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American Society of Clinical Oncology, Journal of Clinical Oncology, 15_suppl(37), p. e14148-e14148, 2019

DOI: 10.1200/jco.2019.37.15_suppl.e14148

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Endocrinopathies associated with immune checkpoint inhibitors: Standard of care at veterans administration medical centers.

This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

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Data provided by SHERPA/RoMEO

Abstract

e14148 Background: Multiple different immune checkpoint inhibitors (ICI) have now received FDA approval for nearly 70 separate indications covering 14 different tumor types. Patients treated with these agents in clinical trials have an observed incidence of immune related adverse events (irAEs), including endocrinopathies, which may increase morbidity and mortality. Limited information describes the incidence and impact of these events outside of clinical trials. Methods: Retrospective data from the Veterans Health Administration (VA) of patients treated with ICI has been aggregated to understand the impact of these events in a standard of care setting, with a goal of improving patient care through predictive models and contributing to the understanding of the mechanisms and response to treatment. Results: Between October 2015 and December 2018, 10,280 patients were prescribed ICI at VA medical centers, with an average age of 70 years (range 20-99). A total of 11098 ICI orders, allowing for combinations or sequential treatments. Overall, nivolumab was prescribed 6024 times (54.3%), pembrolizumab 3976 (35.8%), ipilimumab 565 (5.1%) and atezolizumab 519 (4.6%). Avelumab (13) and durvalumab (1) had limited use. A candidate set of potential endocrine adverse events was estimated based on selected ICD10 codes recorded for the first time after treatment with ICI (Table). Conclusions: The frequency of endocrine immune related adverse events has been reported to be 5-10%. Here we have identified a cohort of ICI treated patients who may have developed endocrine adverse events. This cohort will be used to evaluate phenotyping, potential biomarkers and models of predictive risk.[Table: see text]