Senior Consultant, UNCOMN, Chatham, IL, USA.
Data Science, AI, OR, and Logistics, University of Twente, Twente, Netherlands.
Med Decis Making. 2024 Jul;44(5):554-571. doi: 10.1177/0272989X241258224. Epub 2024 Jun 22.
Detection of colorectal cancer (CRC) in the early stages through available screening tests increases the patient's survival chances. Multimodal screening policies can benefit patients by providing more diverse screening options and balancing the risks and benefits of screening tests. We investigate the cost-effectiveness of a wide variety of multimodal CRC screening policies.
We developed a Monte Carlo simulation framework to model CRC dynamics. We proposed an innovative calibration process using machine learning models to estimate age- and size-specific adenomatous polyps' progression and regression rates. The proposed approach significantly expedites the model parameter space search.
Two multimodal proposed policies (i.e., 1] colonoscopy at 50 y and fecal occult blood test annually between 60 and 75 y and 2] colonoscopy at 50 and 60 y and fecal immunochemical test annually between 70 and 75 y) are identified as efficient frontier policies. Both policies are cost-effective at a willingness to pay of $50,000. Sensitivity analyses were performed to assess the sensitivity of results to a change in screening test costs as well as adherence behavior. The sensitivity analysis results suggest that the proposed policies are mostly robust to the considered changes in screening test costs, as there is a significant overlap between the efficient frontier policies of the baseline and the sensitivity analysis cases. However, the efficient frontier policies were more sensitive to changes in adherence behavior.
Generally, combining stool-based tests with visual tests will benefit patients with higher life expectancy and a lower expected cost compared with unimodal screening policies. Colonoscopy at younger ages (when the colonoscopy complication risk is lower) and stool-based tests at older ages are shown to be more effective.
We propose a detailed Markov model to capture the colorectal cancer (CRC) dynamics. The proposed Markov model presents the detailed dynamics of adenomas progression to CRC.We use more than 44,000 colonoscopy reports and available data in the literature to calibrate the proposed Markov model using an innovative approach that leverages machine learning models to expedite the calibration process.We investigate the cost-effectiveness of a wide variety of multimodal CRC screening policies and compare their performances with the current in-practice policies.
通过现有的筛查测试在早期发现结直肠癌 (CRC) 可以提高患者的生存机会。多模式筛查策略可以通过提供更多样化的筛查选择，并平衡筛查测试的风险和益处，使患者受益。我们研究了各种多模式 CRC 筛查策略的成本效益。
我们开发了一个蒙特卡罗模拟框架来模拟 CRC 动态。我们提出了一种创新的校准过程，使用机器学习模型来估计年龄和大小特异性腺瘤的进展和消退率。所提出的方法大大加快了模型参数空间搜索。
两种多模式建议政策（即 1] 结肠镜检查在 50 岁，60 至 75 岁期间每年进行粪便潜血试验，2] 结肠镜检查在 50 岁和 60 岁，70 至 75 岁期间每年进行粪便免疫化学试验）被确定为有效边界政策。这两种政策在支付意愿为 50,000 美元时都具有成本效益。进行了敏感性分析以评估结果对筛查测试成本变化以及依从性行为的敏感性。敏感性分析结果表明，所提出的政策对筛查测试成本变化具有较强的稳健性，因为在基线和敏感性分析情况下的有效边界政策之间存在显著重叠。然而，有效边界政策对依从性行为的变化更为敏感。
一般来说，与单模式筛查策略相比，将基于粪便的测试与视觉测试相结合将使具有更高预期寿命和更低预期成本的患者受益。在年轻时（当结肠镜检查并发症风险较低时）进行结肠镜检查，并在老年时进行基于粪便的测试，被证明更为有效。
我们提出了一种详细的马尔可夫模型来捕获结直肠癌 (CRC) 的动态。所提出的马尔可夫模型呈现了腺瘤进展为 CRC 的详细动态。我们使用 44000 多份结肠镜检查报告和文献中可用的数据，使用创新方法利用机器学习模型来加速校准过程，对所提出的马尔可夫模型进行校准。我们研究了各种多模式 CRC 筛查策略的成本效益，并将其与当前实践中的策略进行了比较。
