基于全身免疫炎症指数的有意义的列线图可以预测接受化疗的转移性胰腺癌患者的生存率。
Meaningful nomograms based on systemic immune inflammation index predicted survival in metastatic pancreatic cancer patients receiving chemotherapy.
发表日期:2024 Jul
作者:
Yanan Sun, Jiahe Hu, Rongfang Wang, Xinlian Du, Xiaoling Zhang, Jiaoting E, Shaoyue Zheng, Yuxin Zhou, Ruishu Mou, Xuedong Li, Hanbo Zhang, Ying Xu, Yuan Liao, Wenjie Jiang, Lijia Liu, Ruitao Wang, Jiuxin Zhu, Rui Xie
来源:
Best Pract Res Cl Ob
摘要:
本研究的目的是根据转移性胰腺癌接受化疗的独立预后因素构建有意义的列线图模型。本研究为回顾性研究,连续纳入2013年1月至2021年6月的143例患者。受试者工作特征(ROC)曲线为曲线下面积 (AUC) 用于确定最佳截止值。利用 Kaplan-Meier 生存分析、单变量和多变量 Cox 回归分析来确定炎症生物标志物和临床病理特征与生存的相关性。运行 R 软件根据独立风险因素构建列线图,以可视化生存情况。使用校准曲线和决策曲线分析 (DCA) 检查列线图模型。全身免疫炎症指数 (SII)、单核细胞与淋巴细胞比率 (MLR) 和中性粒细胞的最佳截止值为 966.71、0.257 和 2.54通过ROC分析获得淋巴细胞比(NLR)。 Cox 比例风险模型显示,基线 SII、饮酒史和转移部位是生存的独立预后指标。我们为本研究的主要终点建立了预后列线图。通过校准曲线和 DCA 评估了列线图的预测潜力和临床疗效。我们根据独立的预后因素构建了列线图,这些模型在临床实践中具有广阔的应用前景,可帮助临床医生对患者进行个性化管理。© 2024 )。约翰·威利出版的癌症医学
The purpose of the study is to construct meaningful nomogram models according to the independent prognostic factor for metastatic pancreatic cancer receiving chemotherapy.This study is retrospective and consecutively included 143 patients from January 2013 to June 2021. The receiver operating characteristic (ROC) curve with the area under the curve (AUC) is utilized to determine the optimal cut-off value. The Kaplan-Meier survival analysis, univariate and multivariable Cox regression analysis are exploited to identify the correlation of inflammatory biomarkers and clinicopathological features with survival. R software are run to construct nomograms based on independent risk factors to visualize survival. Nomogram model is examined using calibration curve and decision curve analysis (DCA).The best cut-off values of 966.71, 0.257, and 2.54 for the systemic immunological inflammation index (SII), monocyte-to-lymphocyte ratio (MLR), and neutrophil-to-lymphocyte ratio (NLR) were obtained by ROC analysis. Cox proportional-hazards model revealed that baseline SII, history of drinking and metastasis sites were independent prognostic indices for survival. We established prognostic nomograms for primary endpoints of this study. The nomograms' predictive potential and clinical efficacy have been evaluated by calibration curves and DCA.We constructed nomograms based on independent prognostic factors, these models have promising applications in clinical practice to assist clinicians in personalizing the management of patients.© 2024 The Author(s). Cancer Medicine published by John Wiley & Sons Ltd.