从静脉体外膜氧合的机器学习中识别与神经系统结果相关的因素。

IF 4.6 2区 医学 Q1 RESPIRATORY SYSTEM Lung Pub Date : 2024-08-01 Epub Date: 2024-05-30 DOI:10.1007/s00408-024-00708-z
Albert Leng, Benjamin Shou, Olivia Liu, Preetham Bachina, Andrew Kalra, Errol L Bush, Glenn J R Whitman, Sung-Min Cho
{"title":"从静脉体外膜氧合的机器学习中识别与神经系统结果相关的因素。","authors":"Albert Leng, Benjamin Shou, Olivia Liu, Preetham Bachina, Andrew Kalra, Errol L Bush, Glenn J R Whitman, Sung-Min Cho","doi":"10.1007/s00408-024-00708-z","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Neurological complications are common in patients receiving veno-venous extracorporeal membrane oxygenation (VV-ECMO) support. We used machine learning (ML) algorithms to identify predictors for neurological outcomes for these patients.</p><p><strong>Methods: </strong>All demographic, clinical, and circuit-related variables were extracted for adults with VV-ECMO support at a tertiary care center from 2016 to 2022. The primary outcome was good neurological outcome (GNO) at discharge defined as a modified Rankin Scale of 0-3.</p><p><strong>Results: </strong>Of 99 total VV-ECMO patients (median age = 48 years; 65% male), 37% had a GNO. The best performing ML model achieved an area under the receiver operating characteristic curve of 0.87. Feature importance analysis identified down-trending gas/sweep/blender flow, FiO<sub>2</sub>, and pump speed as the most salient features for predicting GNO.</p><p><strong>Conclusion: </strong>Utilizing pre- as well as post-initiation variables, ML identified on-ECMO physiologic and pulmonary conditions that best predicted neurological outcomes.</p>","PeriodicalId":18163,"journal":{"name":"Lung","volume":null,"pages":null},"PeriodicalIF":4.6000,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11417431/pdf/","citationCount":"0","resultStr":"{\"title\":\"Machine Learning from Veno-Venous Extracorporeal Membrane Oxygenation Identifies Factors Associated with Neurological Outcomes.\",\"authors\":\"Albert Leng, Benjamin Shou, Olivia Liu, Preetham Bachina, Andrew Kalra, Errol L Bush, Glenn J R Whitman, Sung-Min Cho\",\"doi\":\"10.1007/s00408-024-00708-z\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Neurological complications are common in patients receiving veno-venous extracorporeal membrane oxygenation (VV-ECMO) support. We used machine learning (ML) algorithms to identify predictors for neurological outcomes for these patients.</p><p><strong>Methods: </strong>All demographic, clinical, and circuit-related variables were extracted for adults with VV-ECMO support at a tertiary care center from 2016 to 2022. The primary outcome was good neurological outcome (GNO) at discharge defined as a modified Rankin Scale of 0-3.</p><p><strong>Results: </strong>Of 99 total VV-ECMO patients (median age = 48 years; 65% male), 37% had a GNO. The best performing ML model achieved an area under the receiver operating characteristic curve of 0.87. Feature importance analysis identified down-trending gas/sweep/blender flow, FiO<sub>2</sub>, and pump speed as the most salient features for predicting GNO.</p><p><strong>Conclusion: </strong>Utilizing pre- as well as post-initiation variables, ML identified on-ECMO physiologic and pulmonary conditions that best predicted neurological outcomes.</p>\",\"PeriodicalId\":18163,\"journal\":{\"name\":\"Lung\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.6000,\"publicationDate\":\"2024-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11417431/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Lung\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1007/s00408-024-00708-z\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/5/30 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q1\",\"JCRName\":\"RESPIRATORY SYSTEM\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Lung","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s00408-024-00708-z","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/5/30 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"RESPIRATORY SYSTEM","Score":null,"Total":0}
引用次数: 0

摘要

背景:在接受静脉-静脉体外膜氧合(VV-ECMO)支持的患者中,神经系统并发症很常见。我们使用机器学习(ML)算法来确定这些患者神经系统结果的预测因素:提取了2016年至2022年在一家三级医疗中心接受VV-ECMO支持的成人的所有人口统计学、临床和电路相关变量。主要结果是出院时的良好神经功能预后(GNO),定义为改良Rankin量表0-3分:在99名VV-ECMO患者(中位年龄=48岁;65%为男性)中,37%的患者出现了GNO。表现最好的 ML 模型的接收者操作特征曲线下面积为 0.87。特征重要性分析表明,气体/扫气/吹气流量、FiO2 和泵速的下降趋势是预测 GNO 的最显著特征:利用启动前和启动后的变量,ML 确定了最能预测神经系统预后的 ECMO 生理和肺部条件。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

摘要图片

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
Machine Learning from Veno-Venous Extracorporeal Membrane Oxygenation Identifies Factors Associated with Neurological Outcomes.

Background: Neurological complications are common in patients receiving veno-venous extracorporeal membrane oxygenation (VV-ECMO) support. We used machine learning (ML) algorithms to identify predictors for neurological outcomes for these patients.

Methods: All demographic, clinical, and circuit-related variables were extracted for adults with VV-ECMO support at a tertiary care center from 2016 to 2022. The primary outcome was good neurological outcome (GNO) at discharge defined as a modified Rankin Scale of 0-3.

Results: Of 99 total VV-ECMO patients (median age = 48 years; 65% male), 37% had a GNO. The best performing ML model achieved an area under the receiver operating characteristic curve of 0.87. Feature importance analysis identified down-trending gas/sweep/blender flow, FiO2, and pump speed as the most salient features for predicting GNO.

Conclusion: Utilizing pre- as well as post-initiation variables, ML identified on-ECMO physiologic and pulmonary conditions that best predicted neurological outcomes.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Lung
Lung 医学-呼吸系统
CiteScore
9.10
自引率
10.00%
发文量
95
审稿时长
6-12 weeks
期刊介绍: Lung publishes original articles, reviews and editorials on all aspects of the healthy and diseased lungs, of the airways, and of breathing. Epidemiological, clinical, pathophysiological, biochemical, and pharmacological studies fall within the scope of the journal. Case reports, short communications and technical notes can be accepted if they are of particular interest.
期刊最新文献
Factors Associated with Corticosteroid Adherence in Sarcoidosis. Percent Predicted vs. Absolute Six-Minute Walk Distance as Predictors of Lung Transplant-Free Survival in Fibrosing Interstitial Lung Diseases. Collagen-V and K-α-1 Tubulin Antibodies as Potential Markers of Unsuspected GERD-Related Lung Damage: Insights from a Cross-Sectional Analysis. IgG Concentrations Distinguish People with Cystic Fibrosis and Mycobacterium abscessus. Impact of Functional Status at the Time of Transplant on Short-Term Pediatric Lung Transplant Outcomes in the USA.
×
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