Table 2_Development of a lung immune prognostic index-based nomogram model for predicting overall survival and immune-related adverse events in non-small cell lung cancer patients treated with sintilimab.docx

BackgroundSintilimab, a programmed cell death protein-1 (PD-1) inhibitor, has shown efficacy in non-small cell lung cancer (NSCLC), though response heterogeneity persists. Previous studies suggest that the Lung Immune Prognostic Index (LIPI) may predict prognosis and immune-related adverse events (irAEs) in immunotherapy. This study aimed to develop and validate LIPI-based nomograms for predicting overall survival (OS) and irAEs in NSCLC patients treated with sintilimab.
MethodsMulticenter data stratified 356 patients into training, internal validation, and external validation cohorts. Propensity score matching (PSM) balanced baseline characteristics. Multivariable Cox regression identified OS and irAEs predictors, and nomograms were constructed using significant variables. Model performance was evaluated via concordance index (C-index), time-dependent receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA). Kaplan-Meier analysis assessed risk stratification.
ResultsIndependent prognostic factors for OS include clinical stage, treatment lines, LIPI scores and albumin level. Among them, stage IV (hazard ratio [HR]=1.725, 95% confidence interval [CI] 1.529-1.902), treatment line ≥2 (HR=1.302, 95%CI: 1.125-1.569), LIPI intermediate (HR=1.736, 95%CI: 1.586-1.925), LIPI poor (HR=1.568, 95% CI: 1.361-1.637) and albumin level≥35 (HR=1.802, 95%CI: 1.698-2.023) were risk factors for OS. The OS prediction model demonstrated excellent discrimination across all cohorts, with time-dependent AUCs maintaining 0.770-0.850 for 1–2 year predictions. Consistent calibration was observed (C-index: training=0.778, internal validation=0.793, external validation=0.790). For irAEs prediction, significant predictors included age, sex, Eastern Cooperative Oncology Group performance status (ECOG PS), and LIPI scores. Similarly, the irAEs model showed robust performance (AUCs 0.754-0.835 for 1–2 year predictions; C-index: training=0.805, internal validation=0.825, external validation=0.775). Both nomograms significantly outperformed single-variable predictions in Kaplan-Meier analyses. DCA confirmed superior net clinical benefit.
ConclusionLIPI-based nomograms effectively predicted OS and irAEs in sintilimab-treated NSCLC patients, offering valuable tools for personalized treatment and clinical decision-making.

研究背景：信迪利单抗（sintilimab）作为程序性死亡蛋白-1（programmed cell death protein-1, PD-1）抑制剂，已在非小细胞肺癌（non-small cell lung cancer, NSCLC）中展现出治疗疗效，但仍存在治疗应答异质性问题。既往研究表明，肺部免疫预后指数（Lung Immune Prognostic Index, LIPI）可预测免疫治疗中的患者预后与免疫相关不良事件（immune-related adverse events, irAEs）。本研究旨在构建并验证基于LIPI的列线图，用于预测接受信迪利单抗治疗的NSCLC患者的总生存期（overall survival, OS）与irAEs。
研究方法：本研究纳入多中心数据，将356例患者划分为训练集、内部验证集与外部验证集。采用倾向得分匹配（propensity score matching, PSM）均衡各组患者的基线特征。通过多变量Cox回归分析筛选OS与irAEs的预测因子，并基于具有统计学意义的变量构建列线图。通过一致性指数（concordance index, C-index）、时间依赖性受试者工作特征（receiver operating characteristic, ROC）曲线、校准曲线与决策曲线分析（decision curve analysis, DCA）评估模型性能，并采用Kaplan-Meier分析进行风险分层评估。
研究结果：OS的独立预后因素包括临床分期、治疗线数、LIPI评分与白蛋白水平。其中，IV期（风险比[HR]=1.725，95%置信区间[CI] 1.529~1.902）、治疗线数≥2（HR=1.302，95%CI：1.125~1.569）、LIPI中危（HR=1.736，95%CI：1.586~1.925）、LIPI高危（HR=1.568，95%CI：1.361~1.637）以及白蛋白水平≥35（HR=1.802，95%CI：1.698~2.023）均为OS的危险因素。OS预测模型在所有队列中均展现出优异的区分能力，1~2年预测的时间依赖性AUC值维持在0.770~0.850之间，且校准度良好（C-index：训练集=0.778，内部验证集=0.793，外部验证集=0.790）。针对irAEs预测，显著的预测因子包括年龄、性别、东部肿瘤协作组体能状态（Eastern Cooperative Oncology Group performance status, ECOG PS）与LIPI评分。同样，irAEs预测模型展现出稳定的性能（1~2年预测的AUC值为0.754~0.835；C-index：训练集=0.805，内部验证集=0.825，外部验证集=0.775）。在Kaplan-Meier分析中，两款列线图的表现均显著优于单变量预测模型，决策曲线分析证实其具有更优的净临床获益。
研究结论：基于LIPI的列线图可有效预测接受信迪利单抗治疗的NSCLC患者的OS与irAEs，为个体化治疗与临床决策提供了极具价值的工具。