Cost-effectiveness of novel diagnostic tools for idiopathic pulmonary fibrosis in the United States.

ABSTRACT:

Objectives

Novel non-invasive machine learning algorithms may improve accuracy and reduce the need for biopsy when diagnosing idiopathic pulmonary fibrosis (IPF). We conducted a cost-effectiveness analysis of diagnostic strategies for IPF.

Methods

We developed a decision analytic model to evaluate diagnostic strategies for IPF in the United States. To assess the full spectrum of costs and benefits, we compared four interventions: a machine learning diagnostic algorithm, a genomic classifier, a biopsy-all strategy, and a treat-all strategy. The analysis was conducted from the health sector perspective with a lifetime horizon. The primary outcome measures were costs, Quality-Adjusted Life-Years (QALYs) gained, and Incremental Cost-Effectiveness Ratios (ICERs) based on the aver

SUBMITTER: Cadham CJ 

PROVIDER: S-EPMC11909868 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature