammation and coagulation can help define coronavirus disease 2019 (COVID-19)–associated ischemic stroke as a novel acute ischemic stroke (AIS) subtype. Methods: We performed a machine learning cluster analysis of common biomarkers in patients admitted with severe acute respiratory syndrome coronavirus 2 to determine if any were associated with AIS. Findings were validated using aggregate data from 3 large healthcare systems. Results: Clustering grouped 2908 unique patient encounters into 4 unique biomarker phenotypes based on levels of c-reactive protein, D-dimer, lactate dehydrogenase, white blood cell count, and partial thromboplastin time. The most severe cluster phenotype had the highest prevalence of AIS (3.6%, P <0.001), in-hospital AIS (53%, P <0.002), severe AIS (31%, P =0.004), and cryptogenic AIS (73%, P <0.001). D-dimer was the only biomarker independently associated with prevalent AIS with quartile 4 having an 8-fold higher risk of AIS compared to quartile 1 ( P =0.005), a finding that was further corroborated in a separate cohort of 157 patients hospitalized with COVID-19 and AIS. Conclusions: COVID-19–associated ischemic stroke may be related to COVID-19 illness severity and associated coagulopathy as defined by increasing D-dimer burden.

Acute Ischemic Stroke (AIS) in the young is increasing in prevalence and the largest subtype within this cohort is cryptogenic. To curb this trend, new ways of defining cryptogenic stroke and associated risk factors are needed. We aimed to gain insights into the presence or absence of cardiovascular risk factors in cases of cryptogenic stroke. We conducted a retrospective cohort study of patients aged 18–49 who presented to an urban tertiary care center with AIS. We manually collected predefined demographic, clinical, laboratory and radiological variables. Clinical risk phenotypes were determined using these variables through multivariate analysis of patients with the small and large vessel disease subtypes (vascular phenotype) and cardioembolic subtype (cardiac phenotype). The resultant phenotype models were applied to cases deemed cryptogenic. Within the 449 patients who met criteria, patients with small and large vessel disease (vascular phenotype) had higher rates of hypertension, intracranial atherosclerosis, and diabetes mellitus, and higher admission glucose, HbA1c, admission blood pressure, and cholesterol compared to the patients with cardioembolic AIS. The cardioembolic subgroup (cardiac phenotype) had significantly higher rates of congestive heart failure (CHF), rheumatic heart disease, atrial fibrillation, clotting disorders, left ventricular hypertrophy, larger left atrial sizes, lower ejection fractions, and higher B-type natriuretic peptide and troponin levels. Adjusted multivariate analysis produced six variables independently associated with the vascular phenotype (age, male sex, hemoglobin A1c, ejection fraction (EF), low-density lipoprotein (LDL) cholesterol, and family history of AIS) and five independently associated with the cardiac phenotype (age, female sex, decreased EF, CHF, and absence of intracranial atherosclerosis). Applying these models to cryptogenic stroke cases yielded that 51.5% fit the vascular phenotype and 3.1% fit the cardiac phenotype. In our cohort, half of young patients with cryptogenic stroke fit the risk factor phenotype of small and large vessel strokes.

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Introduction: Dietary macronutrient balance and reduced sodium intake are key modifiable risk factors for prevention of cardiovascular disease. Pre-heart failure (HF) (evidence of structural heart disease or abnormal cardiac function) is an independent risk factor for incident clinical HF development. We assessed associations of macronutrient and sodium intake with cardiac structure and function from the Echocardiographic Study of Latinos (Echo-SOL), an ancillary study of the Hispanic Community Health Study/Study of Latinos (HCHS/SOL). Hypothesis: We hypothesized that higher intake of protein and lower intake of fat, carbohydrate and sodium would be associated with healthier cardiac structure and function. Methods: Cross-sectional data from HCHS/SOL interviews were analyzed among 1818 adults (57% female, mean age 56 ± 0.17) with complete echo assessments. Intake of carbohydrates, proteins, fats, and sodium in relation to total caloric intake was derived from two 24-hour recalls. Associations between nutrients and pre-HF outcomes were estimated via simple linear regression as well as ANOVA across quintiles of each nutrient. All analyses were weighted and account for complex survey design. Results: Mean ± SE macronutrient intake for the overall target population was 52.3 ± 0.1% carbohydrates, 17.1 ± 0.05% protein, and 29.7 ± 0.1% fat. Mean dietary sodium intake was 3,107.2 ± 47.7mg. Higher percent of total daily calories from carbohydrates was associated with lower LV mass (-13.7g per 5%, p<0.01) and lower e’ velocity (-0.37cm/s per 5%, p<0.01). Higher fat consumption was associated with higher LV mass (11.23g per 5%, p<0.01). No significant associations were seen with protein intake. Higher sodium intake was associated with higher LV mass (1.35g per 100mg, p<0.01) and lower ejection fraction (-0.091% per 100mg, p<0.01). Quintile analysis confirmed the presence and monotonicity of the observed associations. Conclusions: Our results suggest that a macronutrient balance favoring higher carbohydrate and lower fat consumption along with reduced sodium intake is associated with better cardiac structure and function. These findings have important implications for HF prevention. Future studies should explore the role of specific nutrient types.