Comparing High-Flow Nasal Cannula and Non-Invasive Ventilation in Critical Care: Insights from Deep Counterfactual Inference

Comparing High-Flow Nasal Cannula and Non-Invasive Ventilation in Critical Care: Insights from Deep Counterfactual Inference | 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 Article Comparing High-Flow Nasal Cannula and Non-Invasive Ventilation in Critical Care: Insights from Deep Counterfactual Inference Xiaolei Lu, Michael Miller, Alex K. Pearce, Jonathan Y. Lam, Aaron E. Boussina, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7230866/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Randomized trials comparing high-flow nasal cannula (HFNC) and non-invasive positive pressure ventilation (NIV) for acute respiratory failure (ARF) offer population-level guidance but often fail to capture individual variability in treatment response. In this retrospective study, we identified intensive care units (ICU) patients at risk of invasive mechanical ventilation (IMV) using a previously published risk prediction model. Patients who first received HFNC or NIV after crossing the high-risk threshold formed the early treatment cohort. We developed a deep counterfactual model that integrates representation learning, conditional normalizing flows, and confounder adjustment to estimate individualized treatment effects (ITEs) between HFNC and NIV. Treatment concordance, defined as alignment between the model’s recommendation and the treatment actually administered, was assessed using multivariate logistic regression. At UC San Diego Health (UCSD), concordant treatment was associated with significantly reduced odds of IMV (odds ratio [OR] = 0.661 for NIV; 0.677 for HFNC) and mortality or hospice discharge (OR = 0.679 for NIV; 0.749 for HFNC). At UC Irvine Health (UCI), concordant treatment was also linked to improved outcomes, particularly for mortality or hospice discharge (OR = 0.092 for NIV; 0.088 for HFNC). These findings highlight the value of individualized, model-guided respiratory support strategies in improving outcomes for critically ill patients. Health sciences/Diseases Health sciences/Health care Health sciences/Medical research Health sciences/Risk factors Individualized Treatment Counterfactual Modeling Invasive Mechanical Ventilation High-Flow Nasal Cannula Noninvasive Ventilation Full Text Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterial.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 06 Sep, 2025 Reviews received at journal 06 Sep, 2025 Reviews received at journal 24 Aug, 2025 Reviewers agreed at journal 06 Aug, 2025 Reviewers agreed at journal 04 Aug, 2025 Reviewers invited by journal 30 Jul, 2025 Editor assigned by journal 29 Jul, 2025 Submission checks completed at journal 29 Jul, 2025 First submitted to journal 28 Jul, 2025 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. 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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-7230866","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":495518821,"identity":"83c33217-4576-4fa6-a48c-0f02cedd91d9","order_by":0,"name":"Xiaolei Lu","email":"","orcid":"","institution":"University of California, San Diego","correspondingAuthor":false,"prefix":"","firstName":"Xiaolei","middleName":"","lastName":"Lu","suffix":""},{"id":495518822,"identity":"363ca273-1876-4600-8756-f39b0026e948","order_by":1,"name":"Michael Miller","email":"","orcid":"","institution":"University of California, San Diego","correspondingAuthor":false,"prefix":"","firstName":"Michael","middleName":"","lastName":"Miller","suffix":""},{"id":495518823,"identity":"e58fc4ff-4cf3-4305-b8dd-eb1bad60f655","order_by":2,"name":"Alex K. 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In this retrospective study, we identified intensive care units (ICU) patients at risk of invasive mechanical ventilation (IMV) using a previously published risk prediction model. Patients who first received HFNC or NIV after crossing the high-risk threshold formed the early treatment cohort. We developed a deep counterfactual model that integrates representation learning, conditional normalizing flows, and confounder adjustment to estimate individualized treatment effects (ITEs) between HFNC and NIV. Treatment concordance, defined as alignment between the model\u0026rsquo;s recommendation and the treatment actually administered, was assessed using multivariate logistic regression. At UC San Diego Health (UCSD), concordant treatment was associated with significantly reduced odds of IMV (odds ratio [OR]\u0026thinsp;=\u0026thinsp;0.661 for NIV; 0.677 for HFNC) and mortality or hospice discharge (OR\u0026thinsp;=\u0026thinsp;0.679 for NIV; 0.749 for HFNC). At UC Irvine Health (UCI), concordant treatment was also linked to improved outcomes, particularly for mortality or hospice discharge (OR\u0026thinsp;=\u0026thinsp;0.092 for NIV; 0.088 for HFNC). These findings highlight the value of individualized, model-guided respiratory support strategies in improving outcomes for critically ill patients.\u003c/p\u003e","manuscriptTitle":"Comparing High-Flow Nasal Cannula and Non-Invasive Ventilation in Critical Care: Insights from Deep Counterfactual Inference","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-05 18:15:52","doi":"10.21203/rs.3.rs-7230866/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-06T12:54:19+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-06T09:24:56+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-24T19:03:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"297448365478559798368305022942130744587","date":"2025-08-06T17:24:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"209557334427560600285052680033038765255","date":"2025-08-04T15:48:58+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-30T21:05:24+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-29T13:57:30+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-29T07:26:54+00:00","index":"","fulltext":""},{"type":"submitted","content":"npj Health Systems","date":"2025-07-28T07:18:20+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"npj-health-systems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [npj Health Systems](https://www.nature.com/npjhealthsyst/)","snPcode":"44401","submissionUrl":"https://submission.springernature.com/new-submission/44401/3","title":"npj Health Systems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"e9617da7-4304-48d2-a7e3-0ceccdff5d30","owner":[],"postedDate":"August 5th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":52624798,"name":"Health sciences/Diseases"},{"id":52624799,"name":"Health sciences/Health care"},{"id":52624800,"name":"Health sciences/Medical research"},{"id":52624801,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2025-10-21T12:23:37+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-05 18:15:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7230866","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7230866","identity":"rs-7230866","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}