인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
Recently, immune checkpoint inhibitors (ICIs) have driven profound changes in the treatment of non-small cell lung cancer (NSCLC). Their rapid integration into clinical routine is crucial for patient outcomes. However, prescribing patterns may not change immediately after authorization. Therefore, in this study we investigated factors associated with the adoption of ICI therapy for patients with advanced lung cancer in Germany following the initial regulatory approval. In this study we used German health insurance claims of 36,727 lung cancer patients diagnosed in 2015–2016. We included pre-treated patients with advanced disease. Factors potentially influencing the adoption of ICI therapies were analyzed, including demographics, residence type, hospital size, comorbidities, and metastasis location. Changes in prescribing patterns for ICI therapies were evaluated over three years using population-at-risk calculations with statistical analysis conducted using techniques including multivariate Cox regression. Overall, we identified 9,726 pre-treated patients with advanced lung cancer in our dataset. Of these, 285 received ICI therapy during the course of the disease. These initial patients receiving ICI therapy were significantly younger and were more often treated in bigger hospitals. At first, uptake of ICI therapy was slow but started to increase from 1.1% in 01/2017 to 8.6% in 12/2019. Multivariate Cox regression showed that being treated in a bigger hospital (HR = 1.49, p = 0.001), having M1a vs. M1b or c metastases (HR = 2.65, p < 0.0001), being diagnosed in 2016 vs. 2015 (HR = 3.39, p < 0.0001), and having a comorbidity of COPD (HR = 1.46, p = 0.004), led to higher, faster adoption of ICI therapy. Introducing novel therapies necessitates a deliberate focus on disseminating information and enhancing accessibility across healthcare facilities of varying sizes.
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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