Clinical prediction model for the risk of bleeding during hospitalization in patients with acute myocardial infarction: a retrospective cohort study from the MIMIC-IV database

Clinical prediction model for the risk of bleeding during hospitalization in patients with acute myocardial infarction: a retrospective cohort study from the MIMIC-IV database | 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 Clinical prediction model for the risk of bleeding during hospitalization in patients with acute myocardial infarction: a retrospective cohort study from the MIMIC-IV database ZIjie Bai, Tongxian Hou, Pengyu Lu, Huiqin Li, Jieyun Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6993003/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Background Bleeding is a serious and potentially life-threatening complication in patients with acute myocardial infarction (AMI), significantly affecting patient outcomes. Early identification of high-risk patients is critical for reducing complications, improving outcomes, and guiding clinical decision-making. Objective This study aims to develop and validate a machine learning (ML)-based model to predict the risk of in-hospital bleeding events in AMI patients, identify key risk factors, and assess the clinical applicability of the model for risk stratification and decision support. Methods This study retrieved data from 2,646 AMI patients in the MIMIC-IV database, with missing data imputed using KNN. LASSO regression was used to screen the imputed data, and eight machine learning models were constructed based on the selected clinical features. Model performance was evaluated using ROC curves, calibration curves, and SHAP interpretability analysis, while clinical net benefit was assessed using decision curves. Results Among the 2,646 patients with acute myocardial infarction (AMI), the bleeding group had a higher proportion of males (73.0% vs. 64.6%), coagulation disorders (PT 1.6 vs. 1.2, APTT 77.7 vs. 77.0), diabetes (43.1% vs. 32.6%), and CABG treatment rate (84.7% vs. 10.3%) were significantly higher in the bleeding group than in the non-bleeding group (all P < 0.001); age, CABG treatment, IABP use, white blood cell count (WBC), coagulation function (PT, APTT), direct bilirubin (DBIL), platelets (Plt), ALT, and clopidogrel use. Among the eight machine learning models constructed, XGBoost demonstrated the best overall performance, with an AUC of 0.925 (accuracy 0.925, F1 score 0.802). Its decision curve showed the highest net benefit in the threshold range of 0.1–0.2, and the calibration curve indicated that the predicted risk was highly consistent with the actual risk (R² = 0.817). SHAP analysis revealed that CABG treatment and IABP use were the strongest risk factors, while elevated platelet counts were associated with reduced risk. The XGBoost model based on multidimensional features can accurately predict the risk of bleeding during hospitalization for AMI (sensitivity 0.849, specificity 0.884). Its high accuracy and interpretability provide a reliable tool for clinical dynamic decision-making, particularly for personalized optimization of treatment strategies. Conclusion The ML-based XGBoost model provides a reliable and clinically applicable tool for predicting bleeding events during hospitalization in AMI patients. This model combines high accuracy and interpretability, providing a quantitative tool for clinical dynamic assessment of anticoagulation therapy risks. Future multi-center validation can further optimize its application value in personalized treatment decision-making. Acute myocardial infarction bleeding machine learning predictive model Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 31 Oct, 2025 Editor invited by journal 09 Sep, 2025 Editor assigned by journal 28 Jul, 2025 Submission checks completed at journal 26 Jul, 2025 First submitted to journal 26 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-6993003","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":542631081,"identity":"5079fa53-cc59-473e-8cfa-277346fc8c4d","order_by":0,"name":"ZIjie Bai","email":"","orcid":"","institution":"Kaifeng Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"ZIjie","middleName":"","lastName":"Bai","suffix":""},{"id":542631082,"identity":"2c500cc8-fb5c-4e49-9c07-6f5d7f1b6d86","order_by":1,"name":"Tongxian Hou","email":"","orcid":"","institution":"Kaifeng Central 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the MIMIC-IV database","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Acute myocardial infarction, bleeding, machine learning, predictive model","lastPublishedDoi":"10.21203/rs.3.rs-6993003/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6993003/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e\u003cp\u003eBleeding is a serious and potentially life-threatening complication in patients with acute myocardial infarction (AMI), significantly affecting patient outcomes. Early identification of high-risk patients is critical for reducing complications, improving outcomes, and guiding clinical decision-making.\u003c/p\u003e\u003cp\u003e\u003cb\u003eObjective\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study aims to develop and validate a machine learning (ML)-based model to predict the risk of in-hospital bleeding events in AMI patients, identify key risk factors, and assess the clinical applicability of the model for risk stratification and decision support.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study retrieved data from 2,646 AMI patients in the MIMIC-IV database, with missing data imputed using KNN. LASSO regression was used to screen the imputed data, and eight machine learning models were constructed based on the selected clinical features. Model performance was evaluated using ROC curves, calibration curves, and SHAP interpretability analysis, while clinical net benefit was assessed using decision curves.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAmong the 2,646 patients with acute myocardial infarction (AMI), the bleeding group had a higher proportion of males (73.0% vs. 64.6%), coagulation disorders (PT 1.6 vs. 1.2, APTT 77.7 vs. 77.0), diabetes (43.1% vs. 32.6%), and CABG treatment rate (84.7% vs. 10.3%) were significantly higher in the bleeding group than in the non-bleeding group (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.001); age, CABG treatment, IABP use, white blood cell count (WBC), coagulation function (PT, APTT), direct bilirubin (DBIL), platelets (Plt), ALT, and clopidogrel use. Among the eight machine learning models constructed, XGBoost demonstrated the best overall performance, with an AUC of 0.925 (accuracy 0.925, F1 score 0.802). Its decision curve showed the highest net benefit in the threshold range of 0.1\u0026ndash;0.2, and the calibration curve indicated that the predicted risk was highly consistent with the actual risk (R\u0026sup2; = 0.817). SHAP analysis revealed that CABG treatment and IABP use were the strongest risk factors, while elevated platelet counts were associated with reduced risk. The XGBoost model based on multidimensional features can accurately predict the risk of bleeding during hospitalization for AMI (sensitivity 0.849, specificity 0.884). Its high accuracy and interpretability provide a reliable tool for clinical dynamic decision-making, particularly for personalized optimization of treatment strategies.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusion\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe ML-based XGBoost model provides a reliable and clinically applicable tool for predicting bleeding events during hospitalization in AMI patients. This model combines high accuracy and interpretability, providing a quantitative tool for clinical dynamic assessment of anticoagulation therapy risks. Future multi-center validation can further optimize its application value in personalized treatment decision-making.\u003c/p\u003e","manuscriptTitle":"Clinical prediction model for the risk of bleeding during hospitalization in patients with acute myocardial infarction: a retrospective cohort study from the MIMIC-IV database","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-11 12:17:01","doi":"10.21203/rs.3.rs-6993003/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2025-10-31T08:44:59+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-09T06:50:22+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-28T05:11:21+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-26T17:54:02+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cardiovascular Disorders","date":"2025-07-26T14:38:14+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"14599281-ef06-45f8-9134-f5d8261353fe","owner":[],"postedDate":"November 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-11-11T12:17:01+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-11 12:17:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6993003","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6993003","identity":"rs-6993003","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}