Overview
Background
Dr Trish Gilholm is a Research Fellow (3.5 years post‑PhD) within the Children’s Intensive Care Research Program, Child Health Research Centre. Her emerging research programs explore 1) long‑term outcomes for critically ill children using data linkage and 2) adaptive trial designs in paediatric critical care. Dr Gilholmcompleted her PhD in statistics at the Australian Centre of Excellence in Mathematical and Statistical Frontiers, Queensland University of Technology (PhD conferral September 2021) and was awarded an Executive Dean Commendation for Outstanding Doctoral Thesis Award for her PhD thesis. Through her developing research programs in adaptive trial design and data linkage, she has established a unique research profile within paediatric critical care. She is currently supervising 1xHonours (Principal Advisor), 1xPhD (Associate Advisor) and regularly supervises undergraduate and medical school research projects. Her outstanding commitment to research and future potential as a researcher was recognised with the 2024 Child Health Research Centre Rising Star of the Year Award.
Availability
- Dr Trish Gilholm is:
- Available for supervision
Qualifications
- Bachelor (Honours) of Psychological Science, The University of Queensland
- Masters (Research), Utrecht University
- Doctor of Philosophy, Queensland University of Technology
Research interests
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Long-term outcomes of critically ill children.
Children admitted to intensive care units (ICUs) are at higher risk of long-term physical, cognitive and psychological morbidities, which can impact their quality of life into adulthood. Through data linkage of the national PICU registry with external data sources encompassing health, education and socio-economic domains, my research develops predictive models of long-term developmental and educational outcomes. These models identify the modifiable and non-modifiable factors during PICU admission which contribute to poor long-term outcomes for these children.
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Adaptive clinical trials in paediatric critical care
Adaptive designs offer a flexible and efficient alternative to traditional randomised controlled trials (RCTs), however their use in the paediatric intensive care unit setting has been limited. My research aims to identify the barriers to implementation, increase awareness in the PICU community, and demonstrate the effectiveness of adaptive designs in paediatric critical care RCTs.
Works
Search Professor Trish Gilholm’s works on UQ eSpace
Featured
2023
Journal Article
Adaptive clinical trials in pediatric critical care: a systematic review
Gilholm, Patricia, Ergetu, Endrias, Gelbart, Ben, Raman, Sainath, Festa, Marino, Schlapbach, Luregn J., Long, Debbie, Gibbons, Kristen S. and on behalf of the Australian and New Zealand Intensive Care Society Paediatric Study Group (2023). Adaptive clinical trials in pediatric critical care: a systematic review. Pediatric Critical Care Medicine, 24 (9), 738-749. doi: 10.1097/pcc.0000000000003273
Featured
2023
Journal Article
Machine learning to predict poor school performance in paediatric survivors of intensive care: a population-based cohort study
Gilholm, Patricia, Gibbons, Kristen, Brüningk, Sarah, Klatt, Juliane, Vaithianathan, Rhema, Long, Debbie, Millar, Johnny, Tomaszewski, Wojtek, Schlapbach, Luregn J., Ganeshalingam, Anusha, Sherring, Claire, Erickson, Simon, Barr, Samantha, Raman, Sainath, Long, Debbie, Schlapbach, Luregn, Gibbons, Kristen, George, Shane, Singh, Puneet, Smith, Vicky, Butt, Warwick, Delzoppo, Carmel, Millar, Johnny, Gelbart, Ben, Oberender, Felix, Ganu, Subodh, Letton, Georgia, Festa, Marino, Harper, Gail and the Australian and New Zealand Intensive Care Society (ANZICS) Centre for Outcomes & Resource Evaluation (CORE) and ANZICS Paediatric Study Group (ANZICS PSG) (2023). Machine learning to predict poor school performance in paediatric survivors of intensive care: a population-based cohort study. Intensive Care Medicine, 49 (7), 785-795. doi: 10.1007/s00134-023-07137-1
Featured
2023
Journal Article
Validation of a paediatric sepsis screening tool to identify children with sepsis in the emergency department: a statewide prospective cohort study in Queensland, Australia
Gilholm, Patricia, Gibbons, Kristen, Lister, Paula, Harley, Amanda, Irwin, Adam, Raman, Sainath, Rice, Michael, Schlapbach, Luregn J. and Queensland Statewide Sepsis Collaborative (2023). Validation of a paediatric sepsis screening tool to identify children with sepsis in the emergency department: a statewide prospective cohort study in Queensland, Australia. BMJ Open, 13 (1) e061431, 1-25. doi: 10.1136/bmjopen-2022-061431
2025
Journal Article
Identification of distinct clinical profiles of sepsis risk in paediatric emergency department patients using Bayesian profile regression
Gilholm, Patricia, Raman, Sainath, Irwin, Adam, Lister, Paula, Harley, Amanda, Schlapbach, Luregn J. and Gibbons, Kristen S. (2025). Identification of distinct clinical profiles of sepsis risk in paediatric emergency department patients using Bayesian profile regression. BMJ Paediatrics Open, 9 (1) e003100, e003100-1. doi: 10.1136/bmjpo-2024-003100
2025
Journal Article
Comparison of Random Forest and Stepwise Regression for Variable Selection Using Low Prevalence Predictors: A case Study in Paediatric Sepsis
Gilholm, Patricia, Lister, Paula, Irwin, Adam, Harley, Amanda, Raman, Sainath, Schlapbach, Luregn J. and Gibbons, Kristen S. (2025). Comparison of Random Forest and Stepwise Regression for Variable Selection Using Low Prevalence Predictors: A case Study in Paediatric Sepsis. Maternal and Child Health Journal, 1-10. doi: 10.1007/s10995-025-04038-1
2024
Journal Article
Barriers and facilitators to implementing adaptive trial designs in paediatric critical care: an international mixed-methods study
Gilholm, Patricia, Wu, Ken, Le Marsney, Renate and Gibbons, Kristen (2024). Barriers and facilitators to implementing adaptive trial designs in paediatric critical care: an international mixed-methods study. Intensive Care Medicine - Paediatric and Neonatal, 2 (1) 31. doi: 10.1007/s44253-024-00054-1
2024
Journal Article
Assessing the impact of risk-based data monitoring on outcomes for a paediatric multicentre randomised controlled trial
Le Marsney, Renate, Johnson, Kerry, Chumbes Flores, Jenipher, Coetzer, Shelley, Darvas, Jennifer, Delzoppo, Carmel, Jolly, Arielle, Masterson, Kate, Sherring, Claire, Thomson, Hannah, Ergetu, Endrias, Gilholm, Patricia and Gibbons, Kristen S. (2024). Assessing the impact of risk-based data monitoring on outcomes for a paediatric multicentre randomised controlled trial. Clinical Trials, 21 (4), 461-469. doi: 10.1177/17407745231222019
2024
Journal Article
Post-traumatic stress and health-related quality of life after admission to paediatric intensive care: Longitudinal associations in mother–child dyads
Long, Debbie A., Gilholm, Patricia, Le Brocque, Robyne, Kenardy, Justin, Gibbons, Kristen and Dow, Belinda L. (2024). Post-traumatic stress and health-related quality of life after admission to paediatric intensive care: Longitudinal associations in mother–child dyads. Australian Critical Care, 37 (1), 98-105. doi: 10.1016/j.aucc.2023.10.004
2023
Journal Article
Impact of parental and healthcare professional concern on the diagnosis of pediatric sepsis: a diagnostic accuracy study
Sever, Zoe, Schlapbach, Luregn J., Gilholm, Patricia, Jessup, Melanie, Phillips, Natalie, George, Shane, Gibbons, Kristen and Harley, Amanda (2023). Impact of parental and healthcare professional concern on the diagnosis of pediatric sepsis: a diagnostic accuracy study. Frontiers in Pediatrics, 11 1140121, 1140121. doi: 10.3389/fped.2023.1140121
2022
Conference Publication
Identifying clinical symptom profiles for sepsis in children screened for sepsis on admission to the emergency department
Gilholm, P., Irwin, A., Lister, P., Harley, A., Raman, S., Schlapbach, L. J. and Gibbons, K. (2022). Identifying clinical symptom profiles for sepsis in children screened for sepsis on admission to the emergency department. 11th Congress of the World Federation of Pediatric Intensive & Critical Care Societies, Online, 12-16 July 2022. Philadelphia, PA United States: Lippincott Williams & Wilkins. doi: 10.1097/01.pcc.0000899876.46317.ec
2022
Journal Article
The current and future state of pediatric sepsis definitions: an international survey
Morin, Luc, Hall, Mark, de Souza, Daniela, Guoping, Lu, Jabornisky, Roberto, Shime, Nobuaki, Ranjit, Suchitra, Gilholm, Patricia, Nakagawa, Satoshi, Zimmerman, Jerry J., Sorce, Lauren R., Argent, Andrew, Kissoon, Niranjan, Tissières, Pierre, Watson, R. Scott and Schlapbach, Luregn J (2022). The current and future state of pediatric sepsis definitions: an international survey. Pediatrics, 149 (6) e2021052565. doi: 10.1542/peds.2021-052565
2021
Journal Article
Queensland Pediatric Sepsis Breakthrough Collaborative: Multicenter Observational Study to Evaluate the Implementation of a Pediatric Sepsis Pathway Within the Emergency Department
Harley, Amanda, Lister, Paula, Gilholm, Patricia, Rice, Michael, Venkatesh, Bala, Johnston, Amy N.B., Massey, Debbie, Irwin, Adam, Gibbons, Kristen and Schlapbach, Luregn J. (2021). Queensland Pediatric Sepsis Breakthrough Collaborative: Multicenter Observational Study to Evaluate the Implementation of a Pediatric Sepsis Pathway Within the Emergency Department. Critical Care Explorations, 3 (11) e0573, 1-14. doi: 10.1097/cce.0000000000000573
2021
Journal Article
Knowledge translation following the implementation of a state-wide Paediatric Sepsis Pathway in the emergency department- a multi-centre survey study
Harley, Amanda, Schlapbach, Luregn J., Lister, Paula, Massey, Debbie, Gilholm, Patricia and Johnston, Amy N. B. (2021). Knowledge translation following the implementation of a state-wide Paediatric Sepsis Pathway in the emergency department- a multi-centre survey study. BMC Health Services Research, 21 (1) 1161, 1161. doi: 10.1186/s12913-021-07128-2
2021
Journal Article
Bayesian hierarchical multidimensional item response modeling of small sample, sparse data for personalized developmental surveillance
Gilholm, Patricia, Mengersen, Kerrie and Thompson, Helen (2021). Bayesian hierarchical multidimensional item response modeling of small sample, sparse data for personalized developmental surveillance. Educational and Psychological Measurement, 81 (5) 0013164420987582, 936-956. doi: 10.1177/0013164420987582
2020
Journal Article
Identifying latent subgroups of children with developmental delay using Bayesian sequential updating and Dirichlet process mixture modelling
Gilholm, Patricia, Mengersen, Kerrie and Thompson, Helen (2020). Identifying latent subgroups of children with developmental delay using Bayesian sequential updating and Dirichlet process mixture modelling. PLoS One, 15 (6) e0233542, 1-17. doi: 10.1371/journal.pone.0233542
2018
Journal Article
Is inspiring group members an effective predictor of social dominance in early adolescence? direct and moderated effects of behavioral strategies, social skills, and gender on resource control and popularity
Vermande, Marjolijn M., Gilholm, Patricia A., Reijntjes, Albert H. A., Hessen, Dave J., Sterck, Elisabeth H. M. and Overduin-de Vries, Anne M. (2018). Is inspiring group members an effective predictor of social dominance in early adolescence? direct and moderated effects of behavioral strategies, social skills, and gender on resource control and popularity. Journal of Youth and Adolescence, 47 (9), 1813-1829. doi: 10.1007/s10964-018-0830-9
Supervision
Availability
- Dr Trish Gilholm is:
- Available for supervision
Before you email them, read our advice on how to contact a supervisor.
Available projects
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Educational outcomes of PICU sepsis survivors in Queensland
Sepsis is a dangerous condition in children caused by the body's response to infection, which can lead to organ problems and even death if not treated quickly. Children who survive sepsis may face ongoing educational difficulties, such as trouble learning, cognitive issues, and struggles in school. To better understand the long-term effects, this project will use data from a 20-year period, linking information about sepsis survivors in paediatric intensive care units (PICU) with standardised educational assessments in Queensland. The results will help inform support programs for this vulnerable group, ensuring they receive the necessary help to succeed in their education.
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Impact of PICU admission on school performance for Queensland school-aged children: a pre-post study
Our team has developed a machine learning model to predict poor school outcomes in children who survived the intensive care unit (ICU). We used data from over 13,000 childhood ICU survivors in Queensland, Australia, over a 22-year period. The model showed promising results with an ability to predict school performance based on data available at the time of ICU discharge, which could help prioritise patients for follow-up care and target rehabilitation efforts. However, most children who are admitted to ICU are admitted prior to school-age, which limited our ability to assess more immediate effects of ICU admission on children’s educational performance. This project will focus on the school performance of school aged PICU survivors, and will assess the change in educational performance before and after a PICU admission.
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Developing prediction models to evaluate long-term outcomes in paediatric survivors of critical illness
Each year, more than 10,000 children in Australia undergo life-saving treatment in intensive care units (ICUs). While the survival rate exceeds 97%, up to one-third of paediatric ICU (PICU) survivors experience physical, cognitive and/or psychosocial challenges that can persist for months—and even years—after admission. This project will utilise a large registry-based dataset of all PICU admissions in the state of Queensland (>40,000 admissions) which has been linked to administrative datasets encompassing health, developmental and educational outcomes. This project will develop novel statistical methods to predict poor long-term outcomes of PICU survivors and identify modifiable risk factors to guide targeted and standardised interventional clinical trials and inform public policy.
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Educational outcomes of PICU sepsis survivors in Queensland
Sepsis is a dangerous condition in children caused by the body's response to infection, which can lead to organ problems and even death if not treated quickly. Children who survive sepsis may face ongoing educational difficulties, such as trouble learning, cognitive issues, and struggles in school. To better understand the long-term effects, this project will use data from a 20-year period, linking information about sepsis survivors in paediatric intensive care units (PICU) with standardised educational assessments in Queensland. The results will help inform support programs for this vulnerable group, ensuring they receive the necessary help to succeed in their education.
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Impact of PICU admission on school performance for Queensland school-aged children: a pre-post study
Our team has developed a machine learning model to predict poor school outcomes in children who survived the intensive care unit (ICU). We used data from over 13,000 childhood ICU survivors in Queensland, Australia, over a 22-year period. The model showed promising results with an ability to predict school performance based on data available at the time of ICU discharge, which could help prioritise patients for follow-up care and target rehabilitation efforts. However, most children who are admitted to ICU are admitted prior to school-age, which limited our ability to assess more immediate effects of ICU admission on children’s educational performance. This project will focus on the school performance of school aged PICU survivors, and will assess the change in educational performance before and after a PICU admission.
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Developing prediction models to evaluate long-term outcomes in paediatric survivors of critical illness
Each year, more than 10,000 children in Australia undergo life-saving treatment in intensive care units (ICUs). While the survival rate exceeds 97%, up to one-third of paediatric ICU (PICU) survivors experience physical, cognitive and/or psychosocial challenges that can persist for months—and even years—after admission. This project will utilise a large registry-based dataset of all PICU admissions in the state of Queensland (>40,000 admissions) which has been linked to administrative datasets encompassing health, developmental and educational outcomes. This project will develop novel statistical methods to predict poor long-term outcomes of PICU survivors and identify modifiable risk factors to guide targeted and standardised interventional clinical trials and inform public policy.
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Outcome Measures in Paediatric Intensive Care Trials: A systematic Review
This systematic review aims to comprehensively examine the primary and secondary outcomes reported in trials undertaken in the setting of Paediatric Intensive Care Units (PICUs). Specifically, we aim to answer:
- What are the frequency and definitions of primary and secondary outcome measures used in PICU trials reported from 2010 to 2023?
- What are the analysis methods used for the most frequent primary outcome measures?
- What are the trends in use of outcome measures geographically, over time, by sample size, and in multicentre and multinational studies?
- What is the distribution of key primary outcome measures across a number of geographical settings and indications?
The project will involve updating our living database of PICU RCTs, data extraction and contribution to analysis and manuscript writing.
Supervision history
Current supervision
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Doctor Philosophy
Machine learning in the Paediatric Intensive Care Unit: Development of Risk Prediction Models
Associate Advisor
Other advisors: Dr Sainath Raman, Professor Kristen Gibbons
Media
Enquiries
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