Construction of warning model for the risk of postoperative recurrenceinpatients withurinary calculi based on logistic regression analysis and decision tree algorithm
Ye Huijuan
Zhang Gongfang
Abstract：Objective:Based on multivariate logistic regression analysis and decision tree algorithm,the warning model for the risk of postoperative recurrence in patients with urinary calculi was established.Methods:A total of 300 patients with urinary calculi hospitalized in the Department of Urology of Fujian Provincial Hospital from June 2021 to June 2023 were selected by convenience sampling method.According to the recurrence of calculi one year af-ter surgery,they were divided into recurrent group(n=80)and non-recurrent group(n=220).Multivariate logistic regression was used to analyze the risk factors of postoperative recurrence in patients with urinary calculi.SPSS Mod-eler software was used to construct a decision tree model of postoperative recurrence,and the predictive efficacy of the two models was analyzed.Results:Among 300 patients with urinary calculi,80 recurred 1 year later,and the re-currence rate was 26.67%(80/300).Multivariate logistic regression analysis showed that postoperative stone resi-due,urinary system infection,white blood cell"+++"in preoperative urine routine,infectious stones,ureteral ob-struction and high-protein diet were risk factors for postoperative recurrence.A decision tree model for postoperative recurrence was constructed,which had 4 layers and 17 nodes.The AUC of the decision tree model was 0.780(95%CI:0.729～0.825),and that of the multivariate logistic regression model was 0.753(95%CI:0.701～0.801),the Delong test results of the two models are Z=1.669,P=0.095.Conclusion:The predictive performance of the models constructed based on multivariate logistic regression analysis and decision tree methods for postoperative recur-rence in patients with urinary tract stones is satisfactory.There models can provide theoretical references for the pre-vention,treatment,and nursing strategies for recurrence in these patients.
Keywords：algorithmslogistic modelsurinary calculi
Publication Date：2024-06-05
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：7( 160-166 )