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

Background: With the extensive utilization of immune checkpoint inhibitors (ICIs) across various cancers, ICIs-related thyroid dysfunction (ICI-TD) has become a growing concern in clinical practice. This study aimed to devise an individualized management strategy for ICI-TD to enhance the early identification and proactive management in cancer patients.

Methods: We designed and conducted a three-phase study. Initially, we analyzed the influencing factors through a systematic review and meta-analysis, which adhered to the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines. Moreover, the study protocol was registered with PROSPERO (CRD42019131133). Subsequently, prediction models for ICI-TD were developed utilizing 11 algorithms based on the real-world cohort data from July 20, 2018 (the approval date of the first ICIs, Pembrolizumab in China), to October 31, 2022. Considering discrimination, calibration, and clinical utility, we selected the model with the best performance for web calculator development. Finally, individualized management strategies for ICI-TD were proposed by combining evidence-based analysis with practical considerations.

Results: The systematic review encompassed 21 observational studies involving 4,145 patients, revealing associations between ICI-TD and factors such as female gender, age, receipt of Pembrolizumab (versus other ICIs), and baseline levels of thyroid-stimulating hormone, free thyroxine, and antithyroid antibodies. In the prediction model development phase, 621 participants were enrolled, with 36 patients developing ICI-TD. The model based on the LightGBM algorithm demonstrated superior performance, leading to the development of a web calculator. Based on these findings and existing guidelines, individualized monitoring and treatment pathways for pharmacists were devised.

Conclusion: This study offers comprehensive insights into managing ICI-TD, potentially enhancing tailored cancer immunotherapy management.