Derivation and validation of generalized sepsis-induced acute respiratory failure phenotypes among critically ill patients: a retrospective study.

IF 8.8 1区 医学 Q1 CRITICAL CARE MEDICINE
Tilendra Choudhary, Pulakesh Upadhyaya, Carolyn M Davis, Philip Yang, Simon Tallowin, Felipe A Lisboa, Seth A Schobel, Craig M Coopersmith, Eric A Elster, Timothy G Buchman, Christopher J Dente, Rishikesan Kamaleswaran
{"title":"Derivation and validation of generalized sepsis-induced acute respiratory failure phenotypes among critically ill patients: a retrospective study.","authors":"Tilendra Choudhary, Pulakesh Upadhyaya, Carolyn M Davis, Philip Yang, Simon Tallowin, Felipe A Lisboa, Seth A Schobel, Craig M Coopersmith, Eric A Elster, Timothy G Buchman, Christopher J Dente, Rishikesan Kamaleswaran","doi":"10.1186/s13054-024-05061-4","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Septic patients who develop acute respiratory failure (ARF) requiring mechanical ventilation represent a heterogenous subgroup of critically ill patients with widely variable clinical characteristics. Identifying distinct phenotypes of these patients may reveal insights about the broader heterogeneity in the clinical course of sepsis, considering multi-organ dynamics. We aimed to derive novel phenotypes of sepsis-induced ARF using observational clinical data and investigate the generalizability of the derived phenotypes.</p><p><strong>Methods: </strong>We performed a multi-center retrospective study of ICU patients with sepsis who required mechanical ventilation for ≥ 24 h. Data from two different high-volume academic hospital centers were used, where all phenotypes were derived in MICU of Hospital-I (N = 3225). The derived phenotypes were validated in MICU of Hospital-II (N = 848), SICU of Hospital-I (N = 1112), and SICU of Hospital-II (N = 465). Clinical data from 24 h preceding intubation was used to derive distinct phenotypes using an explainable machine learning-based clustering model interpreted by clinical experts.</p><p><strong>Results: </strong>Four distinct ARF phenotypes were identified: A (severe multi-organ dysfunction (MOD) with a high likelihood of kidney injury and heart failure), B (severe hypoxemic respiratory failure [median P/F = 123]), C (mild hypoxia [median P/F = 240]), and D (severe MOD with a high likelihood of hepatic injury, coagulopathy, and lactic acidosis). Patients in each phenotype showed differences in clinical course and mortality rates despite similarities in demographics and admission co-morbidities. The phenotypes were reproduced in external validation utilizing the MICU of Hospital-II and SICUs from Hospital-I and -II. Kaplan-Meier analysis showed significant difference in 28-day mortality across the phenotypes (p < 0.01) and consistent across MICU and SICU of both Hospital-I and -II. The phenotypes demonstrated differences in treatment effects associated with high positive end-expiratory pressure (PEEP) strategy.</p><p><strong>Conclusion: </strong>The phenotypes demonstrated unique patterns of organ injury and differences in clinical outcomes, which may help inform future research and clinical trial design for tailored management strategies.</p>","PeriodicalId":10811,"journal":{"name":"Critical Care","volume":"28 1","pages":"321"},"PeriodicalIF":8.8000,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11445942/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Critical Care","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1186/s13054-024-05061-4","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CRITICAL CARE MEDICINE","Score":null,"Total":0}
引用次数: 0

Abstract

Background: Septic patients who develop acute respiratory failure (ARF) requiring mechanical ventilation represent a heterogenous subgroup of critically ill patients with widely variable clinical characteristics. Identifying distinct phenotypes of these patients may reveal insights about the broader heterogeneity in the clinical course of sepsis, considering multi-organ dynamics. We aimed to derive novel phenotypes of sepsis-induced ARF using observational clinical data and investigate the generalizability of the derived phenotypes.

Methods: We performed a multi-center retrospective study of ICU patients with sepsis who required mechanical ventilation for ≥ 24 h. Data from two different high-volume academic hospital centers were used, where all phenotypes were derived in MICU of Hospital-I (N = 3225). The derived phenotypes were validated in MICU of Hospital-II (N = 848), SICU of Hospital-I (N = 1112), and SICU of Hospital-II (N = 465). Clinical data from 24 h preceding intubation was used to derive distinct phenotypes using an explainable machine learning-based clustering model interpreted by clinical experts.

Results: Four distinct ARF phenotypes were identified: A (severe multi-organ dysfunction (MOD) with a high likelihood of kidney injury and heart failure), B (severe hypoxemic respiratory failure [median P/F = 123]), C (mild hypoxia [median P/F = 240]), and D (severe MOD with a high likelihood of hepatic injury, coagulopathy, and lactic acidosis). Patients in each phenotype showed differences in clinical course and mortality rates despite similarities in demographics and admission co-morbidities. The phenotypes were reproduced in external validation utilizing the MICU of Hospital-II and SICUs from Hospital-I and -II. Kaplan-Meier analysis showed significant difference in 28-day mortality across the phenotypes (p < 0.01) and consistent across MICU and SICU of both Hospital-I and -II. The phenotypes demonstrated differences in treatment effects associated with high positive end-expiratory pressure (PEEP) strategy.

Conclusion: The phenotypes demonstrated unique patterns of organ injury and differences in clinical outcomes, which may help inform future research and clinical trial design for tailored management strategies.

重症患者中由败血症诱发的急性呼吸衰竭表型的推导和验证:一项回顾性研究。
背景:出现需要机械通气的急性呼吸衰竭(ARF)的败血症患者是重症患者中的一个异质性亚群,其临床特征千差万别。考虑到多器官的动态变化,识别这些患者的不同表型可能会揭示脓毒症临床过程中更广泛的异质性。我们的目的是利用临床观察数据得出脓毒症诱发 ARF 的新表型,并研究得出的表型的可推广性:我们对需要机械通气≥24小时的ICU脓毒症患者进行了一项多中心回顾性研究,数据来自两个不同的高容量学术医院中心,其中所有表型均来自医院I的MICU(N = 3225)。得出的表型在第二医院的 MICU(848 人)、第一医院的 SICU(1112 人)和第二医院的 SICU(465 人)中进行了验证。插管前 24 小时的临床数据被用来通过临床专家解释的基于机器学习的可解释聚类模型得出不同的表型:结果:确定了四种不同的 ARF 表型:A(严重多器官功能障碍(MOD),极有可能出现肾损伤和心力衰竭)、B(严重缺氧性呼吸衰竭[中位 P/F = 123])、C(轻度缺氧[中位 P/F = 240])和 D(严重 MOD,极有可能出现肝损伤、凝血病和乳酸酸中毒)。尽管人口统计学和入院并发症相似,但每种表型的患者在临床病程和死亡率上都存在差异。在外部验证中,利用第二医院的 MICU 以及第一和第二医院的 SICU 对表型进行了重现。Kaplan-Meier 分析表明,不同表型的患者 28 天死亡率存在显著差异(p 结论:表型显示了独特的器官功能模式:表型显示了器官损伤的独特模式和临床结果的差异,这可能有助于为未来的研究和临床试验设计提供信息,以制定量身定制的管理策略。
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 求助全文
来源期刊
Critical Care
Critical Care 医学-危重病医学
CiteScore
20.60
自引率
3.30%
发文量
348
审稿时长
1.5 months
期刊介绍: Critical Care is an esteemed international medical journal that undergoes a rigorous peer-review process to maintain its high quality standards. Its primary objective is to enhance the healthcare services offered to critically ill patients. To achieve this, the journal focuses on gathering, exchanging, disseminating, and endorsing evidence-based information that is highly relevant to intensivists. By doing so, Critical Care seeks to provide a thorough and inclusive examination of the intensive care field.
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信