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机器学习风险预测模型(LungFlag)在西班牙非小细胞肺癌筛查高危个体选择中的成本效益分析

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.

作者信息

Trujillo Juan Carlos, Soriano Joan B, Marzo Mercè, Higuera Oliver, Gorospe Luis, Pajares Virginia, Olmedo María Eugenia, Arrabal Natalia, Flores Andrés, García José Francisco, Crespo María, Carcedo David, Heuser Carolina, Obradović Milan M S, Olghi Nicolò, Choman Eran N, Seijo Luis M

机构信息

Thoracic Surgery Department, Hospital de la Santa Creu i Sant Pau and Hospital del Mar, Barcelona, Spain.

Neumology service, Hospital Universitario de la Princesa - UAM, Madrid, Spain.

出版信息

J Med Econ. 2025 Dec;28(1):147-156. doi: 10.1080/13696998.2024.2444781. Epub 2025 Jan 8.

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.

摘要

目的

LungFlag风险预测模型利用个体临床变量来识别非小细胞肺癌(NSCLC)高危个体,以便用低剂量计算机断层扫描(LDCT)进行筛查。本研究评估在西班牙实施LungFlag以识别NSCLC高危个体的成本效益。

方法

将决策树与马尔可夫模型相结合的模型应用于西班牙的情况,以计算终身健康结果和成本,比较两种假设情景:使用LungFlag进行筛查与不筛查,以及使用LungFlag进行筛查与对符合2013年美国预防服务工作组(USPSTF)标准的全体人群进行筛查。模型输入数据来自文献和多学科专家小组的临床实践。仅考虑从当地来源获得的直接成本(2023年欧元)。进行了确定性和概率敏感性分析,以评估我们结果的稳健性。

结果

符合2013年USPSTF标准的3835128名个体组成的队列需要进行2147672次LDCT扫描。然而,使用LungFlag仅需要232120次LDCT扫描。成本效益结果表明,与不筛查情景相比,LungFlag具有优势,并且优于对全体人群进行筛查的情景,因为观察到的有效性损失(-224031生命年[LYs]和-97612质量调整生命年[QALYs])在很大程度上被显著的成本节省(7.053亿欧元)所抵消。该全体人群筛查策略与使用LungFlag的策略相比,得出的增量成本效益比(ICER)为72000欧元/QALY,表明LungFlag具有成本效益。描述了各种因素,如疗效或依从率的来源,以及成本效益分析固有的其他局限性。

结论

在西班牙,使用LungFlag选择NSCLC筛查的高危个体将是一种比筛查符合2013年USPSTF标准的全体人群更具成本效益的策略,并且优于不筛查。

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