Machine learning to predict poor school performance in paediatric survivors of intensive care: a population-based cohort study
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Description
<p>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.</p>
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ID Code: | 246157 | ||
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Item Type: | Contribution to Journal (Journal Article) | ||
Refereed: | Yes | ||
ORCID iD: |
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Additional Information: | Funding: Open access funding provided by University of Zurich. The study was supported by grants from the Intensive Care Foundation Australia, an Education Horizon grant from the Queensland Department of Education, and a grant from the Children`s Hospital Foundation, Australia. LJS was supported by a National Health and Medical Research Council (NHMRC) Practitioner Fellowship, by the Children`s Hospital Foundation, Australia, and by the NOMIS foundation. S.B. was funded by the Botnar Research Centre for Child Health Postdoctoral Excellence Programme (#PEP-2021-1008). | ||
Measurements or Duration: | 11 pages | ||
Keywords: | Child, Intensive care, Machine learning, Neurodevelopment, School | ||
DOI: | 10.1007/s00134-023-07137-1 | ||
ISSN: | 0342-4642 | ||
Pure ID: | 157144217 | ||
Divisions: | Current > Research Centres > Centre for Healthcare Transformation Current > QUT Faculties and Divisions > Faculty of Health Current > Schools > School of Nursing |
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Funding Information: | Australian and New Zealand Intensive Care Society Paediatric Study Group: 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. Open access funding provided by University of Zurich. The study was supported by grants from the Intensive Care Foundation Australia, an Education Horizon grant from the Queensland Department of Education, and a grant from the Children`s Hospital Foundation, Australia. LJS was supported by a National Health and Medical Research Council (NHMRC) Practitioner Fellowship, by the Children`s Hospital Foundation, Australia, and by the NOMIS foundation. S.B. was funded by the Botnar Research Centre for Child Health Postdoctoral Excellence Programme (#PEP-2021-1008). | ||
Copyright Owner: | 2023 The Authors | ||
Copyright Statement: | This work is covered by copyright. Unless the document is being made available under a Creative Commons Licence, you must assume that re-use is limited to personal use and that permission from the copyright owner must be obtained for all other uses. If the document is available under a Creative Commons License (or other specified license) then refer to the Licence for details of permitted re-use. It is a condition of access that users recognise and abide by the legal requirements associated with these rights. If you believe that this work infringes copyright please provide details by email to qut.copyright@qut.edu.au | ||
Deposited On: | 06 Feb 2024 02:47 | ||
Last Modified: | 15 Jul 2024 17:07 |
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