Charly Huxford, Alireza Rafiei, Vuong Nguyen, Matthew O Wiens, J Mark Ansermino, Niranjan Kissoon, Elias Kumbakumba, Stephen Businge, Clare Komugisha, Mellon Tayebwa, Jerome Kabakyenga, Nathan Kenya Mugisha, Rishikesan Kamaleswaran
{"title":"2024 年儿科败血症挑战:预测乌干达疑似败血症患儿的院内死亡率。","authors":"Charly Huxford, Alireza Rafiei, Vuong Nguyen, Matthew O Wiens, J Mark Ansermino, Niranjan Kissoon, Elias Kumbakumba, Stephen Businge, Clare Komugisha, Mellon Tayebwa, Jerome Kabakyenga, Nathan Kenya Mugisha, Rishikesan Kamaleswaran","doi":"10.1097/PCC.0000000000003556","DOIUrl":null,"url":null,"abstract":"<p><p>The aim of this \"Technical Note\" is to inform the pediatric critical care data research community about the \"2024 Pediatric Sepsis Data Challenge.\" This competition aims to facilitate the development of open-source algorithms to predict in-hospital mortality in Ugandan children with sepsis. The challenge is to first develop an algorithm using a synthetic training dataset, which will then be scored according to standard diagnostic testing criteria, and then be evaluated against a nonsynthetic test dataset. The datasets originate from admissions to six hospitals in Uganda (2017-2020) and include 3837 children, 6 to 60 months old, who were confirmed or suspected to have a diagnosis of sepsis. The synthetic dataset was created from a random subset of the original data. The test validation dataset closely resembles the synthetic dataset. The challenge should generate an optimal model for predicting in-hospital mortality. Following external validation, this model could be used to improve the outcomes for children with proven or suspected sepsis in low- and middle-income settings.</p>","PeriodicalId":19760,"journal":{"name":"Pediatric Critical Care Medicine","volume":" ","pages":"1047-1050"},"PeriodicalIF":4.0000,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11534513/pdf/","citationCount":"0","resultStr":"{\"title\":\"The 2024 Pediatric Sepsis Challenge: Predicting In-Hospital Mortality in Children With Suspected Sepsis in Uganda.\",\"authors\":\"Charly Huxford, Alireza Rafiei, Vuong Nguyen, Matthew O Wiens, J Mark Ansermino, Niranjan Kissoon, Elias Kumbakumba, Stephen Businge, Clare Komugisha, Mellon Tayebwa, Jerome Kabakyenga, Nathan Kenya Mugisha, Rishikesan Kamaleswaran\",\"doi\":\"10.1097/PCC.0000000000003556\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>The aim of this \\\"Technical Note\\\" is to inform the pediatric critical care data research community about the \\\"2024 Pediatric Sepsis Data Challenge.\\\" This competition aims to facilitate the development of open-source algorithms to predict in-hospital mortality in Ugandan children with sepsis. The challenge is to first develop an algorithm using a synthetic training dataset, which will then be scored according to standard diagnostic testing criteria, and then be evaluated against a nonsynthetic test dataset. The datasets originate from admissions to six hospitals in Uganda (2017-2020) and include 3837 children, 6 to 60 months old, who were confirmed or suspected to have a diagnosis of sepsis. The synthetic dataset was created from a random subset of the original data. The test validation dataset closely resembles the synthetic dataset. The challenge should generate an optimal model for predicting in-hospital mortality. Following external validation, this model could be used to improve the outcomes for children with proven or suspected sepsis in low- and middle-income settings.</p>\",\"PeriodicalId\":19760,\"journal\":{\"name\":\"Pediatric Critical Care Medicine\",\"volume\":\" \",\"pages\":\"1047-1050\"},\"PeriodicalIF\":4.0000,\"publicationDate\":\"2024-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11534513/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Pediatric Critical Care Medicine\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1097/PCC.0000000000003556\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/6/21 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q1\",\"JCRName\":\"CRITICAL CARE MEDICINE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Pediatric Critical Care Medicine","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1097/PCC.0000000000003556","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/6/21 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"CRITICAL CARE MEDICINE","Score":null,"Total":0}
The 2024 Pediatric Sepsis Challenge: Predicting In-Hospital Mortality in Children With Suspected Sepsis in Uganda.
The aim of this "Technical Note" is to inform the pediatric critical care data research community about the "2024 Pediatric Sepsis Data Challenge." This competition aims to facilitate the development of open-source algorithms to predict in-hospital mortality in Ugandan children with sepsis. The challenge is to first develop an algorithm using a synthetic training dataset, which will then be scored according to standard diagnostic testing criteria, and then be evaluated against a nonsynthetic test dataset. The datasets originate from admissions to six hospitals in Uganda (2017-2020) and include 3837 children, 6 to 60 months old, who were confirmed or suspected to have a diagnosis of sepsis. The synthetic dataset was created from a random subset of the original data. The test validation dataset closely resembles the synthetic dataset. The challenge should generate an optimal model for predicting in-hospital mortality. Following external validation, this model could be used to improve the outcomes for children with proven or suspected sepsis in low- and middle-income settings.
期刊介绍:
Pediatric Critical Care Medicine is written for the entire critical care team: pediatricians, neonatologists, respiratory therapists, nurses, and others who deal with pediatric patients who are critically ill or injured. International in scope, with editorial board members and contributors from around the world, the Journal includes a full range of scientific content, including clinical articles, scientific investigations, solicited reviews, and abstracts from pediatric critical care meetings. Additionally, the Journal includes abstracts of selected articles published in Chinese, French, Italian, Japanese, Portuguese, and Spanish translations - making news of advances in the field available to pediatric and neonatal intensive care practitioners worldwide.