Glucagon-like Peptide-1 Receptor Agonists in Asthma Exacerbations: An Application of High-Dimensional Iterative Causal Forest to Identify Subgroups.

ABSTRACT:

Background

Glucagon-like Peptide-1 Receptor Agonists (GLP1RA) may reduce asthma exacerbation (AE) risk, but it is unclear which populations benefit most. Recent pharmacoepidemiologic studies have employed iterative causal forest (iCF), a machine learning (ML) algorithm, to identify subgroups with heterogeneous treatment effects (HTEs). While iCF does not rely on prior knowledge of treatment-variable interactions, it may be constrained by missing or poorly defined variables in pharmacoepidemiologic studies.

Methods

We applied the high-dimensional iterative causal forest (hdiCF)-a causal ML algorithm requiring predefined variables-to MarketScan 2016-2020 claims data to identify populations with asthma that might benefit most from GLP1RA in reducing AE risk. We built a GLP1RA v

SUBMITTER: Wang T 

PROVIDER: S-EPMC12831167 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature