In this tertiary-care, pediatric hospital in Australia, the MET event rate and the rate of admission to the PICU because of MET events are lower than those reported for US pediatric hospitals. Despite these differences, Australian data suggest that outcomes are similar to US pediatric hospitals.

Radiation-induced damage to normal lung parenchyma remains a dose-limiting factor in thorax-associated radiotherapy (RT). Severe early and late complications with lungs can increase the risk of morbidity in cancer patients after RT. Herein, senescence of lung epithelial cells following RT-induced cellular stress, or more precisely the respective altered secretory profile, the senescence-associated secretory phenotype (SASP), was suggested as a central process for the initiation and progression of pneumonitis and pulmonary fibrosis. We previously reported that abrogation of certain aspects of the secretome of senescent lung cells, in particular, signaling inhibition of the SASP-factor Ccl2/Mcp1 mediated radioprotection especially by limiting endothelial dysfunction. Here, we investigated the therapeutic potential of a combined metformin treatment to protect normal lung tissue from RT-induced senescence and associated lung injury using a preclinical mouse model of radiation-induced pneumopathy. Metformin treatment efficiently limited RT-induced senescence and SASP expression levels, thereby limiting vascular dysfunctions, namely increased vascular permeability associated with increased extravasation of circulating immune and tumor cells early after irradiation (acute effects). Complementary in vitro studies using normal lung epithelial cell lines confirmed the senescence-limiting effect of metformin following RT finally resulting in radioprotection, while fostering RT-induced cellular stress of cultured malignant epithelial cells accounting for radiosensitization. The radioprotective action of metformin for normal lung tissue without simultaneous protection or preferable radiosensitization of tumor tissue might increase tumor control probabilities and survival because higher radiation doses could be used.

Background Mechanical power is a composite variable for energy transmitted to the respiratory system over time that may better capture risk for ventilator-induced lung injury than individual ventilator management components. We sought to evaluate if mechanical ventilation management with a high mechanical power is associated with fewer ventilator-free days (VFD) in children with pediatric acute respiratory distress syndrome (PARDS). Methods Retrospective analysis of a prospective observational international cohort study. Results There were 306 children from 55 pediatric intensive care units included. High mechanical power was associated with younger age, higher oxygenation index, a comorbid condition of bronchopulmonary dysplasia, higher tidal volume, higher delta pressure (peak inspiratory pressure—positive end-expiratory pressure), and higher respiratory rate. Higher mechanical power was associated with fewer 28-day VFD after controlling for confounding variables (per 0.1 J·min−1·Kg−1 Subdistribution Hazard Ratio (SHR) 0.93 (0.87, 0.98), p = 0.013). Higher mechanical power was not associated with higher intensive care unit mortality in multivariable analysis in the entire cohort (per 0.1 J·min−1·Kg−1 OR 1.12 [0.94, 1.32], p = 0.20). But was associated with higher mortality when excluding children who died due to neurologic reasons (per 0.1 J·min−1·Kg−1 OR 1.22 [1.01, 1.46], p = 0.036). In subgroup analyses by age, the association between higher mechanical power and fewer 28-day VFD remained only in children < 2-years-old (per 0.1 J·min−1·Kg−1 SHR 0.89 (0.82, 0.96), p = 0.005). Younger children were managed with lower tidal volume, higher delta pressure, higher respiratory rate, lower positive end-expiratory pressure, and higher PCO2 than older children. No individual ventilator management component mediated the effect of mechanical power on 28-day VFD. Conclusions Higher mechanical power is associated with fewer 28-day VFDs in children with PARDS. This association is strongest in children < 2-years-old in whom there are notable differences in mechanical ventilation management. While further validation is needed, these data highlight that ventilator management is associated with outcome in children with PARDS, and there may be subgroups of children with higher potential benefit from strategies to improve lung-protective ventilation. Take Home Message: Higher mechanical power is associated with fewer 28-day ventilator-free days in children with pediatric acute respiratory distress syndrome. This association is strongest in children <2-years-old in whom there are notable differences in mechanical ventilation management.

Purpose Whilst survival in paediatric critical care has improved, clinicians lack tools capable of predicting long-term outcomes. We developed a machine learning model to predict poor school outcomes in children surviving intensive care unit (ICU). Methods Population-based study of children < 16 years requiring ICU admission in Queensland, Australia, between 1997 and 2019. Failure to meet the National Minimum Standard (NMS) in the National Assessment Program-Literacy and Numeracy (NAPLAN) assessment during primary and secondary school was the primary outcome. Routine ICU information was used to train machine learning classifiers. Models were trained, validated and tested using stratified nested cross-validation. Results 13,957 childhood ICU survivors with 37,200 corresponding NAPLAN tests after a median follow-up duration of 6 years were included. 14.7%, 17%, 15.6% and 16.6% failed to meet NMS in school grades 3, 5, 7 and 9. The model demonstrated an Area Under the Receiver Operating Characteristic curve (AUROC) of 0.8 (standard deviation SD, 0.01), with 51% specificity to reach 85% sensitivity [relative Area Under the Precision Recall Curve (rel-AUPRC) 3.42, SD 0.06]. Socio-economic status, illness severity, and neurological, congenital, and genetic disorders contributed most to the predictions. In children with no comorbidities admitted between 2009 and 2019, the model achieved a AUROC of 0.77 (SD 0.03) and a rel-AUPRC of 3.31 (SD 0.42). Conclusions A machine learning model using data available at time of ICU discharge predicted failure to meet minimum educational requirements at school age. Implementation of this prediction tool could assist in prioritizing patients for follow-up and targeting of rehabilitative measures. Supplementary Information The online version contains supplementary material available at 10.1007/s00134-023-07137-1.