Children’s Intensive Care Research Program Student Projects
Whatever your area or level of study, we have variety of research projects that you can be involved in. Current available research projects are listed below. However, if there is a related area of research in which you are interested, please feel free to contact us to discuss.
BUILD: Advancing Implementation Science in Paediatric Critical Care: Optimising Uptake of Evidence-Based Interventions (PhD, MPhil, Honours)
Project Title
Advancing Implementation Science in Paediatric Critical Care: Optimising Uptake of Evidence-Based Interventions
Project Summary
This PhD project focuses on advancing implementation science in paediatric critical care by developing and applying mixed-methods approaches to evaluate and optimise the adoption of evidence-based interventions. Using the BUILD trial as a real-world application, the candidate will generate new insights into implementation processes, outcomes, and strategies to support scalable, sustainable practice change in intensive care settings.
Project Description
This PhD project focuses on advancing implementation science in paediatric critical care by developing and applying mixed-methods approaches to evaluate and optimise the uptake of evidence-based interventions in intensive care settings. Using the BUILD trial (a multicentre, hybrid effectiveness-implementation trial of prolonged beta-lactam infusions in paediatric sepsis) as a real-world application, the candidate will generate new insights into how complex interventions can be effectively embedded in clinical practice.
The successful candidate will investigate key implementation outcomes, including acceptability, feasibility, appropriateness, fidelity, adoption, sustainability, and cost-effectiveness, using a combination of quantitative and qualitative methods. This will include application of validated implementation science frameworks and measures (e.g., AIM, IAM, FIM), stakeholder interviews, and economic evaluation. This project will also explore barriers and facilitators to practice change across PICU settings and develop strategies to enhance intervention uptake and scalability.
Working closely with clinicians, trialists, and implementation scientists, the candidate will contribute to methodological innovation in hybrid trial design and implementation evaluation. This is an excellent opportunity for a candidate with interests in health services research, implementation science, or mixed-methods research to help shape how evidence is translated into practice and improve outcomes for critically ill children.
Project type: PhD, MPhil, Honours
Contact: Dr Sainath Raman
BUILD: Optimising Beta‑Lactam Infusion Strategies in Paediatric Sepsis: Pharmacokinetics, Resistance and Clinical Outcomes (PhD)
Project Title
Optimising Beta‑Lactam Infusion Strategies in Paediatric Sepsis: Pharmacokinetics, Resistance and Clinical Outcomes
Project Summary
PhD project evaluating pharmacokinetics, antimicrobial resistance and microbiome effects of intermittent, extended and continuous beta‑lactam infusion in children with sepsis to improve dosing and outcomes.
Project Description
This PhD candidate will work within the wider trial team in the multicentre, multinational RCT. The project will examine pharmacokinetic, microbiological and clinical effects of intermittent, extended and continuous beta‑lactam infusion in critically ill children with sepsis. Pharmacokinetic outcomes will be assessed using blood samples collected on day 3 post‑randomisation, alongside measurement of drug concentrations in sterile compartments including cerebrospinal fluid and pleural fluid. Microbiological sampling from skin swabs, stool and endotracheal aspirates or sputum will assess microbial colonisation, antimicrobial resistance development and changes in antibiograms compared with the wider PICU cohort. For patients with positive cultures, minimum inhibitory concentrations will be measured and compared between infusion modes. Associations between administration strategy, site of infection, causative microorganisms and outcomes will be explored. A metagenomics sub‑study will evaluate microbiome changes linked to infusion strategy.
Project type: PhD
Contact: Dr Sainath Raman
MELODY: Machine Learning in the Paediatric Intensive Care Unit: Development of Risk Prediction Models (PhD)
Project Title
Machine Learning in the Paediatric Intensive Care Unit: Development of Risk Prediction Models
Project Summary
This project will develop interpretable machine learning models to support clinical decision‑making in paediatric intensive care units. Using high‑resolution data from over 32,500 admissions across three international PICUs, it will focus on early detection of patient deterioration. A strong emphasis on explainability will ensure predictions are transparent and clinically meaningful. By aligning decision support tools with clinicians’ reasoning and workflows, the project aims to enable timely interventions and improve outcomes for critically ill children.
Project Description
This PhD project will develop interpretable machine learning models to support clinical decision-making for critically ill children in paediatric intensive care units (PICUs). Using high-resolution data from over 32,500 patient admissions across three international PICUs, the research will focus on early detection of patient deterioration. Emphasis will be placed on explainability and reasoning, ensuring that predictions and recommendations are transparent and clinically meaningful. The project will explore how to design and implement decision support tools that align with clinicians’ workflow and reasoning processes in high-pressure environments. The project aims to improve outcomes of Australia’s most critically unwell children by supported enhanced clinical decision making and, in turn, timely interventions, ultimately supporting precision medicine in critical care.
Project type: PhD
Contact: Professor Kristen Gibbons
RESONANCE: Methodological Advances in Paediatric Critical Care Trials: Novel Approaches for Cluster Crossover and Registry‑Enabled Designs (PhD)
Project Title
Methodological Advances in Paediatric Critical Care Trials: Novel Approaches for Cluster Crossover and Registry‑Enabled Designs
Project Summary
This PhD project focuses on developing and applying innovative statistical approaches for cluster crossover and registry-enabled randomised trials, using the landmark RESONANCE-PICU (Buffered Solutions versus 0.9% Sodium Chloride for Intravenous Fluid Therapy to Improve Outcomes for Critically ill Children: The RESONANCE-PICU Multicentre, Binational Randomised Controlled Trial) study as a real-world application setting.
Project Description
This PhD candidate will work within the wider trial team in the multicentre, bi national RCT; RESONANCE PICU. RESONANCE PICU compares the safety and efficacy of balanced/buffered solutions against standard 0.9% sodium chloride for intravenous fluid therapy. The successful candidate will explore cutting‑edge methods for trial design and data integration within large clinical registries, working closely with biostatisticians, clinicians, and national research networks. This is an exceptional opportunity for a statistics/mathematics graduate to help shape how future large‑scale trials are conducted, while gaining hands‑on experience with rich, high‑quality pediatric ICU data.
Project type: PhD
Contact: Dr Sainath Raman
Identifying and quantifying heterogeneity in PICU cohorts to improve short- and long-term outcomes
Precision medicine tailors therapies for an individual by taking into account the underlying heterogeneity in a population. Many models have been developed, but translation has only been successful in few disciplines. To date, the scarce precision medicine models in paediatric critical care have been limited by small sample sizes, lack of external validation and have not been trialled in clinical practice. However, PICU is optimised for precision medicine due to the large quantity of biomarkers measured, widespread implementation of electronic health records (EHRs), and extensive digitised physiological monitoring. Machine learning models derived from these granular, high-quality PICU datasets, coupled with omics data, will explain the biological heterogeneity. The models will identify children who are more likely to benefit from certain interventions and establish personalised care pathways for consumer-prioritised outcomes. Using the extensive, highly curated databank from the NITRIC clinical trial (largest international trial of infants undergoing heart surgery) and the follow-up study, and the RAPIDS observational cohort investigating better identification of children with sepsis, as sandpits to develop models to establish personalised trajectories, this project will:
- Derive and validate phenotypes to quantify the heterogeneity of the patient cohort (including infants undergoing heart surgery, and septix patients).
- Develop prediction models for prioritised short- and long-term outcomes incorporating clinical and omics data.
Project type: MPhil / PhD
Contact: Professor Kristen Gibbons
LYRICAL: Evaluating an Assisted Decision-making Tool on Clinical Outcomes in Critically Ill Children
There is a paucity of high-quality trials to inform the life-saving care of children admitted to PICU. >75% of all PICU trials have ≤100 participants, >80% only recruit from a single centre and only 3% have used an innovative trial design, severely hampering the delivery of robust and generalisable results. The COVID-19 pandemic highlighted the need for agile trial infrastructure and international networks to rapidly generate gold-standard, personalised evidence to inform practice. Adult intensive care researchers demonstrated the feasibility of such an approach in a critical care setting, enrolling thousands of patients into global interventional trials using innovative designs. These learnings need to be translated to the paediatric setting to trial precision medicine approaches in a robust, efficient manner, delivering best care for children. This project will lay the foundations for translating a machine-learning model into clinical practice. Components of the project will include undertaking a systematic review, developing and deploying silent models into clinical care, and collecting and evaluating stakeholder feedback in relation to implementation of such models, and, using implementation science, designing best practice tools to promote the highest level of successful model translation.
Project type: MPhil/ PhD
Contact: Professor Kristen Gibbons
Transforming Paediatric Intensive Care through PLATINUM: A Multicentre, Binational, Bayesian, Randomised, Multifactorial, Adaptive Platform Trial (PhD, MPhil, Honours)
Each year, more than 12,000 critically ill children are admitted to intensive care across Australia and New Zealand (ANZ). Despite receiving multiple concurrent treatments, many PICU practices lack high-quality paediatric evidence and rely on findings extrapolated from adults.
PLATINUM will address three consumer- and clinician-prioritised questions using a common primary outcome of days alive and free of organ support:
- Does a restrictive fluid strategy improve outcomes compared with standard care in mechanically ventilated children receiving intravenous fluids?
- Does a high-analgesia/low-sedation strategy improve outcomes compared with a high-sedation/low-analgesia strategy?
- Does personalised positive end-expiratory pressure (PEEP) titration to minimise driving pressure improve outcomes compared with standard care?
PLATINUM will generate evidence for three core PICU practices, with the potential to reduce organ support and healthcare costs across ANZ. It will also establish Australia’s first PICU platform trial, creating a sustainable and efficient framework for evaluating future interventions.
Project type: MPhil/PhD/Honours
Contact: Professor Kristen Gibbons
Improving the Long-term Health of Children after Intensive Care
Sepsis, defined as life-threatening organ dysfunction caused by dysregulated host response to infection, affects an estimated 48.9 million cases every year. Sepsis is not only a leading cause of global mortality, but can result in long-term morbidity related to physical, cognitive, and psychosocial sequelae. This translates into a major health and economic burden to societies. In 2017, the World Health Organisation (WHO) declared sepsis a global health priority. The WHO resolution stated an urgent need to implement measures for the prevention, diagnosis, management, and follow-up support of sepsis. This project will use existing MRFF and NHMRC funded cohorts to identify and explore patterns of responses to long-term follow-up assessments and questionnaires from children and families after an intensive care admission, co-design with stakeholders enhanced methods for conducting long-term follow-up, and implement such strategies through a prospective project.
Project type: MPhil/ PhD
Contact: Professor Kristen Gibbons