DOI 10.1038/s41598-025-22966-4
Modeling brain metastases in cost effectiveness analysis of atezolizumab for extensive stage small cell lung cancer.
Аннотация
Brain metastases (BM) significantly impact prognosis and healthcare utilization but remain underrepresented in economic evaluations. The IMpower133 trial demonstrated the efficacy of atezolizumab plus chemotherapy (carboplatin and etoposide), particularly among patients without baseline BM, leading to its approval for extensive-stage small-cell lung cancer (EX-SCLC) in several countries. As previous studies did not incorporate BM status, we addressed this gap by including BM in the cost-effectiveness analysis of atezolizumab for EX-SCLC. A four-state Markov model was constructed with the following health states: progression-free, progressive disease (PD) without brain metastases, PD with brain metastases, and death, over a 15-year time horizon. Time-varying efficacy and utility were derived from IMpower133; direct medical costs were estimated using population-based databases. Both deterministic and probabilistic sensitivity analyses were conducted to assess uncertainty in input parameters. Adding atezolizumab to chemotherapy increased QALYs by 0.721 at an incremental cost of NT$2,111,359, yielding an incremental net monetary benefit of NT$68,264, and a 53.22% probability of cost-effectiveness. Key sources of uncertainty include efficacy, cost of atezolizumab, time horizon, and utility. In conclusion, adding atezolizumab to chemotherapy is cost-effective for patients with EX-SCLC without baseline BM. Incorporating BM status into CEA could support more nuanced policy recommendations.
Предметные рубрики MeSH
Подготовка и перевод публикации
Обработка статьи · этапы, ошибки и управление
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Тип публикации указан по данным источника. Уровень доказательности автоматически не присваивается.