Exploration of Early Warning Models for Critical Risk in Emergency Patients

Exploration of Early Warning Models for Critical Risk in Emergency Patients | 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 Exploration of Early Warning Models for Critical Risk in Emergency Patients Xurui Li, Jian Lv, Hongling Li, Hui Guo, Qian Zhao, Jianguo Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6480239/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 Objective To establish an early warning model for assessing the critical risk of emergency patients and evaluate its clinical benefits, providing a reference for the early identification of critically ill patients in emergency departments. Method The 3859 enrolled patients were randomly divided into a training set and a validation set in a 7:3 ratio. In the training set, a predictive model was established on the basis of the results of multivariate logistic stepwise regression analysis. Moreover, risk levels were divided, and the predictive efficacy and clinical benefits of the predictive model were verified. Results Multivariate logistic stepwise regression analysis revealed that sex, age, heart rate (HR), respiratory rate (R), systolic blood pressure (SBP), pulse oxygen (SPO2), consciousness, pupils, mental state, and pain score were independent risk factors for early assessment of critical risk (P < 0.05), and a predictive model was established on this basis. Using a conditional inference tree, critical risks are classified into low risk, medium risk, and high risk. Furthermore, the prediction model was internally validated in both the training and validation sets, with a training set area under the subject working characteristic curve (AUC) of 0.926 (95% confidence interval [CI] 0.912–0.939, P < 0.001) and a validation set AUC of 0.911 (95% CI 0.886–0.936, P < 0.001), indicating good discrimination. The calibration curve of the training set fits the standard curve, whereas the calibration curve of the validation set model slightly deviates from the standard curve, indicating good calibration of the predicted model. The decision curve analysis (DCA) and the clinical impact curve (CIC) suggest that both groups of patients can achieve good clinical effectiveness. Conclusion Establishing a predictive model for the early assessment of emergency critical risk is helpful for the early identification and intervention of emergency critical patients. critical risk early warning predictive model risk level 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-6480239","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":457345552,"identity":"0e6f0393-01af-4836-8b45-b01ae649e581","order_by":0,"name":"Xurui Li","email":"","orcid":"","institution":"Hebei General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xurui","middleName":"","lastName":"Li","suffix":""},{"id":457345553,"identity":"dcb63dd8-5e10-49eb-85f5-553334bc4b5e","order_by":1,"name":"Jian Lv","email":"","orcid":"","institution":"Hebei General 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15:53:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6480239/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6480239/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83995644,"identity":"2a87be55-9ec2-4622-9665-af6008795ab5","added_by":"auto","created_at":"2025-06-05 13:24:02","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":582980,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6480239/v1_covered_7af82252-c048-4d3f-8027-5ccd6574fd6e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Exploration of Early Warning Models for Critical Risk in Emergency 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level","lastPublishedDoi":"10.21203/rs.3.rs-6480239/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6480239/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eTo establish an early warning model for assessing the critical risk of emergency patients and evaluate its clinical benefits, providing a reference for the early identification of critically ill patients in emergency departments.\u003c/p\u003e\u003ch2\u003eMethod\u003c/h2\u003e \u003cp\u003eThe 3859 enrolled patients were randomly divided into a training set and a validation set in a 7:3 ratio. In the training set, a predictive model was established on the basis of the results of multivariate logistic stepwise regression analysis. Moreover, risk levels were divided, and the predictive efficacy and clinical benefits of the predictive model were verified.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eMultivariate logistic stepwise regression analysis revealed that sex, age, heart rate (HR), respiratory rate (R), systolic blood pressure (SBP), pulse oxygen (SPO2), consciousness, pupils, mental state, and pain score were independent risk factors for early assessment of critical risk (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and a predictive model was established on this basis. Using a conditional inference tree, critical risks are classified into low risk, medium risk, and high risk. Furthermore, the prediction model was internally validated in both the training and validation sets, with a training set area under the subject working characteristic curve (AUC) of 0.926 (95% confidence interval [CI] 0.912\u0026ndash;0.939, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and a validation set AUC of 0.911 (95% CI 0.886\u0026ndash;0.936, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating good discrimination. The calibration curve of the training set fits the standard curve, whereas the calibration curve of the validation set model slightly deviates from the standard curve, indicating good calibration of the predicted model. The decision curve analysis (DCA) and the clinical impact curve (CIC) suggest that both groups of patients can achieve good clinical effectiveness.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eEstablishing a predictive model for the early assessment of emergency critical risk is helpful for the early identification and intervention of emergency critical patients.\u003c/p\u003e","manuscriptTitle":"Exploration of Early Warning Models for Critical Risk in Emergency Patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-19 15:02:46","doi":"10.21203/rs.3.rs-6480239/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":"5865641f-b34b-4996-a2f1-92abd988096c","owner":[],"postedDate":"May 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-06-05T13:23:43+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-19 15:02:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6480239","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6480239","identity":"rs-6480239","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}