[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124688-en":3,"doc-seo-124688-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124688,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",8,"Research & Report","Machine learning to predict poor school performance in paediatric survivors of intensive care - a population-based cohort study","Improved survival in paediatric critical care has not been matched by practical tools for forecasting long-term outcomes. This study developed a machine learning model to predict poor school performance in children after ICU survival. Using linked statewide population data, children under 16 admitted to ICU in Queensland from 1997 to 2019 were assessed via NAPLAN against National Minimum Standard criteria. Routine ICU information trained and evaluated classifiers with stratified nested cross-validation. The model achieved AUROC around 0.8, with key contributions from socio-economic status, illness severity, and neurological, congenital and genetic disorders.","Intensive Care Med (2023) 49:785–795  \n[https://doi.org/10.1007/s00134-023-07137-1](https://doi.org/10.1007/s00134-023-07137-1)  \nORIGINAL  \nMachine learning to predict poor school  \nperformance in paediatric survivors of intensive care: a population-based cohort study  \nPatricia Gilholm1, Kristen Gibbons1, Sarah Brüningk2,3, Juliane Klatt2,3, RhemaVaithianathan4, Debbie Long1,5, Johnny Millar6,7,8, Wojtek Tomaszewski4 and Luregn J. Schlapbach 1,9* on behalf of the Australian and New Zealand Intensive Care Society (ANZICS) Centre for Outcomes & Resource Evaluation (CORE) and ANZICS Paediatric Study Group (ANZICS PSG)  \n© 2023 The Author(s)  \nAbstract  \nPurpose: 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) .  \nMethods: Population-based study of children \u003C 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.  \nResults: 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) .  \nConclusions: 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.  \nKeywords: Child, Intensive care, Machine learning, Neurodevelopment, School  \n*Correspondence: luregn. schlapbach@kispi. uzh. ch  \n9 Department of Intensive Care and Neonatology, and Children’s Research Center, University Children’s Hospital Zurich, Steinwiesstrasse 75,  \n8032 Zurich, Switzerland  \nFull author information is available at the end of the article  \nThe details of the “Australian and New Zealand Intensive Care Society Paediatric Study Group” are listed in the Acknowledgements section.  \nIntroduction  \nProgress in the field of paediatric intensive care over the past decades has led to a reduction of in-hospital mortality to as little as 2.5% even for complex conditions such as congenital heart disease or cancer [1–3]. However, critical illness during childhood occurs at a vulnerable period  \nof brain development, and neurological injury may result from disease, complications or treatment-related mechanisms, for example inadequate cerebral oxygen supply during shock or drug-related toxicity [4–6]. Families of critically ill children, clinicians, and researchers consider survival with good long-term neurodevelopment asa priority for care, benchmarking, and research [7]. The ability of a child to meet minimum requirements in primary or secondary school represents a desirable outcome from the family, healthcare provider and societal perspectives and translates into a high chance to ultimately learn a profession, earn an income and lead an independent life in adulthood. Yet, most paediatric intensive care unit (PICU) survivors ","cbCaioUrQQQbZAkx","https://ap.wps.com/l/cbCaioUrQQQbZAkx","pdf",698654,1,11,"English","en",105,"# Abstract\n## Purpose\n## Methods\n## Results\n## Conclusions\n# Introduction\n## Rationale and unmet need\n# Methods\n## Study design and overview\n## Study population\n# Take-home message","[{\"question\":\"What long-term outcome did the study aim to predict after ICU discharge?\",\"answer\":\"It predicted failure to meet the National Minimum Standard in NAPLAN literacy and numeracy during primary and secondary school.\"},{\"question\":\"What data were used to build the prediction model?\",\"answer\":\"Routine ICU information at discharge was linked with NAPLAN results from children admitted to ICU in Queensland, Australia, between 1997 and 2019.\"},{\"question\":\"How well did the machine learning model perform?\",\"answer\":\"Across all included ICU survivors, the model showed an AUROC of about 0.8 and specificity enabling 85% sensitivity, with major predictive contributions from socio-economic status, illness severity, and specific disorder categories.\"}]","Machine learning to predict poor school performance in paediatric survivors of intensive care - a population-based cohort study | PDF",1785893927,28,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-to-predict-poor-school-performance-in-paediatric-survivors-of-intensive-care-a-population-based-cohort-study-124688","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-to-predict-poor-school-performance-in-paediatric-survivors-of-intensive-care-a-population-based-cohort-study-124688/124688/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What long-term outcome did the study aim to predict after ICU discharge?","Question",{"text":75,"@type":76},"It predicted failure to meet the National Minimum Standard in NAPLAN literacy and numeracy during primary and secondary school.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What data were used to build the prediction model?",{"text":80,"@type":76},"Routine ICU information at discharge was linked with NAPLAN results from children admitted to ICU in Queensland, Australia, between 1997 and 2019.",{"name":82,"@type":73,"acceptedAnswer":83},"How well did the machine learning model perform?",{"text":84,"@type":76},"Across all included ICU survivors, the model showed an AUROC of about 0.8 and specificity enabling 85% sensitivity, with major predictive contributions from socio-economic status, illness severity, and specific disorder categories.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]