Development of a New Recurrence-Free Survival Prediction Nomogram for Patients with Primary Non-Muscle-Invasive Bladder Cancer Based on Preoperative Controlling Nutritional Status Score.

Background: Bladder cancer is the second most prevalent neoplasm in the urogenital system in terms of morbidity and mortality, and there is an urgent need for a more accurate assessment of individual prognosis in patients with primary non-muscle-invasive bladder cancer (NMIBC). The Controlling Nutritional Status (CONUT) score is an emerging biomarker score which has been confirmed to have prognostic value in various malignant tumors. The study attempted to systematically identify the prognostic role of preoperative CONUT score on posttreatment recurrence-free survival (RFS) in patients with NMIBC, and determine the predictive value and feasibility of the new prognostic prediction model.

Methods: A total of 94 patients with NMIBC were analyzed retrospectively between January 2011 and December 2015. Statistical analysis was conducted using the nonparametric method. The Kaplan--Meier method was used to assess recurrence-free survival (RFS), and Log rank tests was used to analyze the equivalences of survival curves. We used univariate and multivariate Cox proportional hazards regression model to identify important predictors of RFS. Discrimination of nomogram was measured by the concordance index. Predictive accuracy of the model was evaluated using the internal validation.

Results: In univariate analysis, age, history of smoking, pathological T stage, tumor grade, tumor size, and CONUT score were significantly correlated with RFS. Multivariate analysis indicated that CONUT score (HR =3.855, 95% CI 1.242-11.970, p=0.020) was an independent predictor of RFS in patients with NMIBC. Based on significant parameters in multivariate analysis and reliable recurrence predictors determined in predictive models and relevant guidelines, a new age-, history of smoking-, pathologic factors- and the CONUT score-based scoring model was developed to predict recurrence of NMBIC. In addition, we internally validated the nomogram using the consistency index and calibration plots, which demonstrated that the model has high prediction accuracy (c-index= 0.851).

Conclusion: The development of a new nomogram based on CONUT score could increase the accuracy of recurrence prediction and improve individualized treatment plans for patients with NMIBC.