Neurological manifestations and risk factors associated with poor prognosis in hospitalized children with Omicron variant infection.

There are increasing reports of neurological manifestation in children with coronavirus disease 2019 (COVID-19). However, the frequency and clinical outcomes of in hospitalized children infected with the Omicron variant are unknown. The aim of this study was to describe the clinical characteristics, neurological manifestations, and risk factor associated with poor prognosis of hospitalized children suffering from COVID-19 due to the Omicron variant. Participants included children older than 28 days and younger than 18 years. Patients were recruited from December 10, 2022 through January 5, 2023. They were followed up for 30 days. A total of 509 pediatric patients hospitalized with the Omicron variant infection were recruited into the study. Among them, 167 (32.81%) patients had neurological manifestations. The most common manifestations were febrile convulsions (n = 90, 53.89%), viral encephalitis (n = 34, 20.36%), epilepsy (n = 23, 13.77%), hypoxic-ischemic encephalopathy (n = 9, 5.39%), and acute necrotizing encephalopathy (n = 6, 3.59%). At discharge, 92.81% of patients had a good prognosis according to the Glasgow Outcome Scale (scores ≥ 4). However, 7.19% had a poor prognosis. Eight patients died during the follow-up period with a cumulative 30-day mortality rate of 4.8% (95% confidence interval (CI) 1.5-8.1). Multivariate analysis revealed that albumin (odds ratio 0.711, 95% CI 0.556-0.910) and creatine kinase MB (CK-MB) levels (odds ratio 1.033, 95% CI 1.004-1.063) were independent risk factors of poor prognosis due to neurological manifestations. The area under the curve for the prediction of poor prognosis with albumin and CK-MB was 0.915 (95%CI 0.799-1.000), indicating that these factors can accurately predict a poor prognosis.          Conclusion: In this study, 32.8% of hospitalized children suffering from COVID-19 due to the Omicron variant infection experienced neurological manifestations. Baseline albumin and CK-MB levels could accurately predict poor prognosis in this patient population. What is Known: • Neurological injury has been reported in SARS-CoV-2 infection; compared with other strains, the Omicron strain is more likely to cause neurological manifestations in adults. • Neurologic injury in adults such as cerebral hemorrhage and epilepsy has been reported in patients with Omicron variant infection. What is New: • One-third hospitalized children with Omicron infection experience neurological manifestations, including central nervous system manifestations and peripheral nervous system manifestations. • Albumin and CK-MB combined can accurately predict poor prognosis (AUC 0.915), and the 30-day mortality rate of children with Omicron variant infection and neurological manifestations was 4.8%.

Trends in gynaecologic cancer mortality and the impact of the COVID-19 pandemic in the United States.

OBJECTIVES: Our aim was to assess the trend in gynaecologic cancer (GC) mortality in the period from 2010 to 2022 in the United States, with focus on the impact of the pandemic on increased deaths. METHODS: GC mortality data were extracted from the Center for Disease Control and Prevention Wide-Ranging Online Data for Epidemiologic Research (CDC WONDER) platform. We analysed mortality trends and evaluated observed vs. predicted mortality for the period from 2020 to 2022 with joinpoint regression and prediction modelling analyses. RESULTS: A total of 334,382 deaths among adults aged 25 years and older with gynaecologic cancer were documented from 2010 to 2022. The overall age-standardised mortality rate (ASMR, per 100,000 persons) for ovarian cancer-related death decreased gradually from 7.189 in 2010 to 5.517 in 2019, yielding an APC (annual percentage change) of -2.8%. However, the decrease in ovarian cancer-related mortality slowed down by more than 4-fold during the pandemic. Cervical cancer -related mortality decreased slightly prior to the pandemic and increased during the pandemic with an APC of 0.6%, resulting in excess mortality of 4.92%, 9.73% and 2.03% in 2020, 2021 and 2022, respectively. For uterine corpus cancer, the ASMR increased from 1.905 in 2010 to 2.787 in 2019, and increased sharply to 3.079 in 2021 and 3.211 in 2022. The ASMR rose steadily between 2013 and 2022, yielding an APC of 6.9%. CONCLUSIONS: Overall, we found that GC-related mortality increased during the COVID-19 pandemic, and this increase was not specific to age, race, or ethnicity.

Machine learning versus multivariate logistic regression for predicting severe COVID-19 in hospitalized children with Omicron variant infection.

With the emergence of the Omicron variant, the number of pediatric Coronavirus Disease 2019 (COVID-19) cases requiring hospitalization and developing severe or critical illness has significantly increased. Machine learning and multivariate logistic regression analysis were used to predict risk factors and develop prognostic models for severe COVID-19 in hospitalized children with the Omicron variant in this study. Of the 544 hospitalized children including 243 and 301 in the mild and severe groups, respectively. Fever (92.3%) was the most common symptom, followed by cough (79.4%), convulsions (36.8%), and vomiting (23.2%). The multivariate logistic regression analysis showed that age (1-3 years old, odds ratio (OR): 3.193, 95% confidence interval (CI): 1.778-5.733], comorbidity (OR: 1.993, 95% CI:1.154-3.443), cough (OR: 0.409, 95% CI:0.236-0.709), and baseline neutrophil-to-lymphocyte ratio (OR: 1.108, 95% CI: 1.023-1.200), lactate dehydrogenase (OR: 1.993, 95% CI: 1.154-3.443), blood urea nitrogen (OR: 1.002, 95% CI: 1.000-1.003) and total bilirubin (OR: 1.178, 95% CI: 1.005-3.381) were independent risk factors for severe COVID-19. The area under the curve (AUC) of the prediction models constructed by multivariate logistic regression analysis and machine learning (RandomForest + TomekLinks) were 0.7770 and 0.8590, respectively. The top 10 most important variables of random forest variables were selected to build a prediction model, with an AUC of 0.8210. Compared with multivariate logistic regression, machine learning models could more accurately predict severe COVID-19 in children with Omicron variant infection.