AI Article Synopsis

  • The study investigates how effective ureteral jet flow measurements via Doppler ultrasonography, combined with CT imaging parameters, are in predicting impacted ureteral stones (IUS) in patients.
  • A total of 178 patients were analyzed, with specific factors like stone size, location, and ureteral wall thickness being recorded from CT images, while jet flows were assessed using Doppler USG.
  • Five key predictors were identified for IUS: stone size, HU value under the stone, ureteral wall thickness, anteroposterior diameter, and stone side value, leading to a high predictive scoring system with 89% sensitivity and 91% specificity for treating ureteral stones.

Article Abstract

To examine the efficacy of ureteral jet flow measured by Doppler ultrasonography (USG) together with radiological parameters obtained by noncontrast enhanced CT in predicting whether a ureteral stone is impacted. A total of 178 patients that met the criteria were included in the study. Stone size, location, grade of hydronephrosis, HU values of the stone and the proximal and distal parts of the stone, and ureteral wall thickness (UWT) were obtained from noncontrast enhanced abdominal CT images. Ureteral jet flows () were measured and recorded by Doppler USG. Thirty-eight cases in whom the guidewire or opaque contrast material could not pass beyond the stone were included in the impacted ureteral stone (IUS) group, whereas the remaining 140 patients constituted the non-IUS (non-IUS) group. Significant independent predictors of IUS were determined as stone size (odds ratio [OR]: 2.23,  = 0.020), ureteral HU value under the stone (OR: 1.11,  = 0.014), UWT (OR: 5.66,  = 0.009), anteroposterior diameter (OR: 1.23,  = 0.033), and the value of the stone side (OR: 0.76,  = 0.011). The proposed scoring system predicted IUS with 89% sensitivity and 91% specificity at a cutoff value of 11.5. Determining whether a ureteral stone is impacted is important in deciding on the optimal treatment modality. The nomogram and scoring system that we created based on the data that were obtained with noninvasive methods can predict IUS with high sensitivity and specificity.

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http://dx.doi.org/10.1089/end.2021.0168DOI Listing

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