Background
Immune-related adverse effects (irAE) are widely known for immune checkpoint inhibitors (ICI), but medications used to relieve irAE based on patient profile and condition are not offered in guidelines. Publications are available to reveal irAE from hospitals, but the number of cases are normally only a few hundred and insufficient to stratify by more variables. Claim-based data points were also used in a few publications, based on analysis of diagnosis code such as ICD-9 and ICD-10. However, missing or wrong coding of adverse effects are well-known and over-exaggerated for severe adverse events, and are also affected by reimbursement policy. This research used medications from a large national health system to understand irAE and treatments related to them.
Methods
2017-2019 National Health Insurance database (NHIDB) in Taiwan was used. The case group was identified as (2,474) patients who used PD-1 inhibitors (more specifically Nivolumab, Pembrolizumab, and Atezolizumab because only those three were available in the duration). A control group was identified as (6,207) patients given platinum-based chemotherapy (cases using other cancer drugs at the same time were excluded). Odds ratios were calculated to see frequency of irAE and medications in both groups.
Results
Top 10 medications are listed. Diphenhydramine and pain killers were the most common AE-relief medications for cancer patients in NHIDB and only anilides (mostly propacetamol) are overlapped. The rest of the drugs with OR > 3, especially steroids (thyroid and corticosteroids), were used mainly for irAE.
Conclusions
The research reveals a novel approach to understand how physicians prescribe to treat irAE. The larger number of cases reported compared to previous studies suggests that such a database can be used for future research, including to reveal demographic patterns, and to aid the development of guidelines, teaching aids, and decision support.
Chun‑You Chen, Ya-Lin Chen, Jeremiah Scholl, Hsuan-Chia Yang, Yu-Chuan Jack Li, Ability of machine-learning based clinical decision support system to reduce alert fatigue, wrong-drug errors, and alert users about look alike, sound alike medication, Computer Methods and Programs in Biomedicine, 2023, 107869