A machine learning approach using stone volume to predict stone-free status at ureteroscopy.

INTRODUCTION: To develop a predictive model incorporating stone volume along with other clinical and radiological factors to predict stone-free (SF) status at ureteroscopy (URS). MATERIAL AND METHODS: Retrospective analysis of patients undergoing URS for kidney stone disease at our institution from 2012 to 2021. SF status was defined as stone fragments < 2 mm at the end of the procedure confirmed endoscopically and no evidence of stone fragments > 2 mm at XR KUB or US KUB at 3 months follow up. We specifically included all non-SF patients to optimise our algorithm for identifying instances with residual stone burden. SF patients were also randomly sampled over the same time period to ensure a more balanced dataset for ML prediction. Stone volumes were measured using preprocedural CT and combined with 19 other clinical and radiological factors. A bagged trees machine learning model with cross-validation was used for this analysis. RESULTS: 330 patients were included (SF: n = 276, not SF: n = 54, mean age 59.5 ± 16.1 years). A fivefold cross validated RUSboosted trees model has an accuracy of 74.5% and AUC of 0.82. The model sensitivity and specificity were 75% and 72.2% respectively. Variable importance analysis identified total stone volume (17.7% of total importance), operation time (14.3%), age (12.9%) and stone composition (10.9%) as important factors in predicting non-SF patients. Single and cumulative stone size which are commonly used in current practice to guide management, only represented 9.4% and 4.7% of total importance, respectively. CONCLUSION: Machine learning can be used to predict patients that will be SF at the time of URS. Total stone volume appears to be more important than stone size in predicting SF status. Our findings could be used to optimise patient counselling and highlight an increasing role of stone volume to guide endourological practice and future guidelines.

Synergistic Organoboron/Palladium Catalysis for Regioselective N-Allylations of Azoles with Allylic Alcohols.

A method for regioselective palladium-catalyzed allylic alkylation of ambident nitrogen heterocycles, employing simple allylic alcohols as electrophile precursors, is described. An organoboron co-catalyst serves both to activate the azole-type nucleophile toward selective N-functionalization and to accelerate the formation of a π-allylpalladium complex from the allylic alcohol. The method can be applied to various heterocycle types, including 1,2,3- and 1,2,4-triazoles, tetrazoles, pyrazoles, and purines, and can be extended to substituted allylic alcohol partners.

Dearomative Cyclopropanation of Naphthols via Cyclopropene Ring-Opening.

The dearomatization of 2-naphthols represents a simple method for the construction of complex 3D structures from simple planar starting materials. We describe a cyclopropanation of 2-naphthols that proceeds via cyclopropene ring-opening using rhodium and acid catalysis under mild conditions. The vinyl cyclopropane molecules were formed with high chemoselectivity and scalability, which could be further functionalized at different sites. Both computational and experimental evidence were used to elucidate the reaction mechanism.