目的 探讨术前预后营养指数(PNI) 对肾癌患者复发的临床评估价值,并且构建Nomogram模型,为肾癌患者的预后以及临床相关决策提供参考性价值。方法 回顾性收集从2014年1月至2018年12月之间因肾癌在瑞安市人民医院行手术治疗的723例患者的病历资料,并对患者进行随访。通过受试者操作特征曲线(ROC曲线)分析得到最佳截断点,根据PNI的最佳截断点,分为低PNI组(PNI＜48.55组,n=318),高PNI组(PNI≥48.55组,n=405)。探讨术前PNI对肾癌患者复发的临床评估价值,并且构建Nomogram模型。结果 ROC曲线提示,年龄、肿瘤直径、PNI、体质指数的曲线下面积(AUC)分别为0.610、0.866、0.755、0.654,最佳截断点分别为69.5、4.15、48.55、25.37。总共123例患者复发,PNI＜48.55组患者的中位无复发生存时间为52个月,术后1年的复发率2.07%,术后3年的复发率6.22%,术后5年的复发率17.01%,PNI<48.55组的肾癌患者术后无复发生存率低于PNI≥48.55组患者(P＜0.05)。单因素Cox比例风险回归模型分析提示年龄、PNI、体质指数、临床表现、肿瘤直径、Fuhrman分级、术后肿瘤转移、病理类型、病理分期是肾癌患者术后复发的相关影响因素(P＜0.05),其中PNI≥48.55为保护因素。Nomogram模型C-index为0.871(95%CI=0.824,0.912),显著高于TNM分期(C-index=0.725)、MSKCC模型(C-index=0.743)、IMDC模型(C-index=0.751),NRI=0.327、IDI=0.185,提示模型预测能力显著提升;校正曲线Hosmer-Lemeshow检验P＞0.05,校准度良好;DCA显示概率阈值0.10~0.90时模型具有临床净收益。Fine-Gray竞争风险模型验证结果与Cox回归分析一致,提示非肿瘤死亡竞争风险未显著改变复发风险估计。结论 术前PNI降低与肾癌患者不良预后相关,本研究构建的Nomogram模型预测效能优于传统TNM分期、MSKCC及IMDC模型,可准确个体化预测术后复发风险。
Abstract
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.
关键词
预后营养指数 /
肾癌 /
复发 /
预后预测 /
预后价值 /
Nomogram模型 /
列线图 /
生存分析
Key words
Prognostic nutritional index /
Renal cell carcinoma /
Recurrence /
Prognostic prediction /
Prognostic value /
Nomogram model /
Nomogram /
Survival analysis
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