Association Between Serum Creatinine-to-Albumin Ratio, Serum Glucose-to-Potassium Ratio, and In-Hospital 90-Day All-Cause Mortality in Patients with Cirrhosis: A Machine Learning-Based Retrospective Cohort Study and Predictive Model Development

Association Between Serum Creatinine-to-Albumin Ratio, Serum Glucose-to-Potassium Ratio, and In-Hospital 90-Day All-Cause Mortality in Patients with Cirrhosis: A Machine Learning-Based Retrospective Cohort Study and Predictive Model Development | 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 Association Between Serum Creatinine-to-Albumin Ratio, Serum Glucose-to-Potassium Ratio, and In-Hospital 90-Day All-Cause Mortality in Patients with Cirrhosis: A Machine Learning-Based Retrospective Cohort Study and Predictive Model Development Dandan Weng, Ke cao, Juanfen Chu, Qingren Cao, Jing Yang, Guancheng Huang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8180248/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 Objective Cirrhosis is a progressive liver disease caused by chronic inflammation. The serum albumin-to-creatinine ratio (CAR) and glucose-to-potassium ratio (GPR) are emerging prognostic biomarkers, but their value in liver cirrhosis remains unclear. Our study, based on the Medical Information Mart for Intensive Care IV (MIMIC-IV) database, aims to explore the association between CAR, GPR, and the prognosis of cirrhosis, providing quantitative evidence for clinical decision-making, optimizing treatment, and improving patient outcomes. Methods Based on clinical data from 3,649 cirrhosis patients in the MIMIC-IV database, the cohort was divided into two groups based on survival status, and baseline statistical descriptions were provided for each group. The CAR and GPR were categorized into quartiles, and their associations with prognosis were analyzed using Cox regression, Kaplan-Meier (K-M) curves, restricted cubic splines (RCS), and subgroup analysis. Predictive models were developed using machine learning algorithms, and performance was evaluated through the area under the curve (AUC), calibration curves, and decision curve analysis (DCA). Results Baseline characteristics results showed that patients in the non-survivor group (n = 846) had significantly higher CAR and GPR values, among other indicators, compared to those in the survivor group (n = 2,803) ( p < 0.05). Cox regression analysis found that elevated CAR was significantly associated with an increased 90-day mortality rate, whereas higher GPR was significantly associated with a decreased 90-day mortality rate. K-M curves revealed significant differences in 90-day all-cause mortality between different CAR and GPR groups ( p < 0.005). RCS analysis showed a significant non-linear relationship between CAR, GPR, and 90-day mortality ( p non-linear < 0.001). Subgroup analysis showed that CAR had a more pronounced prognostic impact in married individuals, White patients, and those with Medicaid insurance, while the protective effect of GPR was particularly notable in hypertensive patients. The Gradient Boosting Machine for Survival Analysis (GBM) model demonstrated the best predictive performance (AUC: 0.802-0.806), with superior discrimination, calibration, and clinical net benefit compared to other models. Conclusion CAR and GPR are important prognostic indicators for outcomes in patients with cirrhosis and have significant clinical application value. Health sciences/Biomarkers Health sciences/Diseases Health sciences/Medical research Health sciences/Risk factors Cirrhosis Serum Creatinine-to-Albumin Ratio Serum Glucose-to-Potassium Ratio Machine Learning MIMIC-IV Database Full Text Additional Declarations No competing interests reported. Supplementary Files appendix.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 20 Apr, 2026 Editor invited by journal 05 Dec, 2025 Editor assigned by journal 26 Nov, 2025 Submission checks completed at journal 26 Nov, 2025 First submitted to journal 22 Nov, 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. 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-8180248","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":630370841,"identity":"7f3c5f2c-c315-4e5b-9fc9-821c24c97983","order_by":0,"name":"Dandan Weng","email":"","orcid":"","institution":"Yangming Hospital Affiliated to Ningbo University, Yuyao, Zhejiang","correspondingAuthor":false,"prefix":"","firstName":"Dandan","middleName":"","lastName":"Weng","suffix":""},{"id":630370842,"identity":"fdb9bd3f-b71d-47c6-89fb-a2324f70f5dd","order_by":1,"name":"Ke cao","email":"","orcid":"","institution":"Yuyao Emergency Medical 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Development","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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Cirrhosis, Serum Creatinine-to-Albumin Ratio, Serum Glucose-to-Potassium Ratio, Machine Learning, MIMIC-IV Database","lastPublishedDoi":"10.21203/rs.3.rs-8180248/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8180248/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eObjective\u003c/p\u003e\n\u003cp\u003eCirrhosis is a progressive liver disease caused by chronic inflammation. The serum albumin-to-creatinine ratio (CAR) and glucose-to-potassium ratio (GPR) are emerging prognostic biomarkers, but their value in liver cirrhosis remains unclear. Our study, based on the Medical Information Mart for Intensive Care IV (MIMIC-IV) database, aims to explore the association between CAR, GPR, and the prognosis of cirrhosis, providing quantitative evidence for clinical decision-making, optimizing treatment, and improving patient outcomes.\u003c/p\u003e\n\u003cp\u003eMethods\u003c/p\u003e\n\u003cp\u003eBased on clinical data from 3,649 cirrhosis patients in the MIMIC-IV database, the cohort was divided into two groups based on survival status, and baseline statistical descriptions were provided for each group. The CAR and GPR were categorized into quartiles, and their associations with prognosis were analyzed using Cox regression, Kaplan-Meier (K-M) curves, restricted cubic splines (RCS), and subgroup analysis. Predictive models were developed using machine learning algorithms, and performance was evaluated through the area under the curve (AUC), calibration curves, and decision curve analysis (DCA).\u003c/p\u003e\n\u003cp\u003eResults\u003c/p\u003e\n\u003cp\u003eBaseline characteristics results showed that patients in the non-survivor group (n = 846) had significantly higher CAR and GPR values, among other indicators, compared to those in the survivor group (n = 2,803) (\u003cem\u003ep \u003c/em\u003e\u0026lt; 0.05). Cox regression analysis found that elevated CAR was significantly associated with an increased 90-day mortality rate, whereas higher GPR was significantly associated with a decreased 90-day mortality rate. K-M curves revealed significant differences in 90-day all-cause mortality between different CAR and GPR groups (\u003cem\u003ep \u003c/em\u003e\u0026lt; 0.005). RCS analysis showed a significant non-linear relationship between CAR, GPR, and 90-day mortality (\u003cem\u003ep \u003c/em\u003e\u003csub\u003e\u003cem\u003enon-linear\u003c/em\u003e\u003c/sub\u003e \u0026lt; 0.001). Subgroup analysis showed that CAR had a more pronounced prognostic impact in married individuals, White patients, and those with Medicaid insurance, while the protective effect of GPR was particularly notable in hypertensive patients. The Gradient Boosting Machine for Survival Analysis (GBM) model demonstrated the best predictive performance (AUC: 0.802-0.806), with superior discrimination, calibration, and clinical net benefit compared to other models.\u003c/p\u003e\n\u003cp\u003eConclusion\u003c/p\u003e\n\u003cp\u003eCAR and GPR are important prognostic indicators for outcomes in patients with cirrhosis and have significant clinical application value.\u003c/p\u003e","manuscriptTitle":"Association Between Serum Creatinine-to-Albumin Ratio, Serum Glucose-to-Potassium Ratio, and In-Hospital 90-Day All-Cause Mortality in Patients with Cirrhosis: A Machine Learning-Based Retrospective Cohort Study and Predictive Model Development","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-28 22:36:37","doi":"10.21203/rs.3.rs-8180248/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-04-20T13:14:57+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-12-05T10:57:27+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-26T05:34:33+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-26T05:32:39+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-11-22T11:44:47+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4ef92d4a-68b2-436d-8e56-891254184a55","owner":[],"postedDate":"April 28th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":67093197,"name":"Health sciences/Biomarkers"},{"id":67093198,"name":"Health sciences/Diseases"},{"id":67093199,"name":"Health sciences/Medical research"},{"id":67093200,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2026-04-28T22:36:38+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-28 22:36:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8180248","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8180248","identity":"rs-8180248","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}