Risk Factors and Prediction Model for Early-Onset Immune-Related Adverse Events in Pan-Cancer Patients Undergoing...

Area of Science:

• Oncology
• Immunology
• Medical Informatics

Background:

• Anti-programmed death 1 (PD-1) and anti-programmed death ligand 1 (PD-L1) immune checkpoint inhibitors (ICIs) have revolutionized cancer treatment.
• Immune-related adverse events (irAEs) pose significant challenges to the clinical benefits of ICIs.
• Predicting irAEs is essential for timely detection and management.

Purpose of the Study:

• To develop and validate a predictive model for irAEs in patients receiving anti-PD-(L)1 immunotherapy.
• To identify key clinical and laboratory parameters associated with irAE development.

Main Methods:

• Logistic least absolute shrinkage and selection operator (LASSO) regression with 10-fold cross-validation was used to select relevant variables.
• Multivariate logistic regression analysis was employed to construct the prediction model.
• Patient data from January 2019 to May 2023 were retrospectively analyzed.

Main Results:

• A total of 680 patients were included, with 330 experiencing irAEs.
• Endocrinal toxicities were the most frequent irAEs (25.59%).
• The developed risk model, incorporating basophil percentage, hemoglobin, ALC, PLR, LMR, BUN, CCI, ECOG PS, and hepatitis B status, demonstrated good predictive performance (C-index = 0.727).

Conclusions:

• The established prediction model can effectively screen and monitor patients at high risk for irAEs.
• This tool has the potential to improve outcomes for pan-cancer patients undergoing anti-PD-(L)1 therapy.