Immune checkpoint inhibitors-related thyroid dysfunction: influencing factor analysis, prediction model development,...

Area of Science:

• Oncology
• Endocrinology
• Pharmacology

Background:

• Immune checkpoint inhibitors (ICIs) are widely used in cancer therapy.
• ICI-related thyroid dysfunction (ICI-TD) is an emerging clinical challenge.
• Early identification and management of ICI-TD are crucial for cancer patients.

Purpose of the Study:

• To develop an individualized management strategy for ICI-TD.
• To enhance early detection and proactive management of ICI-TD in cancer patients.
• To identify key factors influencing ICI-TD.

Main Methods:

• Systematic review and meta-analysis following PRISMA guidelines.
• Development of prediction models for ICI-TD using 11 algorithms on real-world data (July 2018 - October 2022).
• Selection of the best-performing model for web calculator development and proposal of individualized management strategies.

Main Results:

• Systematic review of 21 studies (4,145 patients) identified associations between ICI-TD and female gender, age, Pembrolizumab use, and baseline thyroid hormone levels.
• A prediction model using the LightGBM algorithm showed superior performance in a cohort of 621 participants (36 ICI-TD cases).
• A web calculator and individualized management pathways for pharmacists were developed.

Conclusions:

• This study provides valuable insights for managing ICI-TD.
• The developed tools and strategies can enhance tailored cancer immunotherapy management.
• Proactive monitoring and management can improve outcomes for patients on ICIs.