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
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View abstract on PubMed
Summary
This summary is machine-generated.The LungFlag model efficiently identifies high-risk individuals for non-small cell lung cancer (NSCLC) screening, proving cost-effective in Spain. This approach significantly reduces low-dose computed tomography (LDCT) scans compared to current guidelines.
Area Of Science
- Health Economics
- Oncology
- Medical Technology
Background
- Non-small cell lung cancer (NSCLC) screening aims to detect the disease early using low-dose computed tomography (LDCT).
- Current screening guidelines, such as those from the US Preventive Services Task Force (USPSTF), identify a broad population for LDCT, potentially leading to overutilization and increased costs.
- The LungFlag risk prediction model offers a targeted approach to identify individuals at high risk for NSCLC, optimizing screening resource allocation.
Purpose Of The Study
- To evaluate the cost-effectiveness of implementing the LungFlag risk prediction model for NSCLC screening in the Spanish healthcare setting.
- To compare the LungFlag strategy against no screening and against screening the entire population meeting USPSTF 2013 criteria.
Main Methods
- A decision-tree and Markov model were adapted to the Spanish context for lifetime cost-effectiveness analysis.
- Model inputs were derived from literature and expert clinical opinion, considering direct medical costs in Euros (€2023).
- Deterministic and probabilistic sensitivity analyses were conducted to ensure the robustness of the findings.
Main Results
- Implementing LungFlag would significantly reduce the number of LDCT scans required: 232,120 scans versus 2,147,672 for the USPSTF 2013 criteria cohort.
- LungFlag was found to be dominant over a non-screening strategy.
- Compared to screening the entire USPSTF 2013 population, LungFlag resulted in substantial cost savings (€7,053 million) with a minimal loss of life years and quality-adjusted life years, yielding an ICER of €72,000/QALY.
Conclusions
- The LungFlag model represents a cost-effective strategy for selecting high-risk individuals for NSCLC screening in Spain.
- This targeted approach is superior to both non-screening and broad population screening based on USPSTF 2013 criteria.
- LungFlag optimizes resource allocation for NSCLC screening, improving efficiency and potentially reducing healthcare expenditures.
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