The Prognostic Nutritional Index Serves as a Novel and Independent Predictor of Recurrence in Patients with...

This study aimed to evaluate the prognostic significance of preoperative inflammation-based biomarkers, with a particular focus on the Prognostic Nutritional Index (PNI), in predicting recurrence-free survival (RFS) in patients with non-muscle-invasive bladder cancer (NMIBC). We conducted a retrospective analysis of 210 patients with primary NMIBC diagnosed between January 2019 and June 2020. Various inflammatory indices were calculated from preoperative laboratory data, including PNI, neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), lymphocyte-to-monocyte ratio (LMR), systemic immune-inflammation index (SII), neutrophil-to-platelet ratio (NPR), and albumin-to-globulin ratio (AGR). The optimal PNI cutoff was determined using receiver operating characteristic (ROC) curve analysis for 2-year recurrence. Univariable and multivariable Cox proportional hazards models were employed to identify independent prognostic factors for RFS. Survival curves were generated using the Kaplan-Meier method and compared with the log-rank test. A restricted cubic spline (RCS) model was used to examine the dose-response relationship between PNI and recurrence risk. The optimal PNI cutoff for recurrence was 47.5, with an area under the curve (AUC) of 0.816. Multivariable analysis identified high pathological grade (adjusted hazard ratio [HR]: 2.57; 95% confidence interval [CI]: 1.37-4.81; p = 0.003) and low PNI (≤47.5) (adjusted HR: 2.40; 95% CI: 1.31-4.40; p = 0.005) as independent risk factors for reduced RFS. PNI exhibited high discriminative performance, and DeLong pairwise comparisons confirmed its AUC was significantly higher than all other evaluated biomarkers (all p < 0.001). Incorporating PNI into the EAU risk model significantly improved model fit (likelihood-ratio test p = 0.008) and increased the C-index from 0.68 to 0.74. Kaplan-Meier analysis confirmed that patients with low PNI had significantly worse RFS than those with high PNI (log-rank p = 0.005). The RCS model revealed a significant linear inverse relationship between PNI and recurrence risk (P for overall association < 0.001; P for nonlinearity = 0.214). These findings suggest that preoperative PNI is a promising biomarker for recurrence risk stratification in NMIBC, with good prognostic discrimination in this cohort, although external validation is required.