Software-Estimated Stone Volume Is Better Predictor of Spontaneous Passage for Acute Nephrolithiasis

Abstract

Purpose:

To evaluate whether computer program-estimated urolith stone volume (SV) was a better predictor of spontaneous passage (SP) compared with program-estimated stone diameter (PD) or manually measured stone diameter (MD), and whether utilizing SV and MD together provided additional value in SP prediction compared with MD alone.

Materials and Methods:

Retrospective analysis of patients with acute renal colic and single renal/ureteral stone on CT from July 2017 to April 2020. Diameter obtained from radiology reports or manually measured when report not available. Semiautomated stone analysis software (qSAS) was used to estimate SV and PD. ROC analysis was performed to compare accuracy of SV vs MD vs PD in predicting SP by 2, 4, and 6 weeks. Subgroup analysis was performed by stone size (</≥6 mm) and location (proximal/distal).

Results:

Among 172 patients analyzed, SP occurred in 71 (41%). Patient age (mean 53), gender (38%F), and stone history/side did not differ significantly by SP. Average MD, PD, and SV were significantly smaller among SP stones vs stones requiring surgery (MD 4.3 mm vs 8.0 mm, PD 5.5 mm vs 9.4 mm, and SV 40 mm³ vs 312 mm³; p < 0.001). ROC analysis showed significantly higher area under curve (AUC) for SV for predicting SP by 4 and 6 weeks compared with MD and PD (AUC 0.93 vs 0.86 vs 0.85 4 weeks, p < 0.001; 0.92 vs 0.85 vs 0.86 6 weeks, p < 0.003). AUC difference between SV vs MD was much greater among stones ≥6 mm or proximal stones. Utilizing SV and MD together yielded improved positive predictive value and negative predictive value for SP prediction.

Conclusions:

SV is a more accurate predictor of SP compared with linear stone dimensions, especially in the setting of larger and/or more proximal stones. Utilizing SV and diameter together yielded improved SP predictions compared with using either metric alone. Prospective studies are indicated to investigate the clinical utility of SV for SP prediction.