COVID-19 is commonly mild and self-limiting, but in a considerable portion of patients the disease is severe and fatal. Determining which patients are at high risk of severe illness or mortality is essential for appropriate clinical decision making. We propose a novel severity score specifically for COVID-19 to help predict disease severity and mortality. 4711 patients with confirmed SARS-CoV-2 infection were included. We derived a risk model using the first half of the cohort (n = 2355 patients) by logistic regression and bootstrapping methods. The discriminative power of the risk model was assessed by calculating the area under the receiver operating characteristic curves (AUC). The severity score was validated in a second half of 2356 patients. Mortality incidence was 26.4% in the derivation cohort and 22.4% in the validation cohort. A COVID-19 severity score ranging from 0 to 10, consisting of age, oxygen saturation, mean arterial pressure, blood urea nitrogen, C-Reactive protein, and the international normalized ratio was developed. A ROC curve analysis was performed in the derivation cohort achieved an AUC of 0.824 (95% CI 0.814–0.851) and an AUC of 0.798 (95% CI 0.789–0.818) in the validation cohort. Furthermore, based on the risk categorization the probability of mortality was 11.8%, 39% and 78% for patient with low (0–3), moderate (4–6) and high (7–10) COVID-19 severity score. This developed and validated novel COVID-19 severity score will aid physicians in predicting mortality during surge periods.

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.

Objective We aim to characterize the incidence, risk for mortality, and identify risk factors for mortality in patients presenting with hemorrhage and COVID-19. Methods This retrospective cohort study included a cohort of patients admitted to one of three major hospitals of our healthcare network including, an academic medical center and comprehensive stroke center, which accepts transfers for complex cases from eight community hospitals, during March 1 to May 1, 2020. All patients that received imaging of the neuroaxis and had positive PCR testing for COVID-19 were identified and reviewed by an attending neuroradiologist. Demographics and comorbidities were recorded. Biomarkers were recorded from the day of the hemorrhagic event. Vital signs from the day of the hemorrhagic event mechanical ventilation orders at admission were recorded. Imaging findings were divided into 5 subtypes; acute subdural hematoma (SDH), subarachnoid hemorrhage (SAH), multi-compartmental hemorrhage (MCH), multi-focal intracerebral hemorrhage (MFH), and focal intracerebral hemorrhage (fICH). Outcomes were recorded as non-routine discharge and mortality. Results We found a total of 35 out of 5227 patients with COVID-19 that had hemorrhage of some kind. Mortality for the entire cohort was 45.7 % (n = 16). SDH patients had a mortality rate of 35.3 % (n = 6), SAH had a mortality of 50 % (n = 1), MCH patients had a mortality of 71.4 % (n = 5), MFH patients had a mortality of 50 % (n = 2), fICH patients had a mortality of 40 % (n = 2). Patients with severe pulmonary COVID requiring mechanical ventilation (OR 10.24 [.43−243.12] p = 0.015), with INR > 1.2 on the day of the hemorrhagic event (OR 14.36 [1.69−122.14] p = 0.015], and patients presenting with spontaneous vs. traumatic hemorrhage (OR 6.11 [.31−118.89] p = 0.023) had significantly higher risk for mortality. Conclusions Hemorrhagic presentations with COVID-19 are a rare but serious way in which the illness can manifest. It is important for neurosurgeons to realize that patients can present with these findings without primary pulmonary symptoms, and that severe pulmonary symptoms, elevated INR, and spontaneous hemorrhagic presentations is associated with increased risk for mortality.

IntroductionCOVID-19 is commonly mild and self-limiting, but in a considerable portion of patients the disease is severe and fatal. Determining which patients are at high risk of severe illness or mortality is essential for appropriate clinical decision making. We propose a novel severity score specifically for COVID-19 to help predict disease severity and mortality.Methods4,711 patients with confirmed SARS-CoV-2 infection were included. We derived a risk model using the first half of the cohort (n=2,355 patients) by logistic regression and bootstrapping methods. The discriminative power of the risk model was assessed by calculating the area under the receiver operating characteristic curves (AUC). The severity score was validated in a second half of 2,356 patients.ResultsMortality incidence was 26.4% in the derivation cohort and 22.4% in the validation cohort. A COVID-19 severity score ranging from 0 to 10, consisting of age, oxygen saturation, mean arterial pressure, blood urea nitrogen, C-Reactive protein, and the international normalized ratio was developed. A ROC curve analysis was performed in the derivation cohort achieved an AUC of 0.824 (95% CI 0.814-0.851) and an AUC of 0.798 (95% CI 0.789-0.818) in the validation cohort. Furthermore, based on the risk categorization the probability of mortality was 11.8%, 39% and 78% for patient with low (0-3), moderate (4-6) and high (7-10) COVID-19 severity score.ConclusionThis developed and validated novel COVID-19 severity score will aid physicians in predicting mortality during surge periods.