{"paper_id":"0dc5e13d-0060-4ab2-8bef-da0d047d8ccc","body_text":"Causal machine learning for assessing the effectiveness of off-label use of amiodarone in new-onset atrial fibrillation | 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 Causal machine learning for assessing the effectiveness of off-label use of amiodarone in new-onset atrial fibrillation Stefan Feuerriegel, Simon Schallmoser, Jonas Schweisthal, Alexander von Ehr, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6966486/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 Off-label drug use, i.e., uses of a drug that differ from what regulatory authorities have approved, is common, occurring overall in up to 36% of prescriptions. Yet, the effectiveness across different patient subgroups is often poorly understood. In this study, we demonstrate how one can use causal machine learning (ML) together with real-world data to identify which patient groups are most likely to benefit from off-label use. Specifically, we assessed the effectiveness of off-label use of amiodarone in patients with new-onset atrial fibrillation (NOAF). NOAF can often lead to hemodynamic instability and rapid ventricular response, so that hemodynamic stability should be restored. We developed a causal ML model to predict individualized treatment effects (ITEs) of off-label amiodarone use on the probability of returning to hemodynamic stability. We used real-world data from the U.S. to develop the causal ML model and externally evaluated that model on real-world data from the Netherlands. Our predicted ITEs show that 44.8% (95% confidence interval [CI]: 38.4% to 51.0%) of patients benefit from off-label use of amiodarone with large heterogeneity: amiodarone is predicted to increase the probability of restoring hemodynamic stability by a mean of 0.5 percentage points (pp), with an interquartile range (IQR) of −1.1 pp to 1.0 pp, in the external dataset from the Netherlands. Using these ITEs, we defined a personalized treatment rule, which could increase the number of patients achieving hemodynamic stability by 4.4% (95% CI: 1.0% to 7.8%) compared to current practice. Additionally, we studied which biomarkers are predictive of treatment effect heterogeneity and found that patients with higher blood pressure may benefit most from off-label use of amiodarone. Altogether, our study shows the potential of causal ML together with real-world data in identifying patients who benefit from off-label drug use. Health sciences/Diseases/Cardiovascular diseases/Arrhythmias/Atrial fibrillation Health sciences/Medical research/Outcomes research machine learning electronic health records off-label use new-onset atrial fibrillation intensive care unit heterogeneous treatment effects Full Text Additional Declarations There is NO Competing Interest. 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-6966486\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Article\",\"associatedPublications\":[],\"authors\":[{\"id\":480725911,\"identity\":\"abbdf3e6-2f9a-42ea-ba24-8c28ce3b20a5\",\"order_by\":0,\"name\":\"Stefan Feuerriegel\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA10lEQVRIiWNgGAWjYBAC9gYwJQHEzI0PHoDYBwho4TkA18LYbJBAghYQYGyTIE4Le4/h48o9FnLy7o1tFYk5hxn4jjcQ0MJzxtjwzDMJIHGw7UbitsMMkmcIWGMvkWMm2XBAInHjjESIFoMbCQRskX9j/hOopX7j/IdtBWAt9x8Q0CLBY8YI1JIgL8HYxgCxBb8OoF/SikEOM9zAk9gskbgtnUfyDCGHsR/e+LHhQJ28fPvhgx8+brOW4zt+gIA1MGAAVchDpHogkG8gXu0oGAWjYBSMMAAAVLBIaoIrG7AAAAAASUVORK5CYII=\",\"orcid\":\"https://orcid.org/0000-0001-7856-8729\",\"institution\":\"LMU Munich\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Stefan\",\"middleName\":\"\",\"lastName\":\"Feuerriegel\",\"suffix\":\"\"},{\"id\":480725912,\"identity\":\"3c07f02b-d7a5-4046-979e-862eebf9e7ea\",\"order_by\":1,\"name\":\"Simon Schallmoser\",\"email\":\"\",\"orcid\":\"https://orcid.org/0000-0003-4076-0584\",\"institution\":\"LMU Munich\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Simon\",\"middleName\":\"\",\"lastName\":\"Schallmoser\",\"suffix\":\"\"},{\"id\":480725913,\"identity\":\"21d50f2f-3541-4a9e-94e3-242ce93f6988\",\"order_by\":2,\"name\":\"Jonas Schweisthal\",\"email\":\"\",\"orcid\":\"https://orcid.org/0000-0003-3725-3821\",\"institution\":\"LMU Munich\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Jonas\",\"middleName\":\"\",\"lastName\":\"Schweisthal\",\"suffix\":\"\"},{\"id\":480725914,\"identity\":\"67f1a099-a190-476c-9ca6-73a18aaf50ea\",\"order_by\":3,\"name\":\"Alexander von Ehr\",\"email\":\"\",\"orcid\":\"https://orcid.org/0000-0003-0849-321X\",\"institution\":\"University of Freiburg\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Alexander\",\"middleName\":\"\",\"lastName\":\"von Ehr\",\"suffix\":\"\"},{\"id\":480725915,\"identity\":\"bc365cc1-4099-4a34-99f1-1b864a225bb7\",\"order_by\":4,\"name\":\"Hamid Ghanbari\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"University of Michigan\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Hamid\",\"middleName\":\"\",\"lastName\":\"Ghanbari\",\"suffix\":\"\"},{\"id\":480725916,\"identity\":\"8ff8d3e6-9132-4ce2-8266-b58305aa877f\",\"order_by\":5,\"name\":\"Fridtjof Schiefenhövel\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Technical University of Munich\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Fridtjof\",\"middleName\":\"\",\"lastName\":\"Schiefenhövel\",\"suffix\":\"\"},{\"id\":480725917,\"identity\":\"411de517-0bf2-4271-aa94-542092619b15\",\"order_by\":6,\"name\":\"Thomas Valley\",\"email\":\"\",\"orcid\":\"https://orcid.org/0000-0002-5766-4970\",\"institution\":\"University of Michigan\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Thomas\",\"middleName\":\"\",\"lastName\":\"Valley\",\"suffix\":\"\"},{\"id\":480725918,\"identity\":\"f2a9cd15-19cc-463d-b621-46ce3c2542ed\",\"order_by\":7,\"name\":\"Jenna Wiens\",\"email\":\"\",\"orcid\":\"https://orcid.org/0000-0002-1057-7722\",\"institution\":\"University of Michigan\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Jenna\",\"middleName\":\"\",\"lastName\":\"Wiens\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2025-06-24 13:55:29\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-6966486/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-6966486/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":91437896,\"identity\":\"d1054f96-2551-42c8-b6e3-e99d90503a81\",\"added_by\":\"auto\",\"created_at\":\"2025-09-16 13:33:39\",\"extension\":\"pdf\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":2354774,\"visible\":true,\"origin\":\"\",\"legend\":\"Article File\",\"description\":\"\",\"filename\":\"AFTreatmentPolicy.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6966486/v1_covered_9a09cc1a-13aa-4c6b-99da-030a9b6928ec.pdf\"}],\"financialInterests\":\"There is \\u003cb\\u003eNO\\u003c/b\\u003e Competing Interest.\",\"formattedTitle\":\"Causal machine learning for assessing the effectiveness of off-label use of amiodarone in new-onset atrial fibrillation\",\"fulltext\":[],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":false,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":true,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":true,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":true,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"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\":\"machine learning, electronic health records, off-label use, new-onset atrial fibrillation, intensive care unit, heterogeneous treatment effects\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-6966486/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-6966486/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"Off-label drug use, i.e., uses of a drug that differ from what regulatory authorities have approved, is common, occurring overall in up to 36% of prescriptions. Yet, the effectiveness across different patient subgroups is often poorly understood. In this study, we demonstrate how one can use causal machine learning (ML) together with real-world data to identify which patient groups are most likely to benefit from off-label use. Specifically, we assessed the effectiveness of off-label use of amiodarone in patients with new-onset atrial fibrillation (NOAF). NOAF can often lead to hemodynamic instability and rapid ventricular response, so that hemodynamic stability should be restored. We developed a causal ML model to predict individualized treatment effects (ITEs) of off-label amiodarone use on the probability of returning to hemodynamic stability. We used real-world data from the U.S. to develop the causal ML model and externally evaluated that model on real-world data from the Netherlands. Our predicted ITEs show that 44.8% (95% confidence interval [CI]: 38.4% to 51.0%) of patients benefit from off-label use of amiodarone with large heterogeneity: amiodarone is predicted to increase the probability of restoring hemodynamic stability by a mean of 0.5 percentage points (pp), with an interquartile range (IQR) of −1.1 pp to 1.0 pp, in the external dataset from the Netherlands. Using these ITEs, we defined a personalized treatment rule, which could increase the number of patients achieving hemodynamic stability by 4.4% (95% CI: 1.0% to 7.8%) compared to current practice. Additionally, we studied which biomarkers are predictive of treatment effect heterogeneity and found that patients with higher blood pressure may benefit most from off-label use of amiodarone. Altogether, our study shows the potential of causal ML together with real-world data in identifying patients who benefit from off-label drug use.\",\"manuscriptTitle\":\"Causal machine learning for assessing the effectiveness of off-label use of amiodarone in new-onset atrial fibrillation\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-07-07 02:49:08\",\"doi\":\"10.21203/rs.3.rs-6966486/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"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\":\"3baecbfb-9905-41da-911e-a022b67de42f\",\"owner\":[],\"postedDate\":\"July 7th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[{\"id\":51054225,\"name\":\"Health sciences/Diseases/Cardiovascular diseases/Arrhythmias/Atrial fibrillation\"},{\"id\":51054226,\"name\":\"Health sciences/Medical research/Outcomes research\"}],\"tags\":[],\"updatedAt\":\"2025-09-16T13:25:28+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2025-07-07 02:49:08\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-6966486\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-6966486\",\"identity\":\"rs-6966486\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}