PDF(Pubmed)
Abstract：
UNASSIGNED: The purpose of this study was to investigate the predictive value of Pan-Immune-Inflammation Value (PIV) combined with the PILE score for immunotherapy in patients with advanced non-small cell lung cancer (NSCLC) and to construct a nomogram prediction model to provide reference for clinical work.
UNASSIGNED: Patients with advanced NSCLC who received ICIs treatment in Qingdao Municipal Hospital from January 2019 to December 2021 were selected as the study subjects. The chi-square test, Kaplan-Meier survival analysis, and Cox proportional risk regression analysis were used to evaluate the prognosis. The results were visualized by a nomogram, and the performance of the model was judged by indicators such as the area under the subject operating characteristic curve (AUC) and C-index. The patients were divided into high- and low-risk groups by PILE score, and the prognosis of patients in different risk groups was evaluated.
UNASSIGNED: Multivariate Cox regression analysis showed that immune-related adverse events (irAEs) were prognostic factors for overall survival (OS) improvement, and ECOG PS score ≥2, bone metastases before treatment, and high PIV expression were independent risk factors for OS. The C index of OS predicted by the nomogram model is 0.750 (95% CI: 0.677-0.823), and the Calibration and ROC curves show that the model has good prediction performance. Compared with the low-risk group, patients in the high-risk group of PILE were associated with a higher inflammatory state and poorer physical condition, which often resulted in a poorer prognosis.
UNASSIGNED: PIV can be used as a prognostic indicator for patients with advanced NSCLC treated with ICIs, and a nomogram prediction model can be constructed to evaluate the survival prediction of patients, thus contributing to better clinical decision-making and prognosis assessment.
UNASSIGNED: Patients with advanced NSCLC who received ICIs treatment in Qingdao Municipal Hospital from January 2019 to December 2021 were selected as the study subjects. The chi-square test, Kaplan-Meier survival analysis, and Cox proportional risk regression analysis were used to evaluate the prognosis. The results were visualized by a nomogram, and the performance of the model was judged by indicators such as the area under the subject operating characteristic curve (AUC) and C-index. The patients were divided into high- and low-risk groups by PILE score, and the prognosis of patients in different risk groups was evaluated.
UNASSIGNED: Multivariate Cox regression analysis showed that immune-related adverse events (irAEs) were prognostic factors for overall survival (OS) improvement, and ECOG PS score ≥2, bone metastases before treatment, and high PIV expression were independent risk factors for OS. The C index of OS predicted by the nomogram model is 0.750 (95% CI: 0.677-0.823), and the Calibration and ROC curves show that the model has good prediction performance. Compared with the low-risk group, patients in the high-risk group of PILE were associated with a higher inflammatory state and poorer physical condition, which often resulted in a poorer prognosis.
UNASSIGNED: PIV can be used as a prognostic indicator for patients with advanced NSCLC treated with ICIs, and a nomogram prediction model can be constructed to evaluate the survival prediction of patients, thus contributing to better clinical decision-making and prognosis assessment.
摘要：
■本研究旨在探讨泛免疫-炎症值(PIV)联合PILE评分对晚期非小细胞肺癌(NSCLC)患者免疫治疗的预测价值,并构建列线图预测模型,为临床工作提供参考。
■选择2019年1月至2021年12月在青岛市市立医院接受ICIs治疗的晚期非小细胞肺癌患者作为研究对象。卡方检验,Kaplan-Meier生存分析，采用Cox比例风险回归分析评估预后。结果通过列线图可视化，并通过受试者工作特性曲线下面积(AUC)和C指数等指标判断模型的性能。根据PILE评分将患者分为高危组和低危组,评估不同风险组患者的预后。
■多因素Cox回归分析显示，免疫相关不良事件（irAEs）是总生存期（OS）改善的预后因素，ECOGPS评分≥2分，治疗前骨转移，高PIV表达是OS的独立危险因素。通过列线图模型预测的OS的C指数为0.750（95％CI：0.677-0.823），标定曲线和ROC曲线表明该模型具有良好的预测性能。与低风险组相比，PILE高危人群的患者与较高的炎症状态和较差的身体状况相关，这通常导致预后较差。
■PIV可作为ICIs治疗晚期NSCLC患者的预后指标，并且可以构建一个列线图预测模型来评估患者的生存预测，从而有助于更好的临床决策和预后评估。
■选择2019年1月至2021年12月在青岛市市立医院接受ICIs治疗的晚期非小细胞肺癌患者作为研究对象。卡方检验,Kaplan-Meier生存分析，采用Cox比例风险回归分析评估预后。结果通过列线图可视化，并通过受试者工作特性曲线下面积(AUC)和C指数等指标判断模型的性能。根据PILE评分将患者分为高危组和低危组,评估不同风险组患者的预后。
■多因素Cox回归分析显示，免疫相关不良事件（irAEs）是总生存期（OS）改善的预后因素，ECOGPS评分≥2分，治疗前骨转移，高PIV表达是OS的独立危险因素。通过列线图模型预测的OS的C指数为0.750（95％CI：0.677-0.823），标定曲线和ROC曲线表明该模型具有良好的预测性能。与低风险组相比，PILE高危人群的患者与较高的炎症状态和较差的身体状况相关，这通常导致预后较差。
■PIV可作为ICIs治疗晚期NSCLC患者的预后指标，并且可以构建一个列线图预测模型来评估患者的生存预测，从而有助于更好的临床决策和预后评估。
