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. While iCF do not rely on prior knowledge of treatment-variable interactions, it may be constrained by missing or misdefined variables in pharmacepidemiology studies. METHODS: We applied the high-dimensional iterative causal forest (hdiCF), a causal ML algorithm that does not reply on 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 2 outpatient asthma encounters, excluding patients with non-asthma indications for systemic steroids. Using 599 high-dimensional features from inpatient/outpatient services and pharmacy claims, patients were followed for 6 months from their second prescription. The outcome was acute AE (hospital admission or emergency department visit for asthma). RESULTS: In the overall population, GLP1RA decreased AE risk relative to sulfonylurea: aRD -1.4% (-2.0%, -0.8%). hdiCF identified 3 subgroups based on systemic steroid prescription fills (0, 1, and ≥2): patients with ≥2 systemic steroid prescriptions (GLP1RA: 34 events/1367 individuals; sulfonylurea: 53/1013) benefited most from GLP1RA: aRD -3.8% (-5.3%, -2.2%). CONCLUSIONS: This study demonstrates how automated feature identification can pinpoint clinically relevant subgroups with varying treatment effects. Systemic steroid use, as a proxy for severe asthma, may guide personalized predictions of GLP1RA’s short-term benefits on acute AE.

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Background: Immune checkpoint inhibitors (ICIs), including anti-PD-1/L1 therapy and anti-CTLA-4 therapy, are associated with a unique spectrum of immune-related adverse events (irAEs). The association and clinical features of ICIs-related biliary disorders are not well characterized. Methods: Data were extracted from the US Food and Drug Administration Adverse Event Reporting System (FAERS) database. Bile duct and gallbladder diseases were defined by the Medical Dictionary for Regulatory Activities (MedDRA). We performed disproportionality analysis using reporting odds ratios (ROR) and information component (IC). The result was defined as a signal if the lower limit of the 95% confidence interval for ROR is over 1 and the number of cases ≥5, or the lower limit of 95% confidence interval for the IC (IC025)>0. Results: 906 reports of ICI-related bile duct and gallbladder events were identified. The mean age was 64.8±12.1 years and the AEs occurred more in men (60.1% vs 39.9%). ICIs were associated with increased reporting of bile duct diseases (ROR 3.35, 95%CI 3.07-3.66; IC0251.41), especially cholangitis (ROR 5.52, 95% CI 4.94-6.17; IC0251.93); while we didn’t identify signal of gallbladder disease (ROR 0.96, 95%CI 0.86-1.08; IC025 -0.22). PD-1/L1 inhibitors and combination regimen were associated with a spectrum of distinct classes of bile duct disease while anti-CTLA-4 therapy (ipilimumab) had no association with any bile duct and gallbladder diseases. Conclusions: PD-1/L1 inhibitors showed increased reporting of cholangiopathy, especially for cholangitis. Physicians should be aware of this potential adverse event.