Shockwave Lithotripsy Is an Efficacious Treatment Modality for Obese Patients with Upper Ureteral Calculi: Logistic Regression and Matched-Pair Analyses from a Dedicated Center Comparing Treatment Outcomes by Skin-to-Stone Distance

Stone decision engine accurately predicts stone removal and treatment complications for shock wave lithotripsy and laser ureterorenoscopy patients

Kidney stones form when mineral salts crystallize in the urinary tract. While most stones exit the body in the urine stream, some can block the ureteropelvic junction or ureters, leading to severe lower back pain, blood in the urine, vomiting, and painful urination. Imaging technologies, such as X-rays or ureterorenoscopy (URS), are typically used to detect kidney stones. Subsequently, these stones are fragmented into smaller pieces using shock wave lithotripsy (SWL) or laser URS. Both treatments yield subtly different patient outcomes. To predict successful stone removal and complication outcomes, Artificial Neural Network models were trained on 15,126 SWL and 2,116 URS patient records. These records include patient metrics like Body Mass Index and age, as well as treatment outcomes obtained using various medical instruments and healthcare professionals. Due to the low number of outcome failures in the data (e.g., treatment complications), Nearest Neighbor and Synthetic Minority Oversampling Technique (SMOTE) models were implemented to improve prediction accuracies. To reduce noise in the predictions, ensemble modeling was employed. The average prediction accuracies based on Confusion Matrices for SWL stone removal and treatment complications were 84.8% and 95.0%, respectively, while those for URS were 89.0% and 92.2%, respectively. The average prediction accuracies for SWL based on Area-Under-the-Curve were 74.7% and 62.9%, respectively, while those for URS were 77.2% and 78.9%, respectively. Taken together, the approach yielded moderate to high accurate predictions, regardless of treatment or outcome. These models were incorporated into a Stone Decision Engine web application (http://peteranoble.com/webapps.html) that suggests the best interventions to healthcare providers based on individual patient metrics.

Ureteral wall thickness is an independent parameter affecting the success of extracorporeal shock wave lithotripsy treatment in ureteral stones above the iliac crest

BACKGROUND

To investigate the relationship between ureteral wall thickness (UWT) and other variables of patients who underwent extracorporeal shock wave lithotripsy (SWL) in the primary treatment of the ureteral stone above the iliac crest level.

MATERIAL AND METHODS

A total of 147 patients aged 18 years and older, who underwent SWL in our clinic between December 2016 and December 2019 for the treatment of ureteral stones above the iliac crest level and had non-contrast enhanced abdominal computed tomography scans before the procedure were included in the clinical study. The results were evaluated at 3 months after SWL. The absence of residual fragments was considered as stone-free status, and the existence of any size residual fragment was considered as treatment failure.

RESULTS

In our study, the mean age of the patients was 42.4 ± 12.8 years, and the stone-free rate was 92.5%. The median transverse stone size was 7.5 mm (min 2.8-max 15), and the median UWT was 4.2 mm (1-8.7). In the multivariate analysis, UWT (P = .002) and multiple stone presence (P = .027) were found to be independent factors affecting stone-free status. In the receiver operating characteristic curve analysis, the optimal threshold value for UWT was determined as 5.25 mm.

CONCLUSIONS

We found that UWT was the most important independent variable associated with increased failure in SWL treatment. The presence of multiple stones was another independent factor that increased the failure rates. Using SWL technology through experience accumulated with the mechanical hardware of the machine, we can select patients who are more suitable for this treatment and improve treatment outcomes.