In this single-center study, hyperoxia at admission to the PICU was highly correlated with increased risk-adjusted mortality. Further investigation of these observations in a large multicenter cohort is warranted.
Introduction: Septic shock remains amongst the leading causes of childhood mortality. Therapeutic options to support children with septic shock refractory to initial resuscitation with fluids and inotropes are limited. Recently, the combination of intravenous hydrocortisone with high dose ascorbic acid and thiamine (HAT therapy), postulated to reduce sepsis-related organ dysfunction, has been proposed as a safe approach with potential for mortality benefit, but randomized trials in paediatric patients are lacking. We hypothesize that protocolised early use of HAT therapy (“metabolic resuscitation”) in children with septic shock is feasible and will lead to earlier resolution of organ dysfunction. Here, we describe the protocol of the Resuscitation in Paediatric Sepsis Using Metabolic Resuscitation–A Randomized Controlled Pilot Study in the Paediatric Intensive Care Unit (RESPOND PICU).Methods and Analysis: The RESPOND PICU study is an open label randomized-controlled, two-sided multicentre pilot study conducted in paediatric intensive care units (PICUs) in Australia and New Zealand. Sixty children aged between 28 days and 18 years treated with inotropes for presumed septic shock will be randomized in a 1:1 ratio to either metabolic resuscitation (1 mg/kg hydrocortisone q6h, 30 mg/kg ascorbic acid q6h, 4 mg/kg thiamine q12h) or standard septic shock management. Main outcomes include feasibility of the study protocol and survival free of organ dysfunction censored at 28 days. The study cohort will be followed up at 28-days and 6-months post enrolment to assess neurodevelopment, quality of life and functional status. Biobanking will allow ancillary studies on sepsis biomarkers.Ethics and Dissemination: The study received ethical clearance from Children's Health Queensland Human Research Ethics Committee (HREC/18/QCHQ/49168) and commenced enrolment on June 12th, 2019. The primary study findings will be submitted for publication in a peer-reviewed journal.Trial Registration: Australian and New Zealand Clinical Trials Registry (ACTRN12619000829112). Protocol Version: V1.8 22/7/20.
Although durable clinical responses are achieved in a significant number of patients given Immune checkpoint inhibitors (ICI), like anti-CTLA-4 and anti-PD-1 inhibitors, some of the cancers have shown little or no response to ICI therapy. Even within the known responsive cancers, there is often a subset of non-responsive patients. Due to the accelerated FDA approval of these immunotherapies, the biomarker development has not been able to keep pace. Appropriate predictive, prognostic and surrogate biomarkers are needed to maximally exploit the benefits from ICI therapy for correct and timely stratification of patients to treatment, for monitoring treatment effect, and for avoiding costs and unwanted toxicities when therapy is likely to be ineffective. As the number of clinical trials exploring the utility of these treatments, both as stand-alone and as combination therapy for several cancers is escalating dramatically, the need for appropriate biomarkers is further amplified. This review discusses the potential biomarkers being investigated in ICI therapies, focusing mainly on immunohistochemical expression of PDL-1 and the immune correlates. Various immune components discussed here include the cells of innate (natural killer or NK cells) and adaptive (CD4+ and CD8+ cells) immunity, regulatory and inhibitory immune cells (regulatory T cells or Tregs and myeloid derived suppressor cells or MDSCs), as well as cytokines. Immune checkpoint molecule, programmed death receptor ligand-1 (PD-L1) and various molecules and pathways influencing its expression are also discussed.
There are few robust, national-level reports of contemporary trends in pediatric oncology admissions, resource use, and mortality. We aimed to describe national-level data on trends in intensive care admissions, interventions, and survival for children with cancer.
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.
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