Abstract 1284P
Background
Immune checkpoint inhibitors (ICIs) are one of the main pillars of cancer therapy. Since the adverse events (AEs) of ICIs differ from those of non-ICIs, it is important to investigate the safety profile of ICIs using real-world data. We aimed to identify incidence of ICI-related AEs in ICI users compared to AEs in non-ICI users and to detect and evaluate signals using real-world data.
Methods
Cancer patients treated with anticancer medications were analyzed using the nationwide health insurance claims database from 2015 to 2019, and a tertiary care hospital database from 2012 to 2019. ICI users were compared with non-ICI users. PD-1 inhibitors (nivolumab and pembrolizumab) and PD-L1 inhibitors (atezolizumab) were evaluated. We analyzed immune-related AEs (irAEs) using all diagnostic codes after ICI prescription utilizing nationwide health insurance claims database. A signal was defined as an AE that met all 4 indices of data mining: hazard ratio (HR), proportional claims ratio (PCR), claims odds ratio (COR), and information component (IC). All detected signals were reviewed and classified into well-known or potential irAEs, which were severe or unknown. Second category signals were verified from hospital electronic medical record (EMR) using diagnostic codes and text mining.
Results
We identified 141 different signals of ICI users defined using diagnostic codes, and were compared with those of non-ICI users. We detected 37 well-known irAEs, most of which were endocrine diseases and skin diseases. We also detected 24 potential irAEs related to disorders in the nervous system, eye, circulatory system, digestive system, skin and subcutaneous tissues, and bones. Especially, portal vein thrombosis and bone disorders such as osteoporosis with pathological fracture and fracture of shoulder, upper arm, femur, and lower leg showed high HR in ICI users than in non-ICI users. The signals from hospital database were verified using diagnostic codes and text mining.
Conclusions
This real-world data analysis demonstrated an efficient approach for signal detection and evaluation of ICI use. An effective real-world pharmacovigilance system of the nationwide claims database and the EMR could complement each other in detecting significant AE signals.
Clinical trial identification
Editorial acknowledgement
Legal entity responsible for the study
The authors.
Funding
This research was supported by the Bio & Medical Technology Development Program of the National Research Foundation (NRF) & funded by the Korean government (NRF-2019M3E5D4064636).
Disclosure
All authors have declared no conflicts of interest.