Role for Artificial Intelligence in the Detection of Immune-Related Adverse Events
Menée à partir de données de registres hospitaliers portant sur 7 555 admissions concernant 3 521 patients atteints d'un cancer traité par inhibiteurs de point de contrôle immunitaire puis validée à partir de données portant sur 1 270 admissions hospitalières supplémentaires (993 patients), cette étude évalue, par rapport aux codes de la classification internationale des maladies, la sensibilité et la spécificité d'un modèle linguistique de grande taille pour détecter des effets indésirables graves liés au système immunitaire
Despite recent advancements and uptake in the use of immune checkpoint inhibitors (ICIs) for patients with multiple cancer types and stages, ICIs can trigger immune syndromes called immune-related adverse events (irAEs). While these immune responses can affect any organ system, common irAEs include gastrointestinal, rheumatic, endocrine, and dermatologic.1-3 In addition to adversely affecting patients' quality of life, irAEs may result in treatment interruptions or necessitate prolonged courses of high doses of systemic corticosteroids or other immune suppression therapy, which may have long-lasting effects. Some irAEs can cause chronic effects, such as pulmonary toxicities, with patients requiring life-long oxygen, and some irAEs can be fatal.4,5 Research on potential risk factors of irAEs and strategies to mitigate or minimize their risk have been hampered by a lack of valid methods to accurately detect and characterize irAEs from medical records in real-world settings despite the significant risk they pose to patients. In addition, this detection gap prevents researchers from leveraging large data sets, which have been instrumental in leading to breakthroughs in other research fields, such as genomics.