Objective To explore the clinical evaluation value of preoperative prognostic nutritional index (PNI) for the recurrence of renal cancer patients, and to construct a Nomogram model to provide reference value for the prognosis and clinical decision-making of renal cancer patients. Method The medical records of 723 patients who underwent surgical treatment for renal cell carcinoma at Ruian People's Hospital from January 2014 to December 2018 were retrospectively collected and followed up. Perform receiver operating characteristic (ROC)curve analysis on the data to obtain the optimal cutoff value, and use the optimal cutoff value for corresponding analysis. According to the optimal cutoff value of PNI, they were divided into low PNI group (n=318) and high PNI group (n=405). To explore the clinical evaluation value of preoperative PNI for the recurrence of renal cancer patients, and to construct a Nomogram model. Results The results showed that the working characteristic curve (ROC curve) indicated that the AUC values for age, tumor diameter, PNI, and BMI were 0.610, 0.866, 0.755, and 0.654 respectively, and the optimal cut-off values were 69.5, 4.15, 48.55, and 25.37 respectively. A total of 123 patients experienced recurrence. The median recurrence-free survival time for patients with PNI < 48.55 was 52 months, with a 1-year recurrence rate of 2.07%, a 3-year recurrence rate of 6.22%, and a 5-year recurrence rate of 17.01%. The postoperative recurrence-free survival rate of patients with PNI < 48.55 was lower than that of patients with PNI ≥ 48.55 (P<0.05). Univariate Cox regression analysis indicated that age, PNI, BMI, clinical manifestations, tumor diameter, Fuhrman grade, postoperative tumor metastasis, pathological type, and pathological stage were related influencing factors for postoperative recurrence in patients with renal cancer (P<0.05), among which PNI ≥ 48.55 was a protective factor. The C-index of the Nomogram model was 0.871 (95%CI=0.824-0.912), significantly higher than that of the TNM staging (C-index=0.725), the MSKCC model (C-index=0.743), and the IMDC model (C-index=0.751). NRI=0.327, IDI=0.185, indicating a significant improvement in the predictive ability of the model; the Hosmer-Lemeshow test for the calibration curve P>0.05, and the calibration was good; DCA showed that the model had clinical net benefits when the probability threshold was 0.10 to 0.90. The Fine-Gray competing risk model verification results were consistent with the Cox regression, suggesting that the non-tumor death competing risk did not significantly change the recurrence risk estimation. Conclusion A lower preoperative PNI is associated with a poorer prognosis in patients with renal cancer. The Nomogram model constructed in this study has a higher predictive efficacy than the traditional TNM, MSKCC, and IMDC models, and can accurately and individually predict the postoperative recurrence risk. Clinically.
Key words
Prognostic nutritional index /
Renal cell carcinoma /
Recurrence /
Prognostic prediction /
Prognostic value /
Nomogram model /
Nomogram /
Survival analysis
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