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

Jian Xu¹tingting peng²fan kaikai³dou yuxiao⁴Lingti Kong^1*Ran Sang^1*

• ¹Department of Pharmacy, The First Affiliated Hospital of Bengbu Medical College, Bengbu, China
• ²Department of Spinal Surgery, The First Affiliated Hospital of Bengbu Medical University, Bengbu, bengbu, China
• ³Department of Pharmacy, Suzhou Municipal Hospital, suzhou, China
• ⁴Department of Pharmacy, Hefei Eighth People's Hospital, hefei, China

Background: Sintilimab, 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.: Multicenter 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), timedependent receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA). Kaplan-Meier analysis assessed risk stratification. Results: Independent 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.LIPI-based nomograms effectively predicted OS and irAEs in sintilimab-treated NSCLC patients, offering valuable tools for personalized treatment and clinical decision-making.