Severe Pneumonia in PICU Admissions: The Pediatric Acute and Critical Care Medicine Asian Network (PACCMAN) Observational Cohort Study, 2020-2022.

OBJECTIVES: Mortality from pneumonia is three times higher in Asia compared with industrialized countries. We aimed to determine the epidemiology, microbiology, and outcome of severe pneumonia in PICUs across the Pediatric Acute and Critical Care Medicine Asian Network (PACCMAN). DESIGN: Prospective multicenter observational study from June 2020 to September 2022. SETTING: Fifteen PICUs in PACCMAN. PATIENTS: All children younger than 18 years old diagnosed with pneumonia and admitted to the PICU. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Clinical, microbiologic, and outcome data were recorded. The primary outcome was PICU mortality. Univariate and multivariable logistic regression was performed to investigate associations between PICU mortality and explanatory risk factors on presentation to the PICU. Among patients screened, 846 of 11,778 PICU patients (7.2%) with a median age of 1.2 years (interquartile range, 0.4-3.7 yr) had pneumonia. Respiratory syncytial virus was detected in 111 of 846 cases (13.1%). The most common bacteria were Staphylococcus species (71/846 [8.4%]) followed by Pseudomonas species (60/846 [7.1%]). Second-generation cephalosporins (322/846 [38.1%]) were the most common broad-spectrum antibiotics prescribed, followed by carbapenems (174/846 [20.6%]). Invasive mechanical ventilation and noninvasive respiratory support was provided in 438 of 846 (51.8%) and 500 of 846 (59.1%) patients, respectively. PICU mortality was 65 of 846 (7.7%). In the multivariable logistic regression model, age (adjusted odds ratio [aOR], 1.08; 95% CI, 1.00-1.16), Pediatric Index of Mortality 3 score (aOR, 1.03; 95% CI, 1.02-1.05), and drowsiness (aOR, 2.73; 95% CI, 1.24-6.00) were associated with greater odds of mortality. CONCLUSIONS: In the PACCMAN contributing PICUs, pneumonia is a frequent cause for admission (7%) and is associated with a greater odds of mortality.

Development and validation of a clinical predictive model for severe and critical pediatric COVID-19 infection.

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.

Gross Motor Profile and Its Association with Socialization Skills in Children with Autism Spectrum Disorders.

BACKGROUND: While social impairment is considered to be the core deficit in children with autism spectrum disorder (ASD), a large proportion of these children have poor gross motor ability, and gross motor deficits may influence socialization skills in children with ASD. The objectives of this study were to compare gross motor skills in children with ASD to typically developing children, to describe gross motor problems in children with ASD, and to investigate associations between gross motor and socialization skills in children with ASD. METHODS: This was a cross-sectional study including 40 ASD children aged from 18 months to 6 years and 40 age-matched typically developing controls. Gross motor and socialization skills were scored using the Vineland Adaptive Behavior Scales, 2nd edition (Vineland-II). RESULTS: Below average gross motor function was found in eight of 40 (20%) ASD children. The mean gross motor v-scale score in the ASD group was 15.1 [standard deviation (SD) 3.12], significantly lower than in the control group [18.7, SD 2.09, p = 0.0001; 95% confidence intervals (CI) from -4.725 to -2.525]. The differences were most prominent in ball throwing and catching, using stairs, jumping, and bicycling. The ASD children with gross motor impairments had a mean socialization domain score of 66.6 (SD 6.50) compared to 85.7 (SD 10.90) in those without gross motor impairments (p = 0.0001, 95% CI from -25.327 to -12.736). CONCLUSION: Children with ASD had lower gross motor skills compared to typically developing children. Gross motor impairments were found in 20% of the ASD children, and these children also had lower socialization skills than those without gross motor impairments.