TY - JOUR
T1 - Pretreatment Risk Stratification for Endoscopic Kidney-sparing Surgery in Upper Tract Urothelial Carcinoma
T2 - An International Collaborative Study[Formula presented]
AU - Foerster, Beat
AU - Abufaraj, Mohammad
AU - Matin, Surena F.
AU - Azizi, Mounsif
AU - Gupta, Mohit
AU - Li, Wei Ming
AU - Seisen, Thomas
AU - Clinton, Timothy
AU - Xylinas, Evanguelos
AU - Mir, M. Carmen
AU - Schweitzer, Donald
AU - Mari, Andrea
AU - Kimura, Shoji
AU - Bandini, Marco
AU - Mathieu, Romain
AU - Ku, Ja H.
AU - Marcq, Gautier
AU - Guruli, Georgi
AU - Grabbert, Markus
AU - Czech, Anna K.
AU - Muilwijk, Tim
AU - Pycha, Armin
AU - D'Andrea, David
AU - Petros, Firas G.
AU - Spiess, Philippe E.
AU - Bivalacqua, Trinity
AU - Wu, Wen Jeng
AU - Rouprêt, Morgan
AU - Krabbe, Laura Maria
AU - Hendricksen, Kees
AU - Egawa, Shin
AU - Briganti, Alberto
AU - Moschini, Marco
AU - Graffeille, Vivien
AU - Kassouf, Wassim
AU - Autorino, Riccardo
AU - Heidenreich, Axel
AU - Chlosta, Piotr
AU - Joniau, Steven
AU - Soria, Francesco
AU - Pierorazio, Phillip M.
AU - Shariat, Shahrokh F.
N1 - Publisher Copyright:
© 2021 The Author(s)
PY - 2021/10
Y1 - 2021/10
N2 - 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 developed a risk stratification model to better identify patients for endoscopic kidney-sparing surgery in upper tract urothelial carcinoma.
AB - 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 developed a risk stratification model to better identify patients for endoscopic kidney-sparing surgery in upper tract urothelial carcinoma.
KW - Kidney-sparing surgery
KW - Radical nephroureterectomy
KW - Risk stratification
KW - Upper tract urothelial carcinoma
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U2 - 10.1016/j.eururo.2021.05.004
DO - 10.1016/j.eururo.2021.05.004
M3 - Article
C2 - 34023164
AN - SCOPUS:85106389137
SN - 0302-2838
VL - 80
SP - 507
EP - 515
JO - European urology
JF - European urology
IS - 4
ER -