Development and Validation of a Random Survival Forest-Based Model for Predicting All-Cause Mortality in Peritoneal Dialysis Patients: A Retrospective Cohort Study

Development and Validation of a Random Survival Forest-Based Model for Predicting All-Cause Mortality in Peritoneal Dialysis Patients: A Retrospective Cohort 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 Development and Validation of a Random Survival Forest-Based Model for Predicting All-Cause Mortality in Peritoneal Dialysis Patients: A Retrospective Cohort Study Nan Hu, Wenyu Zhang, Xichao Wang, Na Sun, Jinping Li, Wenxiu Chang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8131831/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 Patients undergoing peritoneal dialysis (PD) face elevated risks of all-cause mortality. We aimed to develop and validate a prediction model for all-cause mortality in PD patients using random survival forest (RSF) methodology. Methods In this retrospective cohort study, patients receiving maintenance PD between 1 January 2017 and 31 December 2019 were enrolled as the training cohort (n = 221), with those treated from 1 January 2020 to 31 December 2022 serving as the temporal validation cohort (n = 256). Eligible participants aged 18–80 years had received PD for ≥ 3 months with complete baseline data. We collected demographic characteristics, laboratory parameters (hemoglobin, albumin, alkaline phosphatase, urea nitrogen, creatinine, electrolytes, etc.), and PD-specific metrics (Kt/V, peritonitis rate). RSF analysis was employed to identify mortality-associated variables. Results The prediction model incorporated multiple clinical and biochemical variables, demonstrating robust discriminative ability in the training set (C-index 0.931, 95% CI 0.894–0.968). Temporal validation maintained satisfactory performance (C-index 0.733, 95% CI 0.635–0.831), with age, hypoalbuminemia, and elevated high-sensitivity C-reactive protein (hsCRP) emerging as key predictors. A clinically applicable nomogram was developed to estimate 3-year survival probabilities. Conclusions This RSF-based model reliably predicts all-cause mortality in PD patients, providing a valuable tool for risk stratification and personalized management. External validation is warranted to confirm generalizability. Peritoneal dialysis All-cause mortality Nomogram Predictive model Random survival forest Full Text Additional Declarations No competing interests reported. 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-8131831","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":554315139,"identity":"fb323af4-073b-40be-988e-de654bf19fa3","order_by":0,"name":"Nan Hu","email":"","orcid":"","institution":"Tianjin First Center Hospital","correspondingAuthor":false,"prefix":"","firstName":"Nan","middleName":"","lastName":"Hu","suffix":""},{"id":554315140,"identity":"16c0b25a-e2e5-4df9-bb65-7f78abcf1e86","order_by":1,"name":"Wenyu Zhang","email":"","orcid":"","institution":"Tianjin First Center 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Study","fulltext":[],"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":true,"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":"Peritoneal dialysis, All-cause mortality, Nomogram, Predictive model, Random survival forest","lastPublishedDoi":"10.21203/rs.3.rs-8131831/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8131831/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003ePatients undergoing peritoneal dialysis (PD) face elevated risks of all-cause mortality. We aimed to develop and validate a prediction model for all-cause mortality in PD patients using random survival forest (RSF) methodology.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eIn this retrospective cohort study, patients receiving maintenance PD between 1 January 2017 and 31 December 2019 were enrolled as the training cohort (n\u0026thinsp;=\u0026thinsp;221), with those treated from 1 January 2020 to 31 December 2022 serving as the temporal validation cohort (n\u0026thinsp;=\u0026thinsp;256). Eligible participants aged 18\u0026ndash;80 years had received PD for \u0026ge;\u0026thinsp;3 months with complete baseline data. We collected demographic characteristics, laboratory parameters (hemoglobin, albumin, alkaline phosphatase, urea nitrogen, creatinine, electrolytes, etc.), and PD-specific metrics (Kt/V, peritonitis rate). RSF analysis was employed to identify mortality-associated variables.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThe prediction model incorporated multiple clinical and biochemical variables, demonstrating robust discriminative ability in the training set (C-index 0.931, 95% CI 0.894\u0026ndash;0.968). Temporal validation maintained satisfactory performance (C-index 0.733, 95% CI 0.635\u0026ndash;0.831), with age, hypoalbuminemia, and elevated high-sensitivity C-reactive protein (hsCRP) emerging as key predictors. A clinically applicable nomogram was developed to estimate 3-year survival probabilities.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eThis RSF-based model reliably predicts all-cause mortality in PD patients, providing a valuable tool for risk stratification and personalized management. External validation is warranted to confirm generalizability.\u003c/p\u003e","manuscriptTitle":"Development and Validation of a Random Survival Forest-Based Model for Predicting All-Cause Mortality in Peritoneal Dialysis Patients: A Retrospective Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-03 12:39:53","doi":"10.21203/rs.3.rs-8131831/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":"cc348bbe-d476-41a4-82da-acb834c38365","owner":[],"postedDate":"December 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-13T10:08:15+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-03 12:39:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8131831","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8131831","identity":"rs-8131831","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}