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Objectives: Major postintensive care sequelae affect up to one in three adult survivors of critical illness. Large cohorts on educational outcomes after pediatric intensive care are lacking. We assessed primary school educational outcomes in a statewide cohort of children who survived PICU during childhood., Design: Multicenter population-based study on children less than 5 years admitted to PICU. Using the National Assessment Program-Literacy and Numeracy database, the primary outcome was educational achievement below the National Minimum Standard (NMS) in year 3 of primary school. Cases were compared with controls matched for calendar year, grade, birth cohort, sex, socioeconomic status, Aboriginal and Torres Strait Islander status, and school. Multivariable logistic regression models to predict educational outcomes were derived., Setting: Tertiary PICUs and mixed ICUs in Queensland, Australia., Patients: Children less than 5 years admitted to PICU between 1998 and 2016., Interventions: Not applicable., Measurements and Main Results: Year 3 primary school data were available for 5,017 PICU survivors (median age, 8.0 mo at first PICU admission; interquartile range, 1.9-25.2). PICU survivors scored significantly lower than controls across each domain (p < 0.001); 14.03% of PICU survivors did not meet the NMS compared with 8.96% of matched controls (p < 0.001). In multivariate analyses, socioeconomic status (odds ratio, 2.14; 95% CI, 1.67-2.74), weight (0.94; 0.90-0.97), logit of Pediatric Index of Mortality-2 score (1.11; 1.03-1.19), presence of a syndrome (11.58; 8.87-15.11), prematurity (1.54; 1.09-2.19), chronic neurologic conditions (4.38; 3.27-5.87), chronic respiratory conditions (1.65; 1.24-2.19), and continuous renal replacement therapy (4.20; 1.40-12.55) were independently associated with a higher risk of not meeting the NMS., Conclusions: In this population-based study of childhood PICU survivors, 14.03% did not meet NMSs in the standardized primary school assessment. Socioeconomic status, underlying diseases, and severity on presentation allow risk-stratification to identify children most likely to benefit from individual follow-up and support., Competing Interests: Dr. Tomaszewski’s institution received funding from the Queensland Department of Education; he disclosed that data linkage was provided free of charge by the Queensland Department of Health. This manuscript provides research findings and does not represent the views of the Department of Education. Dr. Schlapbach received support for article research from grants from the Intensive Care Foundation Australia, an Education Horizon grant from the Queensland Department of Education, and Children’s Hospital Foundation Australia, and the National Health and Medical Research Council Practitioner Fellowship. The remaining authors have disclosed that they do not have any potential conflicts of interest., (Copyright © 2022 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the Society of Critical Care Medicine and Wolters Kluwer Health, Inc.)

Bronchiolitis represents the most common cause of non-elective admission to paediatric intensive care units (ICUs).We assessed changes in admission rate, respiratory support, and outcomes of infants <24 months with bronchiolitis admitted to ICU between 2002 and 2014 in Australia and New Zealand.During the study period, bronchiolitis was responsible for 9628 (27.6%) of 34 829 non-elective ICU admissions. The estimated population-based ICU admission rate due to bronchiolitis increased by 11.76 per 100 000 each year (95% CI 8.11-15.41). The proportion of bronchiolitis patients requiring intubation decreased from 36.8% in 2002, to 10.8% in 2014 (adjusted OR 0.35, 95% CI 0.27-0.46), whilst a dramatic increase in high-flow nasal cannula therapy use to 72.6% was observed (p<0.001). We observed considerable variability in practice between units, with six-fold differences in risk-adjusted intubation rates that were not explained by ICU type, size, or major patient factors. Annual direct hospitalisation costs due to severe bronchiolitis increased to over USD30 million in 2014.We observed an increasing healthcare burden due to severe bronchiolitis, with a major change in practice in the management from invasive to non-invasive support that suggests thresholds to admittance of bronchiolitis patients to ICU have changed. Future studies should assess strategies for management of bronchiolitis outside ICUs., Competing Interests: Conflict of interest: None declared., (Copyright ©ERS 2017.)

Objectives: To provide an updated version of the Paediatric Index of Mortality 2 for assessing the risk of mortality among children admitted to an ICU., Design: International, multicenter, prospective cohort study., Setting: Sixty ICUs that accept pediatric admissions in Australia, New Zealand, Ireland, and the United Kingdom., Patients: All children admitted in 2010 and 2011 younger than 18 years old at the time of admission and either died in ICU or were discharged. Patients who were transferred to another ICU were not included. Fifty-three thousand one hundred twelve patient admissions were included in the analysis., Interventions: None., Measurement and Main Results: A revised prediction model was built using logistic regression. Variable selection was based on significance at the 95% level and overall improvement of the model's discriminatory performance and goodness of fit. The final model discriminated well (area under the curve, 0.88, 0.88-0.89); however, the model performed better in Australia and New Zealand than in the United Kingdom and Ireland (area under the curve was 0.91, 0.90-0.93 and 0.85, 0.84-0.86, respectively)., Conclusions: Paediatric Index of Mortality 3 provides an international standard based on a large contemporary dataset for the comparison of risk-adjusted mortality among children admitted to intensive care.

Objective: To quantify between-unit variability in mean length of stay (LoS) between intensive care units (ICUs) after adjusting for differences in case mix using a method that does not require arbitrary trimming of data., Setting: An analysis of registry data from pediatric ICUs (PICUs) in Australia and New Zealand., Study Design: The relationships between patient LoS and associated patient factors were modeled as a log-linear function of the covariates using two gamma distributions. The predicted distribution is estimated as a weighted average of the two distributions where the relative weighting is conditional on the patient's elective status., Data Collection: Data for 12,763 admissions submitted to the Australian and New Zealand Paediatric Intensive Care Registry from the eight dedicated PICUs in Australia and New Zealand in 2007 and 2008., Principal Findings: The two distributions of the mixture model accurately described the distribution of short- and long-stay patients in ICUs. After adjusting for patient case mix, several sites had a statistically significant effect on patient LoS., Conclusion: The two-compartment model characterizes ICU LoS for short- and long-stay patients more effectively than a single-compartment model. There is significant site-level variation in the LoS among children admitted to ICUs in Australia and New Zealand. Differences in the site-level variation between short- and long-stay patients indicate differences in discharge practice., (© Health Research and Educational Trust.)

Objective: To test if there was significant variation of mean adjusted duration of respiratory support (RS) at the site level among Australian and New Zealand pediatric intensive care units (PICUs) and to determine whether the pattern of variation differed from the site-level pattern of variation in length of stay (LoS)., Design: Separate outcome prediction models for estimating PICU LoS and duration of RS were constructed, using patient data collected between 2002 and 2007. Gamma regression was used to model LoS for all admissions included in the study population, and log normal regression was used to model duration of RS for the subset of patients receiving RS. For both models, case-mix adjustment was achieved by entering patient risk factors as fixed effects, and the PICU or site of care was entered as a random effect., Setting: Data for 31,358 admissions were collected from nine specialist PICUs in Australia and New Zealand., Measurements and Main Results: Average risk-adjusted duration of RS and LoS for each PICU. There was significant unit-level variation in the adjusted mean LoS and duration of RS among PICUs in Australia and New Zealand. One site had a mean duration of RS that was significantly longer than expected, whereas two sites had a mean duration of RS that was significantly shorter than expected at the 95% level. Unit-level variation in duration of RS is consistent with unit-level variation in LoS for six PICUs and significantly different in two units., Conclusion: There is unit-level variation in LoS and duration of RS, not accounted for by case-mix. Concurrent analysis of unit-level variation in LoS and duration of RS can help to identify differences in discharge practice and provide direction for improvements in clinical or administrative efficiency.