Prevalence and Mortality Risk of Neurological Disorders during the COVID-19 Pandemic: An Umbrella Review of the Current Evidence

Clinical features of COVID-19-related encephalitis: comparison with the features of herpes virus encephalitis and autoimmune encephalitis

INTRODUCTION

Identifying coronavirus disease 2019 (COVID-19)-related encephalitis without clear etiological evidence is clinically challenging. The distinctions between this condition and other prevalent encephalitis types remain unknown. Therefore, we aimed to explore the similarities and differences in the clinical characteristics of COVID-19-related encephalitis and other encephalitis types.

METHODS

Adult patients with encephalitis admitted to the neurology department at Xuanwu Hospital were enrolled and categorized into the following six groups based on the results of metagenomic next-generation sequencing and autoimmune antibody detection in cerebrospinal fluid (CSF): COVID-19-related encephalitis (n = 36), herpes simplex virus type 1 encephalitis (HSV-1 encephalitis; n = 28), human herpesvirus 3 encephalitis (HHV-3 encephalitis; n = 10), NMDAR-antibody encephalitis (n = 18), LGI1-antibody encephalitis (n = 12), and GABAB-antibody encephalitis (n = 8).

RESULTS

The predominant characteristics of COVID-19-related encephalitis include a low incidence of seizures (38.9%), cognitive defects (30.6%), and meningeal irritation signs (8.3%). Compared with HSV-1 and HHV-3 encephalitis, COVID-19-related encephalitis exhibited lower white blood cell count (2.5 count/mm3), protein (32.2 mg/dL), and immunoglobulin M, G, and A levels (0.09, 3.2, and 0.46 mg/dL, respectively) in the CSF tests. Abnormal imaging findings were present in only 36.1% of COVID-19-related encephalitis cases, mostly showing diffuse inflammation scattered in various parts, which differed from HSV-1 encephalitis. Additionally, COVID-19-related encephalitis exhibited significant differences in clinical symptoms and CSF white blood cell counts compared with NMDAR-antibody encephalitis; however, it showed limited differences compared with LGI1-antibody and GABAB-antibody encephalitis.

DISCUSSION

COVID-19-related encephalitis and herpes virus or autoimmune encephalitis differ clinically. Symptoms and auxiliary examinations can be used as distinguishing tools.

GDF-15 Levels and Other Laboratory Findings as Predictors of COVID-19 Severity and Mortality: A Pilot Study

Growth differentiation factor 15 (GDF-15) is a stress-induced cytokine associated with acute and chronic inflammatory states. This prospective observational study aimed to investigate the prognostic roles of GDF-15 and routine clinical laboratory parameters in COVID-19 patients. Upon the admission of 95 adult hospitalized COVID-19 patients in Croatia, blood analysis was performed, and medical data were collected. The patients were categorized based on survival, ICU admission, and hospitalization duration. Logistic regression and ROC curve methods were employed for the statistical analysis. Logistic regression revealed two independent predictors of negative outcomes: CURB-65 score (OR = 2.55) and LDH (OR = 1.005); one predictor of ICU admission: LDH (OR = 1.004); and one predictor of prolonged hospitalization: the need for a high-flow nasal cannula (HFNC) upon admission (OR = 4.75). The ROC curve showed diagnostic indicators of negative outcomes: age, CURB-65 score, LDH, and GDF-15. The largest area under the curve (AUC = 0.767, specificity = 65.6, sensitivity = 83.9) was represented by GDF-15, with a cutoff value of 3528 pg/mL. For ICU admission, significant diagnostic indicators were LDH, CRP, and IL-6. Significant diagnostic indicators of prolonged hospitalization were CK, GGT, and oxygenation with an HFNC upon admission. This study reaffirms the significance of the commonly used laboratory parameters and clinical scores in evaluating COVID-19. Additionally, it introduces the potential for a new diagnostic approach and research concerning GDF-15 levels in this widespread disease.

COVID-19: The Development and Validation of a New Mortality Risk Score

Background: The coronavirus disease 2019 (COVID-19) pandemic has found the whole world unprepared for its correct management. Italy was the first European country to experience the spread of the SARS-CoV-2 virus at the end of February 2020. As a result of hospital overcrowding, the quality of care delivered was not always optimal. A substantial number of patients admitted to non-ICU units could have been treated at home. It would have been extremely useful to have a score that, based on personal and clinical characteristics and simple blood tests, could have predicted with sufficient reliability the probability that a patient had or did not have a disease that could have led to their death. This study aims to develop a scoring system to identify which patients with COVID-19 are at high mortality risk upon hospital admission, to expedite and enhance clinical decision making. Methods: A retrospective analysis was performed to develop a multivariable prognostic prediction model. Results: Derivation and external validation cohorts were obtained from two Italian University Hospital databases, including 388 (10.31% deceased) and 1357 (7.68% deceased) patients with confirmed COVID-19, respectively. A multivariable logistic model was used to select seven variables associated with in-hospital death (age, baseline oxygen saturation, hemoglobin value, white blood cell count, percentage of neutrophils, platelet count, and creatinine value). Calibration and discrimination were satisfactory with a cumulative AUC for prediction mortality of 0.924 (95% CI: 0.893-0.944) in derivation cohorts and 0.808 (95% CI: 0.886-0.828) in external validation cohorts. The risk score obtained was compared with the ISARIC 4C Mortality Score, and with all the other most important scores considered so far, to evaluate the risk of death of patients with COVID-19. It performed better than all the above scores to evaluate the predictability of dying. Its sensitivity, specificity, and AUC were higher than the other COVID-19 scoring systems when the latter were calculated for the 388 patients in our derivation cohort. Conclusions: In conclusion, the CZ-COVID-19 Score may help all physicians by identifying those COVID-19 patients who require more attention to provide better therapeutic regimens or, on the contrary, by identifying those patients for whom hospitalization is not necessary and who could therefore be sent home without overcrowding healthcare facilities. We developed and validated a new risk score based on seven variables for upon-hospital admission of COVID-19 patients. It is very simple to calculate and performs better than all the other similar scores to evaluate the predictability of dying.

Neurological manifestations and complications of COVID-19 in patients admitted to a tertiary care center in Nepal during the second wave

Neurological symptoms and signs of Coronavirus disease-19 (COVID-19) can accompany, follow, or precede respiratory symptoms and signs; hence, they are important in the diagnosis and management of COVID-19 patients. In this retrospective study conducted during the second wave of COVID-19, we included all patients diagnosed with COVID-19 using real-time polymerase chain reaction and admitted to the Tribhuvan University Teaching Hospital between June 2021 and October 2021. The patients were categorized into 2 groups: group A (with neurological manifestations or complications) and Group-B (without neurological manifestations or complications). The 2 groups were compared in terms of intensive care unit (ICU) admission, need for ventilatory support, length of hospital stay, and various outcomes. The study included 235 participants ranging in age from 13 to 102 years (mean age = 54 years, standard deviation = 18). Among the participants, 54.50% were male. The proportion of individuals in group A was higher (59.15%, N = 139) than that in Group-B (40.85%, N = 96). Notably, a significantly greater number of patients were admitted to the ICU in Group B than in Group A. However, there were no statistically significant differences in the need for ventilatory support or hospital stay between the 2 groups. Interestingly, group A showed a higher rate of improvement (Z = -3.1145, P = .00188, 95% CI), while Group-B had a higher rate of mortality (Z = 4.5562, P < .00001, 95% CI). Altered mental status and stroke have been specifically linked to poorer outcomes, whereas typical neurological manifestations, such as hyposmia, hypogeusia, dizziness, headache, and myalgia, are associated with better outcomes.