Lessons learned from big data (APRICOT, NECTARINE, PeDI)

IF 4.7 3区 医学 Q1 ANESTHESIOLOGY Best Practice & Research-Clinical Anaesthesiology Pub Date : 2024-06-01 DOI:10.1016/j.bpa.2024.04.006
Nicola Disma , Walid Habre , Francis Veyckemans
{"title":"Lessons learned from big data (APRICOT, NECTARINE, PeDI)","authors":"Nicola Disma ,&nbsp;Walid Habre ,&nbsp;Francis Veyckemans","doi":"10.1016/j.bpa.2024.04.006","DOIUrl":null,"url":null,"abstract":"<div><p>Big data in paediatric anaesthesia allows the evaluation of morbidity and mortality of anaesthesia in a large population, but also the identification of rare critical events and of their causes. This is a major step to focus education and design clinical guidelines. Moreover, they can help trying to determine normative data in a population with a wide range of ages and body weights. The example of blood pressure under anaesthesia will be detailed. Big data studies should encourage every department of anaesthesia to collect its own data and to benchmark its performance by comparison with published data. The data collection processes are also an opportunity to build collaborative research networks and help researchers to complete multicentric studies. Up to recently, big data studies were only performed in well developed countries. Fortunately, big data collections have started in some low and middle income countries and truly international studies are ongoing.</p></div>","PeriodicalId":48541,"journal":{"name":"Best Practice & Research-Clinical Anaesthesiology","volume":"38 2","pages":"Pages 111-117"},"PeriodicalIF":4.7000,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Best Practice & Research-Clinical Anaesthesiology","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1521689624000235","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ANESTHESIOLOGY","Score":null,"Total":0}
引用次数: 0

Abstract

Big data in paediatric anaesthesia allows the evaluation of morbidity and mortality of anaesthesia in a large population, but also the identification of rare critical events and of their causes. This is a major step to focus education and design clinical guidelines. Moreover, they can help trying to determine normative data in a population with a wide range of ages and body weights. The example of blood pressure under anaesthesia will be detailed. Big data studies should encourage every department of anaesthesia to collect its own data and to benchmark its performance by comparison with published data. The data collection processes are also an opportunity to build collaborative research networks and help researchers to complete multicentric studies. Up to recently, big data studies were only performed in well developed countries. Fortunately, big data collections have started in some low and middle income countries and truly international studies are ongoing.

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
从大数据中汲取的经验教训(亚太地区数据中心、NECTARINE、PeDI)
儿科麻醉大数据不仅可以评估大量人群的麻醉发病率和死亡率,还可以识别罕见的危急事件及其原因。这是集中教育和设计临床指南的重要一步。此外,这些数据还有助于确定不同年龄和体重人群的标准数据。我们将以麻醉状态下的血压为例进行详细说明。大数据研究应鼓励每个麻醉部门收集自己的数据,并通过与已公布的数据进行比较来确定其绩效基准。数据收集过程也是建立合作研究网络的机会,有助于研究人员完成多中心研究。迄今为止,大数据研究只在发达的国家进行。幸运的是,一些中低收入国家已经开始收集大数据,真正的国际研究也正在进行中。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
37
审稿时长
36 days
期刊最新文献
Editorial Board Preoperative fasting in children. The evolution of recommendations and guidelines, and the underlying evidence What's new in pediatric critical care? Update on perioperative fluids Lessons learned from big data (APRICOT, NECTARINE, PeDI)
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1