BackgroundPretreatment controlling nutritional status (CONUT) score is a novel index which was used to predict outcomes in cancer patients. We aim to explore the prognostic significance of CONUT score in patients with upper tract urothelial carcinoma (UTUC) after radical nephroureterectomy (RNU).Patients and methodsA total of 662 UTUC patients between 2004 and 2016 were retrospectively analyzed. Patients were categorized into three groups based on CONUT score (Normal: 0‐1; Light: 2‐4; Moderate/severe: 5‐12). Associations of CONUT score with oncological outcomes were analyzed using Logistic and Cox regression analysis. Harrell concordance index was used to assess the predictive accuracy of the multivariate models. Subgroup analyses were conducted according to tumor grade and stage.ResultsThe median follow‐up duration was 41 months. Multivariate Logistic analysis showed that high CONUT score was independently associated with high‐grade disease, high pT stage, lymphovascular invasion, sessile carcinoma, variant histology, and positive surgical margins (each P < 0.05). Multivariate analysis demonstrated that CONUT score 5‐12 was an independent factor for worse cancer‐specific survival (CSS, hazard ratio [HR]:2.39, 95% confidence interval [CI] 1.55‐3.68, P < 0.0001), disease recurrence‐free‐survival (RFS, HR: 1.80, 95% CI 1.24‐2.60, P = 0.002), and overall survival (OS, HR: 2.26, 95% CI 1.53‐3.34, P < 0.0001). The estimated c‐index of the multivariate models for CSS, RFS, and OS increased from 0.755, 0.715 and 0.745 to 0.772, 0.723, and 0.756 when CONUT score supplemented. Subgroup analyses showed that especially in patients with high‐grade carcinoma and advanced stage (≥pT3), higher CONUT score predicts decreased CSS, RFS, and OS (all P < 0.05).ConclusionPreoperative CONUT score is a negative independent prognostic indicator for both pathologic and survival outcomes in UTUC, especially in those with high‐grade carcinoma and advanced stage. Adding this parameter into our clinical prediction model is appropriate so as to improve its predictive accuracy.