晚期非小细胞肺癌患者经PD-1抑制剂治疗后生存预后的列线图预测模型构建  

Construction of a nomogram prediction model for survival prognosis of patients with advanced non-small cell lung cancer after PD-1 inhibitor treatment

作　　者：李雄兵[1] 周瑞芬 李佳丽 王汉姣 王超 李婧 曹喆[1] 舒诚荣[1] LI Xiongbing;ZHOU Ruifen;LI Jiali;WANG Hanjiao;WANG Chao;LI Jing;CAO Zhe;SHU Chengrong(Department of Oncology,Xianning Central Hospital/the First Hospital Affiliated to Hubei Institute of Technology,Xianning,Hubei 437000,China;Department of Transfusion,Xianning Central Hospital/the First Hospital Affiliated to Hubei Institute of Technology,Xianning,Hubei 437000,China;Department of Endocrinology,Xianning Central Hospital/the First Hospital Affiliated to Hubei Institute of Technology,Xianning,Hubei 437000,China;Department of Nephrology,Xianning Central Hospital/the First Hospital Affiliated to Hubei Institute of Technology,Xianning,Hubei 437000,China)

机构地区：[1]咸宁市中心医院/湖北科技学院附属第一医院肿瘤科,湖北咸宁437100 [2]咸宁市中心医院/湖北科技学院附属第一医院输血科,湖北咸宁437100 [3]咸宁市中心医院/湖北科技学院附属第一医院内分泌科,湖北咸宁437100 [4]咸宁市中心医院/湖北科技学院附属第一医院肾内科,湖北咸宁437100

出　　处：《国际检验医学杂志》2023年第24期2975-2979,共5页International Journal of Laboratory Medicine

基　　金：湖北省卫生健康委员会科研项目(WJ2019M095);湖北省咸宁市中心医院科研项目(2022XYB014)。

摘　　要：目的探索中性粒细胞与淋巴细胞比值(NLR)等相关指标对经程序性死亡受体1(PD-1)抑制剂治疗的晚期非小细胞肺癌患者预后的影响并构建列线图预测模型。方法选取2020年2月至2022年4月于该院接受PD-1抑制剂治疗的198例晚期非小细胞肺癌患者,随访至2022年8月,根据临床结局分为死亡组(46例)和存活组(152例)。记录死亡组和存活组患者的临床资料,分析影响经PD-1抑制剂治疗的晚期非小细胞肺癌患者预后的相关因素;采用受试者工作特征(ROC)曲线分析NLR、血小板与淋巴细胞比值(PLR)、淋巴细胞与单核细胞比值(LMR)对患者预后的预测价值;采用多因素Logistic回归模型分析影响患者预后的独立危险因素;采用R语言软件4.0“rms”包构建患者预后的列线图预测模型,校正曲线对列线图预测模型进行内部验证。结果与存活组比较,死亡组吸烟、TNM分期Ⅳ期、ECOG评分2分比例及NLR、PLR、LMR较高(P<0.05)。NLR、PLR、LMR的曲线下面积分别为0.707、0.793、0.819,最佳临界值分别为4.72%、179.21%、3.44%。吸烟、TNM分期、美国东部肿瘤协作组评分、NLR、PLR、LMR是影响PD-1抑制剂治疗的晚期非小细胞肺癌患者预后的独立危险因素(P<0.05)。内部验证结果显示,列线图模型预测经PD-1抑制剂治疗的晚期非小细胞肺癌患者预后的C-index为0.847(95%CI 0.769~0.902),校准曲线趋于理想曲线。列线图模型预测经PD-1抑制剂治疗的晚期非小细胞肺癌患者预后的阈值>0.22,列线图预测模型提供临床净收益,并且临床净收益均高于吸烟、TNM分期、ECOG评分、NLR、PLR、LMR。结论该研究基于吸烟、TNM分期、ECOG评分、NLR、PLR、LMR构建了经PD-1抑制剂治疗的晚期非小细胞肺癌患者预后的列线图预测模型,具有重要的临床应用价值。Objective To explore the effect of neutrophil to lymphocyte ratio(NLR)and other related indicators on the prognosis of advanced non-small cell lung cancer patients treated with programmed death 1(PD-1)inhibitor and construct a nomogram prediction model.Methods A total of 198 patients with advanced non-small cell lung cancer who received PD-1 inhibitor treatment in the hospital from February 2020 to April 2022 were selected and followed up to August 2022.According to the clinical outcome,they were divided into the death group(46 cases)and the survival group(152 cases).The clinical data of the death group and the survival group were recorded,and the prognostic factors of advanced non-small cell lung cancer patients treated with PD-1 inhibitor were analyzed.Receiver operating characteristic(ROC)curve was used to analyze the predictive value of NLR,platelet to lymphocyte ratio(PLR)and lymphocyte to monocyte ratio(LMR)for the prognosis of patients.Multivariate Logistic regression model was used to analyze the independent risk factors affecting the prognosis of patients.A prediction nomogram model for the prognosis of patients was constructed using R software 4.0“rms”package,and the calibration curve was used to internally validate the nomogram prediction model.Results Compared with the survival group,the proportion of smoking,TNM stageⅣ,ECOG score 2,and NLR,PLR,LMR were higher(P<0.05).The area under the curve of NLR,PLR and LMR were 0.707,0.793 and 0.819,respectively,and the optimal cut-off value were 4.72%,179.21%and 3.44%,respectively.Smoking,TNM stage,ECOG score,NLR,PLR,and LMR were independent risk factors for the prognosis of advanced non-small cell lung cancer patients treated with PD-1 inhibitor(P<0.05).The internal validation results show that the nomogram inhibitor treatment of PD-1 model prediction the prognosis of patients with advanced non-small cell lung cancer C-index was 0.847(95%CI 0.769-0.902),the calibration curve tends to be the ideal curve.The threshold value of the nomogram model for predict

关 键 词：中性粒细胞与淋巴细胞比值 程序性死亡受体1抑制剂 晚期非小细胞肺癌 列线图预测模型 

分 类 号：R734.2[医药卫生—肿瘤]