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 Wangxu, He Yongfang, Chen Xiaobo, Huang Chuanfen   

  1. (Department of Radiology, Changshou District People's Hospital, Chongqing 401220)

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

Abstract:

Objective: To analyze the predictive value of stone CT value for the efficiency of flexible ureteroscopic lithotomy. Methods: A 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 data(number, volume, and CT value) were recorded. After 3 months of postoperative follow-up, the patients were divided into stone clearance group and stone residue group based on the stone clearance status. Univariate analysis, multivariate Logistic regression analysis, and receiver operator characteristic(ROC) curve analysis were performed. Results: The univariate analysis showed that there were statistically significant differences in stone number, stone volume, and stone CT value between the two groups(P<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 lithotomy(P<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 HU, with an area under the curve of 0.725(95%CI: 0.632-0.791), sensitivity of 51.91%, and specificity of 84.72%. Conclusion: The 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: