Abstract
PurposeThe aim of this study was to evaluate the prognostic significance of the systemic inflammatory response index (SIRI) in patients with bladder cancer (BCa) treated with radical cystectomy (RC) and develop a survival predictive model through establishing a nomogram.Materials and MethodsA total of 203 BCa patients who underwent RC were included in this study. The relationship between the SIRI and overall survival (OS), disease-free survival (DFS), and clinicopathological features were evaluated. Cox regression analysis was performed to investigate the effect of the factors on the OS and DFS. The results were applied in the establishment of a nomogram. Receiver operating characteristic (ROC) curves, decision curve analysis (DCA) curves, and calibration curves were performed to assess the predictive performance and accuracy of the nomogram, respectively.ResultsAccording to the classification of the SIRI, 81 patients (39.9%) were assigned to SIRI grade 1, 94 patients (46.3%) to SIRI grade 2, and the remaining 28 patients (13.8%) to SIRI grade 3. Multivariate Cox regression revealed that a higher SIRI grade was significantly associated with a poor prognosis and served as an independent prognostic factor for the OS [Grade 2 vs Grade 1, odds ratio = 2.54, 95% confidence interval (CI),1.39–4.64, P = 0.002; Grade 3 vs Grade 1, odds ratio = 4.79, 95%CI: 2.41–9.50, P < 0.001] and DFS [Grade 2 vs Grade 1, odds ratio = 2.19, 95% CI, 1.12–4.31, P = 0.023; Grade 3 vs Grade 2, odds ratio = 3.36, 95%CI, 1.53–7.35, P = 0.002]. The ROC and DCA analysis indicated that the nomogram based on the SIRI contained a better predictive performance compared with the TNM stage (AUC = 0.750 and 0.791; all P < 0.05). The ROC analysis showed that nomograms can better predict the 3- and 5-year OS and DFS. The calibration curves exhibited a significant agreement between the nomogram and the actual observation.ConclusionSIRI as a novel independent prognostic index and potential prognostic biomarker can effectively improve the traditional clinicopathological analysis and optimize individualized clinical treatments for BCa patients after RC.
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