Department of Epidemiology and.
Department of Medicine and.
Ann Am Thorac Soc. 2024 Nov;21(11):1496-1506. doi: 10.1513/AnnalsATS.202309-836OC.
Although recent evidence suggested that glucagon-like peptide 1 receptor agonists (GLP1RAs) might reduce the risk of asthma exacerbations, it remains unclear which subpopulations might derive the most benefit from GLP1RA treatment. To identify characteristics of patients with asthma that predict who might benefit the most from GLP1RA treatment using real-world data. We implemented an active-comparator, new-user design analysis using commercially ensured patients 18-65 years of age from MarketScan data for 2007-2019 and identified two cohorts: GLP1RAs versus thiazolidinediones and GLP1RAs versus sulfonylureas. The outcome was acute exacerbation of asthma (hospital admission or emergency department visit for asthma) within 180 days after initiation. We applied iterative causal forest, a novel causal machine learning subgrouping algorithm, to assess heterogeneous treatment effects. In identified subgroups, we predicted propensity score, conducted propensity score trimming, and then estimated adjusted risk differences for the effect of GLP1RAs relative to comparators on asthma exacerbation using inverse probability treatment weighting in the propensity score-trimmed subpopulation. Among 10,989 patients initiating GLP1RAs or thiazolidinediones and 17,088 patients initiating GLP1RAs versus sulfonylurea, GLP1RA initiators had fewer exacerbations, with adjusted risk differences of -0.5% (95% confidence interval [CI], -1.1% to 0.1%) and -1.6% (95% CI, -2.2% to -1.1%), respectively. In the GLP1RA versus sulfonylurea cohort, in which we observed a beneficial effect, our iterative causal forest analysis identified five subgroups with different treatment effects, defined by the number of emergency department visits, the number of prescriptions for short-acting β-agonists, the number of prescriptions for inhaled steroids and long-acting β-agonists (either combination therapy or concurrent use), and age ≥ 50 years. Among these, patients with two or more emergency department visits during the 12-month baseline period had the largest absolute exacerbation risk reduction, with a decrease of 2.8% for GLP1RAs (95% CI, -4.8% to -0.9%). GLP1RAs demonstrated a beneficial effect on reducing asthma exacerbation relative to sulfonylureas. Patients with asthma with two or more emergency department visits (a proxy for disease severity) benefit most from GLP1RAs. Emergency department visit frequency, the number of maintenance and reliever inhalers, and age might help individualize prediction of the short-term benefit of GLP1RAs on asthma exacerbation.
尽管最近的证据表明胰高血糖素样肽 1 受体激动剂 (GLP1RAs) 可能降低哮喘恶化的风险,但尚不清楚哪些亚组可能从 GLP1RA 治疗中获益最大。为了确定哮喘患者的特征,以使用真实世界的数据预测谁可能从 GLP1RA 治疗中获益最大。我们使用商业保障的 2007-2019 年 MarketScan 数据中的 18-65 岁患者实施了活性对照、新用户设计分析,并确定了两个队列:GLP1RAs 与噻唑烷二酮和 GLP1RAs 与磺酰脲类。结果是在起始后 180 天内哮喘急性加重(因哮喘住院或急诊就诊)。我们应用了一种新的因果机器学习亚组算法——迭代因果森林,以评估治疗效果的异质性。在确定的亚组中,我们预测了倾向评分,进行了倾向评分修剪,然后在倾向评分修剪的亚组中使用逆概率处理加权法估计 GLP1RA 相对于对照药物对哮喘加重的调整风险差异。在 10989 名开始使用 GLP1RA 或噻唑烷二酮的患者和 17088 名开始使用 GLP1RA 与磺酰脲类的患者中,GLP1RA 起始者的发作次数较少,调整后的风险差异分别为-0.5%(95%置信区间 [CI],-1.1%至 0.1%)和-1.6%(95% CI,-2.2%至-1.1%)。在 GLP1RA 与磺酰脲类的队列中,我们观察到了有益的效果,我们的迭代因果森林分析确定了五个具有不同治疗效果的亚组,这些亚组的定义是急诊就诊次数、短效 β-激动剂处方数、吸入性皮质类固醇和长效β-激动剂(无论是联合治疗还是同时使用)以及年龄≥50 岁。在这些亚组中,在 12 个月基线期内有两次或两次以上急诊就诊的患者哮喘加重的绝对风险降低最大,GLP1RA 降低 2.8%(95% CI,-4.8%至-0.9%)。GLP1RA 与磺酰脲类相比,对降低哮喘恶化具有有益的效果。有两次或两次以上急诊就诊的哮喘患者(疾病严重程度的替代指标)从 GLP1RA 中获益最大。急诊就诊频率、维持和缓解吸入器的数量以及年龄可能有助于个体化预测 GLP1RA 对哮喘恶化的短期益处。