Veera Nurmela, Anni Juntunen, Satu Tiainen, Janne Martikainen, Sanna Pasonen-Seppänen, Aino Rönkä
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引用次数: 0
Abstract
Background: Up to half of cancer patients treated with immune checkpoint inhibitors (ICIs) develop immune-related adverse events (irAEs). However, the long-term consequences of irAEs remain poorly characterized.
Objective: This study investigated the likelihood of chronic irAEs, challenges related to their corticosteroid treatment and clinical workload associated with their management.
Methods: We retrospectively analyzed 230 patients who received ICIs at Kuopio University Hospital, Finland, between the years 2015 and 2024.
Results: IrAEs were chronic in 60.9% of patients who developed toxicities. Thyroiditis was the most common chronic AE (40%). Of the patients with irAE+, 42.9% were treated with systemic corticosteroids and steroid-related AEs were observed in 58% of them. Corticosteroid usage for ≥ 3 months was associated with an increased risk of steroid-related AEs (< 3 months 38.2% vs ≥ 3 months 78.8%, p < 0.001). An average of 5.3 contacts with a medical oncologist was needed for the management of an irAE, with the highest workload associated with hepatitis (10.0 contacts/irAE) and colitis (6.1 contacts/irAE).
Conclusions: The characterization of the long-term sequelae of irAEs and the associated clinical workload is crucial for proper patient education and clinical resource planning.
期刊介绍:
Drugs - Real World Outcomes targets original research and definitive reviews regarding the use of real-world data to evaluate health outcomes and inform healthcare decision-making on drugs, devices and other interventions in clinical practice. The journal includes, but is not limited to, the following research areas: Using registries/databases/health records and other non-selected observational datasets to investigate: drug use and treatment outcomes prescription patterns drug safety signals adherence to treatment guidelines benefit : risk profiles comparative effectiveness economic analyses including cost-of-illness Data-driven research methodologies, including the capture, curation, search, sharing, analysis and interpretation of ‘big data’ Techniques and approaches to optimise real-world modelling.