Efficacy of left internal spermatic vein reflux reconstruction under microscope left spermatic vein high ligation for treatment of degree III left spermatic vein Significance of influencing factors Ranking and Construction and validation of predictive model

Efficacy of left internal spermatic vein reflux reconstruction under microscope left spermatic vein high ligation for treatment of degree III left spermatic vein Significance of influencing factors Ranking and Construction and validation of predictive model | 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 Efficacy of left internal spermatic vein reflux reconstruction under microscope left spermatic vein high ligation for treatment of degree III left spermatic vein Significance of influencing factors Ranking and Construction and validation of predictive model Xichun Zhang, Baoan Li, Hong Wang, Xiaofei Liu, Shaoke Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6368277/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Objective To analyze the importance ranking of influencing factors of postoperative complications in patients with ureteral calculi using Logistic regression and random forest algorithm, and to construct and verify the prediction model. Methods Clinical information of 254 patients with ureteral calculi who received surgical treatment in our hospital from January 2021 to December 2023 was collected as variables, including age, gender, calculi diameter, calculi location, calculi type, preoperative urine routine, etc. Patients were divided into complication group (n=52) and no-complication group (n=202) according to whether there was any postoperative complication. Univariate and multivariate analyses using Logistic regression were performed to identify independent risk factors for postoperative complications. The random forest algorithm was further used to construct the prediction model, and the performance of the model was verified by receiver operating characteristic curve (ROC) analysis, calibration curve evaluation, and decision curve analysis (DCA). Results A total of 254 patients were included, and 52 cases (20.47%) had postoperative complications. Logistic regression analysis showed that hypertension, diabetes, neurogenic bladder, preoperative urinary tract infection, stone diameter, operation time and intraoperative perfusion pressure were the independent risk factors for postoperative complications in patients with ureteral calculi (P<0.05). The order of importance of variables obtained from the random forest model was operation time, stone diameter, perfusion pressure during operation, presence of urinary tract infection before operation, concomitant neurogenic bladder, concomitant diabetes mellitus and concomitant hypertension. Nomograms model predicts AUC of 0.801 (95% CI: 0.719-0.883) for postoperative complications of ureteral calculi. The predicted curve lines of the model group and the verification group were basically fitted with the standard curves. Analysis of the decision curve shows that when the probability threshold of Nomograms model for predicting postoperative complications in patients with ureteral calculi is 0.1–0.9, the net benefit rate for patients is large. Conclusion Logistic regression and random forest algorithm can effectively analyze the influencing factors of postoperative complications in patients with ureteral calculi and construct accurate prediction model. The model can provide a theoretical reference for the prevention and treatment of postoperative complications and optimize the individualized treatment plan. ureteral calculi Postoperative complications Logistic regression Random forest algorithm prediction model Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 21 May, 2025 Reviewers agreed at journal 13 May, 2025 Reviewers invited by journal 05 May, 2025 Editor invited by journal 10 Apr, 2025 Editor assigned by journal 08 Apr, 2025 Submission checks completed at journal 08 Apr, 2025 First submitted to journal 03 Apr, 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-6368277","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":453554188,"identity":"23a0a695-3d74-4637-9f07-0cceb42eaf69","order_by":0,"name":"Xichun Zhang","email":"","orcid":"","institution":"Nanyang First People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xichun","middleName":"","lastName":"Zhang","suffix":""},{"id":453554189,"identity":"21086b09-dfdc-44af-96ee-a3c4016c18e8","order_by":1,"name":"Baoan 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Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"ureteral calculi, Postoperative complications, Logistic regression, Random forest algorithm, prediction model","lastPublishedDoi":"10.21203/rs.3.rs-6368277/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6368277/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective \u003c/strong\u003eTo analyze the importance ranking of influencing factors of postoperative complications in patients with ureteral calculi using Logistic regression and random forest algorithm, and to construct and verify the prediction model.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods \u003c/strong\u003eClinical information of 254 patients with ureteral calculi who received surgical treatment in our hospital from January 2021 to December 2023 was collected as variables, including age, gender, calculi diameter, calculi location, calculi type, preoperative urine routine, etc. Patients were divided into complication group (n=52) and no-complication group (n=202) according to whether there was any postoperative complication. Univariate and multivariate analyses using Logistic regression were performed to identify independent risk factors for postoperative complications. The random forest algorithm was further used to construct the prediction model, and the performance of the model was verified by receiver operating characteristic curve (ROC) analysis, calibration curve evaluation, and decision curve analysis (DCA).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults \u003c/strong\u003eA total of 254 patients were included, and 52 cases (20.47%) had postoperative complications. Logistic regression analysis showed that hypertension, diabetes, neurogenic bladder, preoperative urinary tract infection, stone diameter, operation time and intraoperative perfusion pressure were the independent risk factors for postoperative complications in patients with ureteral calculi (P\u0026lt;0.05). The order of importance of variables obtained from the random forest model was operation time, stone diameter, perfusion pressure during operation, presence of urinary tract infection before operation, concomitant neurogenic bladder, concomitant diabetes mellitus and concomitant hypertension. Nomograms model predicts AUC of 0.801 (95% CI: 0.719-0.883) for postoperative complications of ureteral calculi. The predicted curve lines of the model group and the verification group were basically fitted with the standard curves. Analysis of the decision curve shows that when the probability threshold of Nomograms model for predicting postoperative complications in patients with ureteral calculi is 0.1–0.9, the net benefit rate for patients is large.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion \u003c/strong\u003eLogistic regression and random forest algorithm can effectively analyze the influencing factors of postoperative complications in patients with ureteral calculi and construct accurate prediction model. The model can provide a theoretical reference for the prevention and treatment of postoperative complications and optimize the individualized treatment plan.\u003c/p\u003e","manuscriptTitle":"Efficacy of left internal spermatic vein reflux reconstruction under microscope left spermatic vein high ligation for treatment of degree III left spermatic vein Significance of influencing factors Ranking and Construction and validation of predictive model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-09 05:36:26","doi":"10.21203/rs.3.rs-6368277/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2025-05-21T21:00:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"108366672574107664935570427588179889424","date":"2025-05-13T18:37:17+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-06T02:35:04+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-04-10T05:58:59+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-09T03:04:30+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-09T03:03:26+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Nephrology","date":"2025-04-03T09:58:37+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-nephrology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bnep","sideBox":"Learn more about [BMC Nephrology](http://bmcnephrol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bnep/default.aspx","title":"BMC Nephrology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"94866412-db2b-4972-9e7f-4a51630ef313","owner":[],"postedDate":"May 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-05-09T05:36:26+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-09 05:36:26","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6368277","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6368277","identity":"rs-6368277","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}