建立基于全身炎症指标预测弥漫性大B细胞淋巴瘤（DLBCL）患者总生存率的模型，并研究新模型结合炎症相关参数是否比传统模型更有效唯一预测DLBCL患者OS的临床因素。对213例DLBCL患者的临床资料进行回顾性分析。采用向后逐步Cox回归分析筛选与OS相关的独立预后因素，并根据这些因素构建预测OS的列线图。采用赤池信息准则（AIC）和贝叶斯信息准则（BIC）评价模型的拟合程度，采用一致性指数（C-index）、受试者工作特征（ROC）曲线下面积（AUC）和校准曲线来评价模型的拟合程度。评价列线图的预测准确性，并采用决策曲线分析（DCA）和Kaplan Meier曲线评价列线图的临床实用性。多因素分析证实年龄、ECOG PS评分、血清乳酸脱氢酶（LDH）水平、全身免疫炎症指标(SII) 和预后营养指数 (PNI) 用于构建列线图。列线图的AIC和BIC均低于国际预后指数（IPI）和国家综合癌症网络（NCCN）-IPI，表明列线图具有更好的拟合优度。列线图的C-index和AUC均高于IPI和NCCN-IPI，表明列线图的预测精度得到了显着提高，校准曲线显示预测结果与实际生存结果吻合较好。 DCA 显示列线图具有更好的临床净收入。 Kaplan Meier曲线显示，根据列线图评分可以很好地将患者分为低危、中危和高危组（P < 0.001）。列线图结合炎症指标可以准确预测DLBCL患者的个体生存概率。

To establish a model to predict the overall survival (OS) rate of patients with diffuse large B-cell lymphoma (DLBCL) based on systemic inflammatory indicators, and study whether the new model combined with inflammatory related parameters is more effective than the conventional model using only clinical factors to predict the OS of patients with DLBCL.The clinical data of 213 patients with DLBCL were analyzed retrospectively. Backward stepwise Cox regression analysis was used to screen independent prognostic factors related to OS, and a nomogram for predicting OS was constructed based on these factors. Akaike information criterion (AIC) and Bayesian information criterion (BIC) were used to evaluate the fitting of the model, the consistency index (C-index), area under receiver operating characteristic (ROC) curve (AUC) and calibration curve were used to evaluate the prediction accuracy of nomogram, and decision curve analysis (DCA) and Kaplan Meier curve were used to evaluate the clinical practicability of nomogram.Multivariate analysis confirmed that age, ECOG PS score, serum lactate dehydrogenase (LDH) level, systemic immune inflammatory index (SII), and prognostic nutritional index (PNI) were used to construct the nomogram. The AIC and BIC of the nomogram were lower than the International Prognostic Index (IPI) and the National Comprehensive Cancer Network (NCCN)-IPI, indicating that the nomogram had better goodness of fit. The C-index and AUC of the nomogram were higher than IPI and NCCN-IPI, indicating that the prediction accuracy of the nomogram had been significantly improved, and the calibration curve showed that the prediction results were in good agreement with the actual survival results. DCA showed that the nomogram had better clinical net income. Kaplan Meier curve showed that patients could be well divided into low-risk, medium-risk and high-risk groups according to the nomogram score (P < 0.001).The nomogram combined with inflammatory indicators can accurately predict the individual survival probability of DLBCL patients.