Construction and Validation of a Risk Prediction Model for Immune-Related Adverse Events in Lung Cancer Patients Receiving Immunosuppressants

OBJECTIVE: To examine the factors influencing immune-related adverse events (irAEs) in lung cancer (LC) patients undergoing treatment with immune checkpoint inhibitors (ICIs) and to develop and validate a prediction model. STUDY DESIGN: An observational study. Place and Duration of the Study: Department of Oncology, the Second People's Hospital of Datong, Shanxi, China, from January 2020 to July 2024. METHODOLOGY: The demographic characteristics and blood biomarkers of 108 LC patients treated with ICIs were evaluated retrospectively within one week before and after at least one course of therapy treatment. To compare categorical variables, χ2 or Fisher's exact test was used. Univariate and multifactorial logistic regression analyses were conducted using the forward stepwise method to determine independent predictors of irAEs. Brain metastasis (BrMs), ECOG-PS, TNM stage, IL-1 (after), IL-4 (after), IL-6 (after), IL-8 (after), IL-10 (after), WBC (after), and NLR (after) were statistically significant in the univariate logistic analysis and were then included in the multifactorial analysis. RESULTS: Univariate and multivariate logistic regression models were employed to estimate the odds ratios (OR) and their 95% confidence intervals (CI). The analysis revealed that BrMs (OR = 0.053, 95% CI: 0.009 to 0.305, p = 0.001), interleukin-10 (IL-10, OR = 1.866, 95% CI: 1.347 to 2.584, p