Journal of Dali University ›› 2023, Vol. 8 ›› Issue (10): 81-84.

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The Predictive Value of Stone CT Value for the Efficiency of Flexible Ureteroscopic Lithotomy

Xia WangxuHe YongfangChen XiaoboHuang Chuanfen   

  1. Department of RadiologyChangshou District People's HospitalChongqing 401220

  • Received:2022-03-29 Revised:2022-04-08 Online:2023-10-15 Published:2023-10-26

Abstract:

ObjectiveTo analyze the predictive value of stone CT value for the efficiency of flexible ureteroscopic lithotomy. MethodsA retrospective analysis was conducted on the medical records of 135 patients who underwent flexible ureteroscopic lithotomy at Changshou District People's Hospital in Chongqing. The patients' general preoperative information and stone datanumbervolumeand CT valuewere recorded. After 3 months of postoperative follow-upthe patients were divided into stone clearance group and stone residue group based on the stone clearance status. Univariate analysismultivariate Logistic regression analysisand receiver operator characteristicROCcurve analysis were performed. ResultsThe univariate analysis showed that there were statistically significant differences in stone numberstone volumeand stone CT value between the two groupsP<0.05. The results of multivariate Logistic regression analysis showed that the stone number 2 and elevated stone CT value were risk factors for residual stones after flexible ureteroscopic lithotomyP<0.05. The ROC curve analysis results showed that the predicted cut-off value of stone CT for residual stones after flexible ureteroscopic lithotomy was 1 092.75 HUwith an area under the curve of 0.72595%CI0.632-0.791), sensitivity of 51.91%and specificity of 84.72%. ConclusionThe residual stones after flexible ureteroscopic lithotripsy are mainly influenced by stone number and stone CT value. Stone CT value has a high diagnostic efficacy in predicting residual stones after flexible ureteroscopic lithotripsy. 

Key words:

stone CT value, flexible ureteroscopic lithotomy, residual stones, multivariate Logistic regression analysis, receiver operator characteristic curve analysis

CLC Number: