Characteristics of the Severe Acute Respiratory Syndrome Coronavirus 2 Omicron BA.2 Subvariant in Jilin, China from March to May 2022

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%.

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.