Liu Yajiao, Sheng Li, Hua Haiying, Zhou Jingfen, Zhao Ying, Wang Bei
Wuxi School of Medicine, Jiangnan University, Wuxi, Jiangsu Province, 214000, People's Republic of China.
Department of Hematology, Affiliated Hospital of Jiangnan University, Wuxi, Jiangsu Province, 214000, People's Republic of China.
Cancer Manag Res. 2023 Jul 13;15:651-666. doi: 10.2147/CMAR.S408100. eCollection 2023.
We aimed to create a novel prognostic score, the inflammation-based prognosis score (IBPS). In addition, we attempted to establish and validate a nomogram to predict the overall survival (OS) of patients with DLBCL.
We retrospectively investigated the data of 213 patients with DLBCL diagnosed and treated in the Affiliated Hospital of Jiangnan University and used these data to develop nomograms. At the same time, 89 patients diagnosed and treated in Wuxi People's Hospital Affiliated with Nanjing Medical University from January 2015 to June 2021 were collected as an external validation cohort. We developed IBPS through the least absolute shrinkage and selection operator (LASSO) Cox regression. The univariate and multivariate Cox regression method was used to develop the nomogram. We used the concordance index (C-index), calibration chart, time-dependent receiver operating characteristic (ROC) analysis, decision curve analysis (DCA), and the Kaplan-Meier curve were used to assess the nomogram.
The systemic immune inflammation index (SII), prognostic nutrition index (PNI), and modified Glasgow prognostic score (mGPS) were used to construct IBPS. The Eastern Cooperative Oncology Group performance status (ECOG PS), IBPS, response to treatment, and whether accept surgery were used to construct the nomogram to predict the OS of DLBCL patients. The C-index in the training and validation cohorts were 0.844 and 0.828, respectively. According to the time-dependent ROC curve and DCA, the nomogram has good predictive accuracy and clinical net benefit. The Kaplan-Meier curve showed that according to the nomogram score, patients in the training and validation cohorts could be classified into three risk groups.
In patients with DLBCL, baseline IBPS was a reliable predictor of OS. The survival probability of DLBCL patients can be precisely predicted using the prognosis nomogram based on IBPS.
我们旨在创建一种新的预后评分,即基于炎症的预后评分(IBPS)。此外,我们试图建立并验证一种列线图,以预测弥漫性大B细胞淋巴瘤(DLBCL)患者的总生存期(OS)。
我们回顾性研究了江南大学附属医院诊断和治疗的213例DLBCL患者的数据,并使用这些数据来制定列线图。同时,收集了2015年1月至2021年6月在南京医科大学附属无锡人民医院诊断和治疗的89例患者作为外部验证队列。我们通过最小绝对收缩和选择算子(LASSO)Cox回归开发了IBPS。使用单变量和多变量Cox回归方法来制定列线图。我们使用一致性指数(C指数)、校准图、时间依赖性受试者工作特征(ROC)分析、决策曲线分析(DCA)以及Kaplan-Meier曲线来评估列线图。
使用全身免疫炎症指数(SII)、预后营养指数(PNI)和改良格拉斯哥预后评分(mGPS)构建IBPS。使用东部肿瘤协作组体能状态(ECOG PS)、IBPS、治疗反应以及是否接受手术来构建列线图以预测DLBCL患者的OS。训练队列和验证队列中的C指数分别为0.844和0.828。根据时间依赖性ROC曲线和DCA,列线图具有良好的预测准确性和临床净效益。Kaplan-Meier曲线显示,根据列线图评分,训练队列和验证队列中的患者可分为三个风险组。
在DLBCL患者中,基线IBPS是OS的可靠预测指标。使用基于IBPS的预后列线图可以精确预测DLBCL患者的生存概率。