Liu Ya-Jiao, Sheng Li, Zhou Jing-Fen, Hua Hai-Ying
Department of Hematology, The Affiliated Hospital of Jiangnan University, Wuxi 214000, Jiangsu Province, China.
Wuxi School of Medicine, Jiangnan University, Wuxi 214000, Jiangsu Province, China.
Zhongguo Shi Yan Xue Ye Xue Za Zhi. 2024 Aug;32(4):1136-1145. doi: 10.19746/j.cnki.issn.1009-2137.2024.04.025.
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 ( < 0.001).
The nomogram combined with inflammatory indicators can accurately predict the individual survival probability of DLBCL patients.
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（OS）的模型，并研究该新模型联合炎症相关参数是否比仅使用临床因素的传统模型更有效地预测DLBCL患者的OS。
回顾性分析213例DLBCL患者的临床资料。采用向后逐步Cox回归分析筛选与OS相关的独立预后因素，并基于这些因素构建预测OS的列线图。采用赤池信息准则（AIC）和贝叶斯信息准则（BIC）评估模型的拟合度，一致性指数（C-index）、受试者操作特征曲线下面积（AUC）和校准曲线评估列线图的预测准确性，决策曲线分析（DCA）和Kaplan-Meier曲线评估列线图的临床实用性。
多因素分析证实年龄、美国东部肿瘤协作组体能状态（ECOG PS）评分、血清乳酸脱氢酶（LDH）水平、全身免疫炎症指数（SII）和预后营养指数（PNI）用于构建列线图。该列线图的AIC和BIC低于国际预后指数（IPI）和美国国立综合癌症网络（NCCN）-IPI，表明列线图具有更好的拟合优度。列线图的C-index和AUC高于IPI和NCCN-IPI，表明列线图的预测准确性显著提高，校准曲线显示预测结果与实际生存结果吻合良好。DCA显示列线图具有更好的临床净收益。Kaplan-Meier曲线显示，根据列线图评分（<0.001）可将患者很好地分为低风险、中风险和高风险组。
联合炎症指标的列线图可准确预测DLBCL患者的个体生存概率。
