A model for predicting AKI after cardiopulmonary bypass surgery  in Chinese patients with normal preoperative renal function.

A model for predicting AKI after cardiopulmonary bypass surgery in Chinese patients with normal preoperative renal function. | 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 A model for predicting AKI after cardiopulmonary bypass surgery in Chinese patients with normal preoperative renal function. Xuan Lin, Li Xiao, Weibin Lin, Dahui Wang, Kangqing Xu, Liting Kuang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4543762/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Dec, 2024 Read the published version in BMC Surgery → Version 1 posted 10 You are reading this latest preprint version Abstract Objective The objective of this study is to establish and validate a predictive model for the occurrence of acute kidney injury (AKI) following cardiopulmonary bypass (CPB) surgery in Chinese patients with preoperative renal function within normal range. Method From January 2015 to September 2022, a total of 1003 patients were added into the analysis. We used the ratio of 7:3 to divide the patients into a training group (n = 700) and a testing group (n = 303). Independent risk factors for postoperative AKI were identified through the least absolute shrinkage and selection operator (LASSO) regression and multifactor logistic regression analysis. A nomogram predictive model was then established. Various metrics such as the Area Under the ROC curve (AUC), calibration curve, and decision curve were used for validation of the nomogram predictive model in the training and testing groups. Additionally, the nomogram model was compared with three conventional models (Cleveland Clinic score, Mehta score, and Simplified Renal Index (SRI) score) using the AUC, calibration curve, and decision curve. Results The AKI group had a worse prognosis. Age, Body mass index (BMI), emergent surgery, CPB time, intraoperative use of adrenaline, and postoperative procalcitonin (PCT) were identified as important risk factors for AKI after CPB surgery. The nomogram predictive model demonstrated good discrimination (AUC: 0.772 (95%CI: 0.735 − 0.809) and 0.780 (95% CI: 0.724 − 0.835) ), calibration (Hosmer and Lemeshow goodness of fit test: P -value 0.6941 and 0.9539) and clinical utility in both the training and testing groups. Moreover, our model exhibited better discrimination, calibration capacity, and superior net benefit compared to the other three conventional models. Conclusion The nomogram predictive model, which established in patients with normal preoperative renal function, has high accuracy, calibration and clinical utility. The model's performance is superior to the other three conventional models (Cleveland Clinic score, Mehta score, and SRI score). Cardiopulmonary bypass Acute kidney injury Risk factors Nomogram Predictive model Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 02 Dec, 2024 Read the published version in BMC Surgery → Version 1 posted Editorial decision: Revision requested 28 Jun, 2024 Reviews received at journal 26 Jun, 2024 Reviews received at journal 19 Jun, 2024 Reviewers agreed at journal 16 Jun, 2024 Reviewers agreed at journal 14 Jun, 2024 Reviewers invited by journal 14 Jun, 2024 Editor invited by journal 14 Jun, 2024 Editor assigned by journal 13 Jun, 2024 Submission checks completed at journal 13 Jun, 2024 First submitted to journal 07 Jun, 2024 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. 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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-4543762","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":319844192,"identity":"a0aa8205-2d48-423a-b0c2-c7daa4784680","order_by":0,"name":"Xuan Lin","email":"","orcid":"","institution":"First Affiliated Hospital of Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Xuan","middleName":"","lastName":"Lin","suffix":""},{"id":319844194,"identity":"1e6857aa-732b-48f1-8f64-d4caa5a889c8","order_by":1,"name":"Li Xiao","email":"","orcid":"","institution":"First Affiliated Hospital of Sun Yat-sen 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We used the ratio of 7:3 to divide the patients into a training group (n\u0026thinsp;=\u0026thinsp;700) and a testing group (n\u0026thinsp;=\u0026thinsp;303). Independent risk factors for postoperative AKI were identified through the least absolute shrinkage and selection operator (LASSO) regression and multifactor logistic regression analysis. A nomogram predictive model was then established. Various metrics such as the Area Under the ROC curve (AUC), calibration curve, and decision curve were used for validation of the nomogram predictive model in the training and testing groups. Additionally, the nomogram model was compared with three conventional models (Cleveland Clinic score, Mehta score, and Simplified Renal Index (SRI) score) using the AUC, calibration curve, and decision curve.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe AKI group had a worse prognosis. Age, Body mass index (BMI), emergent surgery, CPB time, intraoperative use of adrenaline, and postoperative procalcitonin (PCT) were identified as important risk factors for AKI after CPB surgery. The nomogram predictive model demonstrated good discrimination (AUC: 0.772 (95%CI: 0.735\u0026thinsp;\u0026minus;\u0026thinsp;0.809) and 0.780 (95% CI: 0.724\u0026thinsp;\u0026minus;\u0026thinsp;0.835) ), calibration (Hosmer and Lemeshow goodness of fit test: \u003cem\u003eP\u003c/em\u003e-value 0.6941 and 0.9539) and clinical utility in both the training and testing groups. Moreover, our model exhibited better discrimination, calibration capacity, and superior net benefit compared to the other three conventional models.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe nomogram predictive model, which established in patients with normal preoperative renal function, has high accuracy, calibration and clinical utility. The model's performance is superior to the other three conventional models (Cleveland Clinic score, Mehta score, and SRI score).\u003c/p\u003e","manuscriptTitle":"A model for predicting AKI after cardiopulmonary bypass surgery in Chinese patients with normal preoperative renal function.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-28 19:47:56","doi":"10.21203/rs.3.rs-4543762/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-06-28T04:45:30+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-26T15:29:25+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-20T03:21:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"117498913487981639642356821777848906949","date":"2024-06-16T05:55:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"127741561696180518190635446487993368722","date":"2024-06-14T14:42:59+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-06-14T14:38:05+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-06-14T12:26:32+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-06-13T10:45:27+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-06-13T10:44:11+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Surgery","date":"2024-06-07T05:54:39+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-surgery","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bsur","sideBox":"Learn more about [BMC Surgery](http://bmcsurg.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bsur/default.aspx","title":"BMC Surgery","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"24fa91b4-ceae-46ac-a959-d0278e95acaf","owner":[],"postedDate":"June 28th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-12-09T15:59:40+00:00","versionOfRecord":{"articleIdentity":"rs-4543762","link":"https://doi.org/10.1186/s12893-024-02683-x","journal":{"identity":"bmc-surgery","isVorOnly":false,"title":"BMC Surgery"},"publishedOn":"2024-12-02 15:57:03","publishedOnDateReadable":"December 2nd, 2024"},"versionCreatedAt":"2024-06-28 19:47:56","video":"","vorDoi":"10.1186/s12893-024-02683-x","vorDoiUrl":"https://doi.org/10.1186/s12893-024-02683-x","workflowStages":[]},"version":"v1","identity":"rs-4543762","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4543762","identity":"rs-4543762","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}