Glucagon-like Peptide-1 Receptor Agonists in Asthma Exacerbations: An Application of High-Dimensional Iterative...

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.

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 vs. sulfonylurea new-user cohort with ≥ 1 inpatient or two outpatient asthma encounters, excluding patients with nonasthma indications for systemic steroids. The outcome was acute AE (hospital admission or emergency department visit for asthma), assessed over 6 months using 599 high-dimensional features from inpatient/outpatient services and pharmacy claims.

In the overall population, GLP1RA decreased AE risk relative to sulfonylurea: aRD -1.4% (-2.0%, -0.8%). hdiCF identified three subgroups based on the quantity of systemic steroid prescription fills (0, 1, and ≥ 2): patients with ≥ 2 prescriptions (GLP1RA: 34 events/1367 individuals; sulfonylurea: 53/1013) benefited most from GLP1RA: aRD -3.8% (-5.3%, -2.2%).

This study demonstrates how automated feature identification can pinpoint clinically relevant subgroups with HTEs. The quantity of systemic steroid prescriptions, as a proxy for severe asthma, may guide personalized predictions of GLP1RA's short-term benefits on acute AE.