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Introduction: Children infected with COVID-19 are susceptible to severe manifestations. We aimed to develop and validate a predictive model for severe/ critical pediatric COVID-19 infection utilizing routinely available hospital level data to ascertain the likelihood of developing severe manifestations., Methods: The predictive model was based on an analysis of registry data from COVID-19 positive patients admitted to five tertiary pediatric hospitals across Asia [Singapore, Malaysia, Indonesia (two centers) and Pakistan]. Independent predictors of severe/critical COVID-19 infection were determined using multivariable logistic regression. A training cohort (n = 802, 70%) was used to develop the prediction model which was then validated in a test cohort (n = 345, 30%). The discriminative ability and performance of this model was assessed by calculating the Area Under the Curve (AUC) and 95% confidence interval (CI) from final Receiver Operating Characteristics Curve (ROC)., Results: A total of 1147 patients were included in this analysis. In the multivariable model, infant age group, presence of comorbidities, fever, vomiting, seizures and higher absolute neutrophil count were associated with an increased risk of developing severe/critical COVID-19 infection. The presence of coryza at presentation, higher hemoglobin and platelet count were associated with a decreased risk of severe/critical COVID-19 infection. The AUC (95%CI) generated for this model from the training and validation cohort were 0.96 (0.94, 0.98) and 0.92 (0.86, 0.97), respectively., Conclusion: This predictive model using clinical history and commonly used laboratory values was valuable in estimating the risk of developing a severe/critical COVID-19 infection in hospitalized children. Further validation is needed to provide more insights into its utility in clinical practice., Competing Interests: The authors have declared that no competing interests exist.

Background: Traumatic brain injury (TBI) can lead to significant psychological distress, but few psychologists in Australia are trained in working with this complex clinical group. Despite government funding to provide video-consulting (VC) services in Australia, uptake before COVID-19 was limited., Objective: This mixed methods study evaluated whether training in eHealth and evidence based TBI psychological therapies increased provider uptake of VC in clinical practice, and delivery of mental health services to individuals with TBI., Methods: Mental health professionals completed a range of self-report measures before (n = 50), after (n = 48), and four months following (n = 30) a one-day workshop. Participants' TBI knowledge, client-base and levels of access, confidence, motivation and attitudes toward VC were assessed. Knowledge did not increase after training but participants had significant increases in their confidence and motivation to using VC at follow up. Significant reductions in pragmatic barriers to using VC were reported post training and at follow up, all barrier categories indicated significant reductions. There was no significant change in clinical practice of the participants., Conclusions: Training to increase TBI knowledge requires specific assessment tools and although training appears to reduce barriers to using VC, uptake in clinical practice may require additional supervision and warrants further research.

There is a scarcity of data regarding coronavirus disease 2019 (COVID-19) infection in children from southeast and south Asia. This study aims to identify risk factors for severe COVID-19 disease among children in the region. This is an observational study of children with COVID-19 infection in hospitals contributing data to the Pediatric Acute and Critical Care COVID-19 Registry of Asia. Laboratory-confirmed COVID-19 cases were included in this registry. The primary outcome was severity of COVID-19 infection as defined by the World Health Organization (WHO) (mild, moderate, severe, or critical). Epidemiology, clinical and laboratory features, and outcomes of children with COVID-19 are described. Univariate and multivariable logistic regression models were used to identify risk factors for severe/critical disease. A total of 260 COVID-19 cases from eight hospitals across seven countries (China, Japan, Singapore, Malaysia, Indonesia, India, and Pakistan) were included. The common clinical manifestations were similar across countries: fever (64%), cough (39%), and coryza (23%). Approximately 40% of children were asymptomatic, and overall mortality was 2.3%, with all deaths reported from India and Pakistan. Using the multivariable model, the infant age group, presence of comorbidities, and cough on presentation were associated with severe/critical COVID-19. This epidemiological study of pediatric COVID-19 infection demonstrated similar clinical presentations of COVID-19 in children across Asia. Risk factors for severe disease in children were age younger than 12 months, presence of comorbidities, and cough at presentation. Further studies are needed to determine whether differences in mortality are the result of genetic factors, cultural practices, or environmental exposures.