Positive end-expiratory pressure selection by comprehensively considering clinical measurements and patient characteristics may improve ICU outcome in ARDS patients: an observational study

Positive end-expiratory pressure selection by comprehensively considering clinical measurements and patient characteristics may improve ICU outcome in ARDS patients: an observational study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Positive end-expiratory pressure selection by comprehensively considering clinical measurements and patient characteristics may improve ICU outcome in ARDS patients: an observational study Huiqing Ge, Qing Pan, Yuhan Zhou, Yilin Qian, Zhongheng Zhang, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1322548/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: It remains controversial as how to set positive end-expiratory pressure (PEEP) for acute respiratory distress syndrome (ARDS) patients. This study aims to provide suggestions to the clinicians in selecting PEEP for ARDS patients receiving invasive mechanical ventilation based on artificial intelligence (AI). Methods: Invasively ventilated ARDS patients in MIMIC-IV and eICU databases were enrolled in the observational cohort study. An AI model trained by awarding survival for suggesting optimal PEEP was developed and tested on the MIMIC-IV database and externally validated on the eICU database. Three subgroups were defined in which the PEEP grades set by the AI model are lower, equal, and higher than that set by the clinicians (denoted as , , and , respectively). Intensive care unit (ICU) mortality and 28-day ventilation-free days are the primary and secondary outcomes. Results: 6839 (MIMIC-IV) and 2117 (eICU) ARDS admissions were included in the study. The ICU mortalities are 10.8% and 8.6% in the subgroup in the MIMIC-IV and eICU databases, respectively, and become higher in the and subgroups (MIMIC-IV: 25.6% and 23.7%, eICU: 26.9% and 27.6%). An explainable analysis reflects that the clinicians’ PEEP setting relates more to the oxygenation, respiratory mechanics, and ventilatory settings, while the RL model also pays attention to the more comprehensive parameters concerning patient characteristics such as Sequential Organ Failure Assessment (SOFA) and age. Conclusions: AI-based PEEP selection tends to consider clinical measurements and patient characteristics comprehensively, and is promising to improve the ICU outcomes for ARDS patients. positive end-expiratory pressure reinforcement learning mechanical ventilation intensive care unit Full Text Additional Declarations No competing interests reported. Supplementary Files ESM20220203CC.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1322548","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":81413064,"identity":"c6fa7020-069e-4838-8608-5a8af343c664","order_by":0,"name":"Huiqing Ge","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAu0lEQVRIiWNgGAWjYFACHgaGDwxsIJYB8VoYZ5CshZkHwiJSi7x778HbNn/4EhvYm7dJMNTcIazF8My5ZOscHrbEBp5jZRIMx54RoWVGjpl0jgRQi0SOmQRjw2EitMx/YyZtYQDUIv+GSC3yEjxm0gwJIFt4iNRiwJNjbNlzgM24jSet2CLhGDG2tJ8xvPHjzzHZfvbDG298qCHGlgMMDBIMDMcgkZlAWAPQlgawlhpi1I6CUTAKRsFIBQDP4jMTIdVKcQAAAABJRU5ErkJggg==","orcid":"","institution":"Sir Run Run Shaw Hospital of Zhejiang University","correspondingAuthor":true,"prefix":"","firstName":"Huiqing","middleName":"","lastName":"Ge","suffix":""},{"id":81413056,"identity":"b9e3752d-7381-4697-9d7c-e9bcf3150e6b","order_by":1,"name":"Qing Pan","email":"","orcid":"","institution":"Zhejiang University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Qing","middleName":"","lastName":"Pan","suffix":""},{"id":81413057,"identity":"6a22ab75-c13e-4d94-8aaa-04d1ede25d3b","order_by":2,"name":"Yuhan Zhou","email":"","orcid":"","institution":"Zhejiang University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Yuhan","middleName":"","lastName":"Zhou","suffix":""},{"id":81413058,"identity":"a24639b0-4f4e-4fb4-a502-d4a0ea19d4c5","order_by":3,"name":"Yilin Qian","email":"","orcid":"","institution":"Zhejiang University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Yilin","middleName":"","lastName":"Qian","suffix":""},{"id":81413059,"identity":"0585677d-f350-483f-8481-bab42d283b5e","order_by":4,"name":"Zhongheng Zhang","email":"","orcid":"","institution":"Sir Run Run Shaw Hospital of Zhejiang University","correspondingAuthor":false,"prefix":"","firstName":"Zhongheng","middleName":"","lastName":"Zhang","suffix":""},{"id":81413060,"identity":"870da91c-7b6b-4b13-80b1-fbd560b9ee90","order_by":5,"name":"Luping Fang","email":"","orcid":"","institution":"Zhejiang University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Luping","middleName":"","lastName":"Fang","suffix":""},{"id":81413061,"identity":"705bc9da-869f-410d-999d-24822232ab87","order_by":6,"name":"Gangmin Ning","email":"","orcid":"","institution":"Zhejiang University","correspondingAuthor":false,"prefix":"","firstName":"Gangmin","middleName":"","lastName":"Ning","suffix":""},{"id":81413062,"identity":"f855190c-5a7b-469b-a1b3-563490fa0e12","order_by":7,"name":"Leo M. Heunks","email":"","orcid":"","institution":"Erasmus Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Leo","middleName":"M.","lastName":"Heunks","suffix":""}],"badges":[],"createdAt":"2022-02-03 03:29:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1322548/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1322548/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":18004823,"identity":"680a5848-3574-4573-b0bc-9b8272753ad0","added_by":"auto","created_at":"2022-02-07 17:42:59","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":575450,"visible":true,"origin":"","legend":"","description":"","filename":"paper20220128CC.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1322548/v1_covered.pdf"},{"id":18004822,"identity":"505c7536-18ec-44d6-9d11-e5b4db628d98","added_by":"auto","created_at":"2022-02-07 17:42:51","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":357585,"visible":true,"origin":"","legend":"","description":"","filename":"ESM20220203CC.docx","url":"https://assets-eu.researchsquare.com/files/rs-1322548/v1/5db093e1e131026e0c7a5de4.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Positive end-expiratory pressure selection by comprehensively considering clinical measurements and patient characteristics may improve ICU outcome in ARDS patients: an observational study","fulltext":[{"header":"Full Text","content":"This preprint is available for \u003ca href='/article/rs-1322548/latest.pdf' target='_blank'\u003edownload as a PDF\u003c/a\u003e."}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"positive end-expiratory pressure, reinforcement learning, mechanical ventilation, intensive care unit","lastPublishedDoi":"10.21203/rs.3.rs-1322548/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1322548/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e It remains controversial as how to set positive end-expiratory pressure (PEEP) for acute respiratory distress syndrome (ARDS) patients. This study aims to provide suggestions to the clinicians in selecting PEEP for ARDS patients receiving invasive mechanical ventilation based on artificial intelligence (AI).\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e Invasively ventilated ARDS patients in MIMIC-IV and eICU databases were enrolled in the observational cohort study. An AI model trained by awarding survival for suggesting optimal PEEP was developed and tested on the MIMIC-IV database and externally validated on the eICU database. Three subgroups were defined in which the PEEP grades set by the AI model are lower, equal, and higher than that set by the clinicians (denoted as , , and , respectively). Intensive care unit (ICU) mortality and 28-day ventilation-free days are the primary and secondary outcomes.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e 6839 (MIMIC-IV) and 2117 (eICU) ARDS admissions were included in the study. The ICU mortalities are 10.8% and 8.6% in the \u0026nbsp;subgroup in the MIMIC-IV and eICU databases, respectively, and become higher in the \u0026nbsp;and \u0026nbsp;subgroups (MIMIC-IV: 25.6% and 23.7%, eICU: 26.9% and 27.6%). An explainable analysis reflects that the clinicians’ PEEP setting relates more to the oxygenation, respiratory mechanics, and ventilatory settings, while the RL model also pays attention to the more comprehensive parameters concerning patient characteristics such as Sequential Organ Failure Assessment (SOFA) and age.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e AI-based PEEP selection tends to consider clinical measurements and patient characteristics comprehensively, and is promising to improve the ICU outcomes for ARDS patients.\u0026nbsp;\u003c/p\u003e","manuscriptTitle":"Positive end-expiratory pressure selection by comprehensively considering clinical measurements and patient characteristics may improve ICU outcome in ARDS patients: an observational study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-02-07 17:42:49","doi":"10.21203/rs.3.rs-1322548/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fd5fcc64-583a-44a9-9d31-e77c8f6bf824","owner":[],"postedDate":"February 7th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2022-02-07T17:42:51+00:00","versionOfRecord":[],"versionCreatedAt":"2022-02-07 17:42:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1322548","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1322548","identity":"rs-1322548","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}