PDF(Pubmed)
Abstract：
OBJECTIVE: The purpose of the study is to construct meaningful nomogram models according to the independent prognostic factor for metastatic pancreatic cancer receiving chemotherapy.
METHODS: 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).
RESULTS: 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.
CONCLUSIONS: 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.
METHODS: 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).
RESULTS: 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.
CONCLUSIONS: 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.
摘要：
目的：本研究的目的是根据接受化疗的转移性胰腺癌的独立预后因素构建有意义的列线图模型。
方法：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。利用具有曲线下面积(AUC)的接收器工作特征(ROC)曲线来确定最佳截止值。Kaplan-Meier生存分析，利用单变量和多变量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软件以基于独立风险因素构建列线图以可视化生存。使用校准曲线和决策曲线分析(DCA)检查列线图模型。
结果：全身免疫炎症指数（SII）的最佳临界值为966.71、0.257和2.54，单核细胞与淋巴细胞比率（MLR），通过ROC分析获得中性粒细胞与淋巴细胞比率（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.我们建立了本研究主要终点的预后列线图。通过校准曲线和DCA评估了列线图的预测潜力和临床疗效。
结论：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良好的应用前景,可帮助临床医生对患者进行个性化管理.
方法：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。利用具有曲线下面积(AUC)的接收器工作特征(ROC)曲线来确定最佳截止值。Kaplan-Meier生存分析，利用单变量和多变量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软件以基于独立风险因素构建列线图以可视化生存。使用校准曲线和决策曲线分析(DCA)检查列线图模型。
结果：全身免疫炎症指数（SII）的最佳临界值为966.71、0.257和2.54，单核细胞与淋巴细胞比率（MLR），通过ROC分析获得中性粒细胞与淋巴细胞比率（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.我们建立了本研究主要终点的预后列线图。通过校准曲线和DCA评估了列线图的预测潜力和临床疗效。
结论：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良好的应用前景,可帮助临床医生对患者进行个性化管理.
