Detection and evaluation of signals for immune-related adverse events: a nationwide, population-based study.

Background: Immune checkpoint inhibitors (ICIs) are one of the main pillars of cancer therapy. Since other studies such as clinical trial and retrospective study have limitations for detecting the immune-related adverse events (irAEs) characterized by unpredictable onset, nonspecific symptoms and wide clinical spectrum, we aimed to identify the incidence of irAEs and to detect and evaluate the signals using real-world data. Methods: Cancer patients treated with anticancer medications were analyzed using the nationwide health insurance claims database of South Korea from 2017 to 2019, and Clinical Data Warehouse (CDW) database of Asan Medical Center (AMC), a tertiary referral hospital, from 2012 to 2019. AEs of ICI users were compared with those of non-ICI anticancer medication users. PD-1 inhibitors (nivolumab and pembrolizumab) and PD-L1 inhibitors (atezolizumab) were evaluated. We defined an AE as a newly added diagnosis after the ICI prescription using an ICD-10 diagnostic code. A signal was defined as an AE that was detected by any one of the four indices of data mining: hazard ratio (HR), proportional claims ratio (PCR), claims odds ratio (COR), or information component (IC). All detected signals were reviewed and classified into well-known or potential irAEs. Signal verification was performed for targeted AEs using CDW of AMC using diagnostic codes and text mining. Results: We identified 118 significant signals related to ICI use. We detected 31 well-known irAEs, most of which were endocrine diseases and skin diseases. We also detected 33 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. Conclusion: 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.