Predicting extracorporeal shock wave lithotripsy success with a machine learning nomogram: A pilot study.

Abstract

Introduction: The aim of this pilot study was to develop and validate a machine learning (ML)-based clinical nomogram to predict the success rate of extracorporeal shock wave lithotripsy (ESWL) for kidney stones, optimizing patient selection and treatment outcomes.

Material and methods: A retrospective analysis of ESWL data in all nephrolithiasis patients was performed from January 2018 to September 2022. Age, gender, stone size, stone area, stone location inside the kidney, stone density, skin-to-stone distance (SSD), stent presence, hydronephrosis presence, complications, and number of ESWL procedures were analysed. Inclusion criteria were a single kidney stone, stone size 5 mm to 20 mm, stone density less than average 1000 HU according to a native CT scan, and an adult patient. Statistical analysis was performed using the T test, mean, and standard deviation, and the calculations were processed using IBM SPSS Statistics for Macintosh, Version 25.0 with statistically significant values indicated by p <0.05. Python programming language was used to test machine learning models based on the previous study data. The scikit-learn library was used as a source of different ML models.

Results: 102 patients fulfilled inclusion and exclusion criteria. There were 70 male and 32 female patients. The mean age was 54.1 ±13.2 years, mean stone size 9.1 ±3.1 mm, stone area 47.3 ±30.8 mm² and SSD 8.4 ±1.8 cm. Patients were divided into two groups. The first group consisted of patients who achieved an SFR ≤3 mm after single ESWL procedure (group ESWL 1^st, n = 42), the second group were patients who needed more than one ESWL procedure to achieve the SFR (group ESWL n^th, n = 60). Statistically significant predictors of single-treatment success were stone size (7.9 ±2.2 mm vs 10.0 ±3.4 mm, p <0.001), stone area (34.1 ±19.4 mm² vs 56.6 ±33.9 mm², p <0.001), and SSD (7.2 ±1.3 cm vs 9.3 ±1.7 cm, p <0.001). The Linear Discriminant Analysis model (LDA) achieved a mean predictive accuracy of ~70%. The final nomogram identified the highest probability of single-treatment ESWL success for SSD ≤8 cm and stone area ≤60 mm², in all locations except for stones in the lower kidney pole.

Conclusions: Using machine learning we have introduced the nomogram predicting single treatment success rate of an ESWL procedure. The application of this ML-nomogram in clinical practice enables the nomogram to continuously improve and refine its outputs as new data become available.