Iqbal N. et al., 2025: Role of variation coefficient of stone density in determining success of shock wave lithotripsy in urinary calculi.

Nadeem Iqbal, Aisha Hasan, Sajid Iqbal, Sadaf Noureen, Saeed Akhter 
World J Nephrol. 2025 Mar 25;14(1):96946. doi: 10.5527/wjn.v14.i1.96946 FREE PMC ARTICLE

Abstract

Background: Various stone factors can affect the net results of shock wave lithotripsy (SWL). Recently a new factor called variation coefficient of stone density (VCSD) is being considered to have an impact on stone free rates.

Aim: To assess the role of VCSD in determining success of SWL in urinary calculi.

Methods: Charts review was utilized for collection of data variables. The patients were subjected to SWL, using an electromagnetic lithotripter. Mean stone density (MSD), stone heterogeneity index (SHI), and VCSD were calculated by generating regions of interest on computed tomography (CT) images. Role of these factors were determined by applying the relevant statistical tests for continuous and categorical variables and a P value of < 0.05 was gauged to be statistically significant.

Results: There were a total of 407 patients included in the analysis. The mean age of the subjects in this study was 38.89 ± 14.61 years. In total, 165 out of the 407 patients could not achieve stone free status. The successful group had a significantly lower stone volume as compared to the unsuccessful group (P < 0.0001). Skin to stone distance was not dissimilar among the two groups (P = 0.47). MSD was significantly lower in the successful group (P < 0.0001). SHI and VCSD were both significantly higher in the successful group (P < 0.0001).

Conclusion: VCSD, a useful CT based parameter, can be utilized to gauge stone fragility and hence the prediction of SWL outcomes.

Comment Peter Alken

The ROC curves of stone volume, density, standard deviation and VCSD seem to indicate the latter as the winner. As it is possible to retrospectively analyse CT- images, hopefully ESWL teams from different departments will join to publish large series.

Peter Alken