Search

OBJECTIVE To build the Wisconsin Stone Quality of Life Machine-Learning Algorithm (WISQOL-MLA) to predict urolithiasis patients' health-related quality of life (HRQoL) based on demographic, symptomatic and clinical data collected for the validation of the Wisconsin Stone Quality-of-Life (WISQOL) questionnaire, an HRQoL measurement tool designed specifically for patients with kidney stones. MATERIAL AND METHODS We used data from 3206 stone patients from 16 centres. We used gradient-boosting and deep-learning models to predict HRQoL scores. We also stratified HRQoL scores by quintile. The dataset was split using a standard 70%/10%/20% training/validation/testing ratio. Regression performance was evaluated using Pearson's correlation. Classification was evaluated with an area under the receiver-operating characteristic curve (AUROC). RESULTS Gradient boosting obtained a test correlation of 0.62. Deep learning obtained a correlation of 0.59. Multivariate regression achieved a correlation of 0.44. Quintile stratification of all patients in the WISQOL dataset obtained an average test AUROC of 0.70 for the five classes. The model performed best in identifying the lowest (0.79) and highest quintiles (0.83) of HRQoL. Feature importance analysis showed that the model weighs in clinically relevant factors to estimate HRQoL, such as symptomatic status, body mass index and age. CONCLUSIONS Harnessing the power of the WISQOL questionnaire, our initial results indicate that the WISQOL-MLA can adequately predict a stone patient's HRQoL from readily available clinical information. The algorithm adequately relies on relevant clinical factors to make its HRQoL predictions. Future improvements to the model are needed for direct clinical applications.

Renal colic encounters are common; in the United States alone, they represent greater than one million annual emergency department (ED) visits. Most of these stones are managed conservatively with a trial of passage. However, some lead to repeat colic episodes, secondary ED visits, increased anxiety, and increased cost. Of the 5%–10% of symptomatic stones that become lodged at the ureteropelvic junction and are larger than 5 mm, most require operative intervention. In the process of executing a NASA‐funded study of ultrasonic repositioning of kidney stones, the subject was administered fluid to dilate the collecting system, placed in Trendelenburg bed positioning, and rolled to both sides. During this process a symptomatic, obstructing 9‐mm ureteropelvic junction stone moved back into the kidney's lower pole/infundibulum and symptoms were immediately resolved. The patient remained asymptomatic for a period of 5 weeks at which point elective intervention was scheduled. This case demonstrates that ureteropelvic junction stones may be repositioned in a non‐invasive manner, turning a stone that requires urgent intervention into one that can be managed electively.

BACKGROUND The delivery of urologic oncology care is susceptible to regional variation. In the current study, the authors sought to define patterns of care for patients undergoing genitourinary cancer surgery to identify underserved areas for urologic cancer care in Washington State. METHODS The authors accessed the Washington State Comprehensive Hospital Abstract Reporting System from 2003 through 2007. They identified patients undergoing radical prostatectomy, radical cystectomy (RC), partial nephrectomy (PN), radical nephrectomy, and transurethral resection of the prostate (TURP). TURP was included for comparison as a reference procedure indicative of access to urologic care. Hospital service areas (HSAs) are where the majority of local patients are hospitalized; hospital referral regions (HRR) are where most patients receive tertiary care. The authors created multivariate hierarchical logistic regression models to examine patient and HSA characteristics associated with the receipt of urologic oncology care out of the HRR for each procedure. RESULTS Greater than one-half of patients went out of their HRR in 7 HSAs (11%) for radical prostatectomy, 3 HSAs (5%) for radical nephrectomy, 10 HSAs (15%) for PN, and 14 HSAs (22%) for RC. No HSAs had high export rates for TURP. Few patient factors were found to be associated with surgical care out of the HRR. High-export HSAs for PN and RC exhibited lower socioeconomic characteristics than low-export HSAs, adjusting for HSA population, race, and HSA procedure rates for PN and RC. CONCLUSIONS Patients living in areas with lower socioeconomic status have a greater need to travel for complex urologic surgery. Consideration of geographic delineation in the delivery of urologic oncology care may aid in regional quality improvement initiatives. Cancer 2014;120:1565–1571. © 2014 American Cancer Society.