Machine learning to predict poor school performance in paediatric survivors of intensive care: a population-based cohort study
Year: 2023
Type: article
Study design: Observational Study
Abstract: 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.
Source: Intensive Care Medicine
Authors: Patricia Gilholm, Kristen Gibbons, Sarah Brüningk, Juliane Klatt, Rhema Vaithianathan, Debbie Long, Johnny Millar, Wojtek Tomaszewski, Luregn J. Schlapbach, Anusha Ganeshalingam, Claire Sherring, Simon Erickson, Samantha Barr, Sainath Raman, Debbie Long, Luregn Schlapbach, Kristen Gibbons, Shane George, Puneet Singh, Vicky Smith, Warwick Butt, Carmel Delzoppo, Johnny Millar, Ben Gelbart, Felix Oberender, Subodh Ganu, Georgia Letton, Marino Festa, Gail Harper
Institutions: The University of Queensland, SIB Swiss Institute of Bioinformatics, ETH Zurich, Queensland University of Technology, Royal Children's Hospital, Australian and New Zealand Intensive Care Society, Murdoch Children's Research Institute, University of Zurich, University Children's Hospital Zurich
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $4,790
Award: G1294725005
OpenAlex ID: W4381853232