Department of Epidemiology and Data Science, Amsterdam Public Health, Amsterdam UMC, Vrije Universiteit Amsterdam, de Boelelaan 1117, PO Box 7057, 1007 MB, Amsterdam, The Netherlands.
Department of Clinical Epidemiology and Medical Technology Assessment, Maastricht University Medical Centre+, Maastricht, The Netherlands.
Sci Rep. 2023 Feb 9;13(1):2349. doi: 10.1038/s41598-023-29286-5.
Recent discoveries in molecular diagnostics and drug treatments have improved the treatment of patients with advanced (inoperable) non-squamous non-small cell lung cancer (NSCLC) from solely platinum-based chemotherapy to more personalized treatment, including targeted therapies and immunotherapies. However, these improvements come at considerable costs, highlighting the need to assess their cost-effectiveness in order to optimize lung cancer care. Traditionally, cost-effectiveness models for the evaluation of new lung cancer treatments were based on the findings of the randomized control trials (RCTs). However, the strict RCT inclusion criteria make RCT patients not representative of patients in the real-world. Patients in RCTs have a better prognosis than patients in a real-world setting. Therefore, in this study, we developed and validated a diagnosis-treatment decision model for patients with advanced (inoperable) non-squamous NSCLC based on real-world data in the Netherlands. The model is a patient-level microsimulation model implemented as discrete event simulation with five health events. Patients are simulated from diagnosis to death, including at most three treatment lines. The base-model (non-personalized strategy) was populated using real-world data of patients treated with platinum-based chemotherapy between 2008 and 2014 in one of six Dutch teaching hospitals. To simulate personalized care, molecular tumor characteristics were incorporated in the model based on the literature. The impact of novel targeted treatments and immunotherapies was included based on published RCTs. To validate the model, we compared survival under a personalized treatment strategy with observed real-world survival. This model can be used for health-care evaluation of personalized treatment for patients with advanced (inoperable) NSCLC in the Netherlands.
近年来，分子诊断和药物治疗方面的新发现改善了晚期（不可手术）非鳞状非小细胞肺癌（NSCLC）患者的治疗方法，从单纯的铂类化疗转变为更具个性化的治疗方法，包括靶向治疗和免疫疗法。然而，这些改进带来了相当大的成本，突出了需要评估其成本效益，以优化肺癌治疗。传统上，用于评估新的肺癌治疗方法的成本效益模型是基于随机对照试验（RCT）的结果。然而，严格的 RCT 纳入标准使得 RCT 患者不能代表真实世界中的患者。RCT 患者的预后比真实世界患者好。因此，在这项研究中，我们基于荷兰的真实世界数据，为晚期（不可手术）非鳞状 NSCLC 患者开发和验证了一种诊断-治疗决策模型。该模型是一个基于患者水平的微观模拟模型，采用离散事件模拟方法，包含五个健康事件。患者从诊断到死亡被模拟，最多接受三种治疗方案。基础模型（非个性化策略）是使用 2008 年至 2014 年间在六家荷兰教学医院之一接受铂类化疗治疗的患者的真实世界数据填充的。为了模拟个性化护理，根据文献将肿瘤分子特征纳入模型。根据已发表的 RCT 结果，纳入了新型靶向治疗和免疫疗法的影响。为了验证模型，我们将个性化治疗策略下的生存情况与真实世界的观察生存情况进行了比较。该模型可用于评估荷兰晚期（不可手术）NSCLC 患者个性化治疗的卫生保健效果。
