Cost-effectiveness of a machine learning risk prediction model (LungFlag) in the selection of high-risk individuals for non-small cell lung cancer screening in Spain.

IF 2.9 4区 医学 Q2 HEALTH CARE SCIENCES & SERVICES
Journal of Medical Economics Pub Date : 2025-12-01 Epub Date: 2025-01-08 DOI:10.1080/13696998.2024.2444781
Juan Carlos Trujillo, Joan B Soriano, Mercè Marzo, Oliver Higuera, Luis Gorospe, Virginia Pajares, María Eugenia Olmedo, Natalia Arrabal, Andrés Flores, José Francisco García, María Crespo, David Carcedo, Carolina Heuser, Milan M S Obradović, Nicolò Olghi, Eran N Choman, Luis M Seijo
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引用次数: 0

Abstract

Objective: The LungFlag risk prediction model uses individualized clinical variables to identify individuals at high-risk of non-small cell lung cancer (NSCLC) for screening with low-dose computed tomography (LDCT). This study evaluates the cost-effectiveness of LungFlag implementation in the Spanish setting for the identification of individuals at high-risk of NSCLC.

Methods: A model combining a decision-tree with a Markov model was adapted to the Spanish setting to calculate health outcomes and costs over a lifetime horizon, comparing two hypothetical scenarios: screening with LungFlag versus non-screening, and screening with LungFlag versus screening the entire population meeting 2013 US Preventive Services Task Force (USPSTF) criteria. Model inputs were obtained from the literature and the clinical practice of a multidisciplinary expert panel. Only direct costs (€of 2023), obtained from local sources, were considered. Deterministic and probabilistic sensitivity analyses were performed to assess the robustness of our results.

Results: A cohort of 3,835,128 individuals meeting 2013 USPSTF criteria would require 2,147,672 LDCTs scans. However, using LungFlag would only require 232,120 LDCTs scans. Cost-effectiveness results showed that LungFlag was dominant versus non-screening scenario, and outperformed the scenario where the entire population were screened since the observed loss of effectiveness (-224,031 life years [LYs] and -97,612 quality-adjusted life years [QALYs]) was largely offset by the significant cost savings provided (€7,053 million). The resulting incremental cost-effectiveness ratio (ICER) for this strategy of screening the whole population versus using LungFlag was €72,000/QALY, showing that LungFlag is cost-effective. Various were described, such as the source of the efficacy or adherence rates, and other limitations inherent to cost-effectiveness analyses.

Conclusions: Using LungFlag for the selection of high-risk individuals for NSCLC screening in Spain would be a cost-effective strategy over screening the entire population meeting USPSTF 2013 criteria and is dominant over non-screening.

机器学习风险预测模型(LungFlagTM)在西班牙非小细胞肺癌筛查中选择高风险个体的成本效益
目的:LungFlagTM风险预测模型利用个体化临床变量识别非小细胞肺癌(NSCLC)高危人群,进行低剂量计算机断层扫描(LDCT)筛查。本研究评估了在西班牙实施LungFlagTM识别非小细胞肺癌高危人群的成本效益。方法:将决策树与马尔可夫模型相结合的模型适用于西班牙环境,以计算一生的健康结果和成本,比较两种假设情景:使用LungFlagTM进行筛查与不进行筛查,以及使用LungFlagTM进行筛查与筛查符合2013年美国预防服务工作组(USPSTF)标准的整个人群。模型输入来自文献和多学科专家小组的临床实践。只考虑了从当地来源获得的直接成本(2023年欧元)。进行确定性和概率敏感性分析以评估结果的稳健性。结果:符合2013年USPSTF标准的3,835,128人队列将需要2,147,672次ldct扫描。然而,使用LungFlagTM只需要232,120个ldct扫描。成本效益结果显示,与非筛查方案相比,LungFlagTM占主导地位,并且优于对整个人群进行筛查的方案,因为观察到的有效性损失(-224,031生命年[LYs]和-97,612质量调整生命年[QALYs])在很大程度上被提供的显著成本节省(70.53亿欧元)所抵消。与使用LungFlagTM相比,对整个人群进行筛查的增量成本效益比(ICER)为72,000欧元/QALY,表明LungFlagTM具有成本效益。描述了各种方法,如疗效或依从率的来源,以及成本-效果分析固有的其他限制。结论:在西班牙,使用LungFlagTM筛选高危人群进行非小细胞肺癌筛查,与筛查符合USPSTF 2013标准的整个人群相比,将是一种具有成本效益的策略,并且优于非筛查。
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来源期刊
Journal of Medical Economics
Journal of Medical Economics HEALTH CARE SCIENCES & SERVICES-MEDICINE, GENERAL & INTERNAL
CiteScore
4.50
自引率
4.20%
发文量
122
期刊介绍: Journal of Medical Economics'' mission is to provide ethical, unbiased and rapid publication of quality content that is validated by rigorous peer review. The aim of Journal of Medical Economics is to serve the information needs of the pharmacoeconomics and healthcare research community, to help translate research advances into patient care and be a leader in transparency/disclosure by facilitating a collaborative and honest approach to publication. Journal of Medical Economics publishes high-quality economic assessments of novel therapeutic and device interventions for an international audience
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