Abstract 2006P
Background
mUC presents significant treatment challenges. This study identifies key prognostic factors to enhance therapeutic strategies and overall survival outcomes. By developing a cost-effective prognostic model, we aim to improve clinical decision-making for mUC patients.
Methods
We conducted a comprehensive retrospective analysis on a cohort of 255 patients with advanced, unresectable urothelial carcinoma treated with either chemotherapy or immunotherapy across four centers from 2005 to 2023. Kaplan-Meier survival curves, the log-rank test and the Cox proportional hazards model were used. Key clinical and laboratory parameters including hemoglobin levels, lymphocyte counts, neutrophil counts, albumin levels, Eastern Cooperative Oncology Group Performance Status (ECOG PS), Prognostic Nutritional Index (PNI), and neutrophil-to-lymphocyte ratio (NLR) were examined for their association with overall survival, prior to systemic therapy initiation.
Results
The median OS was 26 months. Lower levels of hemoglobin, lymphocytes, albumin, and PNI, along with a higher NLR, were significantly associated with decreased OS. The optimal PNI cutoff was established at 44 through receiver operating characteristic (ROC) analysis. Patients with a PNI below 44 had a median OS of 23.2 months (95% CI: 11.3-35.1), while those with a PNI above 44 exhibited a median OS of 56.2 months (95% CI: 7.6-104.8). Similarly, NLR values above 3 and 5 were linked to poorer survival outcomes (p < 0.05). Additionally, a poorer ECOG PS correlated with poor prognosis. A novel prognostic model using these parameters effectively stratified patients into three risk groups—good, moderate, and poor—with median OS of 56.6 months (95% CI: 23.2-89.9), 24.8 months (95% CI: 14.9-34.7), and 14.6 months (95% CI: 4.6-24.6), respectively (p < 0.0001). Patients receiving mono-immunotherapy or chemoimmunotherapy demonstrated improved prognostic outcomes.
Conclusions
Our study validates the PNI, NLR, and new risk model as cost-effective, accessible markers for predicting survival in patients with mUC. These indicators may help stratify patients into risk groups, improving prognosis accuracy and guiding tailored treatment strategies.
Clinical trial identification
Editorial acknowledgement
Legal entity responsible for the study
The authors.
Funding
Has not received any funding.
Disclosure
All authors have declared no conflicts of interest.
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