Pretreatment Risk Stratification for Endoscopic Kidney-sparing Surgery in Upper Tract Urothelial Carcinoma: An International Collaborative Study

Background: Several groups have proposed features to identify low-risk patients who may benefit from endoscopic kidney-sparing surgery in upper tract urothelial carcinoma (UTUC). Objective: To evaluate standard risk stratification features, develop an optimal model to identify >= pT2/N+ stage at radical nephroureterectomy (RNU), and compare it with the existing unvalidated models. Design, setting, and participants: This was a collaborative retrospective study that included 1214 patients who underwent ureterorenoscopy with biopsy followed by RNU for nonmetastatic UTUC between 2000 and 2017. Outcome measurements and statistical analysis: We performed multiple imputation of chained equations for missing data and multivariable logistic regression analysis with a stepwise selection algorithm to create the optimal predictive model. The area under the curve and a decision curve analysis were used to compare the models. Results and limitations: Overall, 659 (54.3%) and 555 (45.7%) patients had <= pT1N0/Nx and >= pT2/N+ disease, respectively. In the multivariable logistic regression analysis of our model, age (odds ratio [OR] 1.02, 95% confidence interval [CI] 1.0-1.03, p = 0.013), high-grade biopsy (OR 1.81, 95% CI 1.37-2.40, p < 0.001), biopsy cT1+ staging (OR 3.23, 95% CI 1.93-5.41, p < 0.001), preoperative hydronephrosis (OR 1.37 95% CI 1.04-1.80, p = 0.024), tumor size (OR 1.09, 95% CI 1.01-1.17, p = 0.029), invasion on imaging (OR 5.10, 95% CI 3.32-7.81, p < 0.001), and sessile architecture (OR 2.31, 95% CI 1.58-3.36, p < 0.001) were significantly associated with >= pT2/pN+ disease. Compared with the existing models, our model had the highest performance accuracy (75% vs 66-71%) and an additional clinical net reduction (four per 100 patients). Conclusions: Our proposed risk-stratification model predicts the risk of harboring >= pT2/N+ UTUC with reliable accuracy and a clinical net benefit outperforming the current risk-stratification models. Patient summary: We develop