Saw Swee Hock School of Public Health, National University of Singapore, MD1 - Tahir Foundation Building, 12 Science Drive 2, Singapore, 117549, Singapore.
The Woolcock Institute of Medical Research, The University of Sydney, Sydney, Australia.
Respir Res. 2023 May 2;24(1):120. doi: 10.1186/s12931-023-02409-2.
BACKGROUND: Individualized prediction of treatment response may improve the value proposition of advanced treatment options in severe asthma. This study aimed to investigate the combined capacity of patient characteristics in predicting treatment response to mepolizumab in patients with severe asthma. METHODS: Patient-level data were pooled from two multinational phase 3 trials of mepolizumab in severe eosinophilic asthma. We fitted penalized regression models to quantify reductions in the rate of severe exacerbations and the 5-item Asthma Control Questionnaire (ACQ5) score. The capacity of 15 covariates towards predicting treatment response was quantified by the Gini index (measuring disparities in treatment benefit) as well as observed treatment benefit within the quintiles of predicted treatment benefit. RESULTS: There was marked variability in the ability of patient characteristics to predict treatment response; covariates explained greater heterogeneity in predicting treatment response to asthma control than to exacerbation frequency (Gini index 0.35 v. 0.24). Key predictors for treatment benefit for severe exacerbations included exacerbation history, blood eosinophil count, baseline ACQ5 score and age, and those for symptom control included blood eosinophil count and presence of nasal polyps. Overall, the average reduction in exacerbations was 0.90/year (95%CI, 0.87‒0.92) and average reduction in ACQ5 score was 0.18 (95% CI, 0.02‒0.35). Among the top 20% of patients for predicted treatment benefit, exacerbations were reduced by 2.23/year (95% CI, 2.03‒2.43) and ACQ5 score were reduced by 0.59 (95% CI, 0.19‒0.98). Among the bottom 20% of patients for predicted treatment benefit, exacerbations were reduced by 0.25/year (95% CI, 0.16‒0.34) and ACQ5 by -0.20 (95% CI, -0.51 to 0.11). CONCLUSION: A precision medicine approach based on multiple patient characteristics can guide biologic therapy in severe asthma, especially in identifying patients who will not benefit as much from therapy. Patient characteristics had a greater capacity to predict treatment response to asthma control than to exacerbation. TRIAL REGISTRATION: ClinicalTrials.gov number, NCT01691521 (registered September 24, 2012) and NCT01000506 (registered October 23, 2009).
背景：个体化预测治疗反应可能会提高重度哮喘中先进治疗方案的价值主张。本研究旨在探讨患者特征在预测美泊利珠单抗治疗重度嗜酸性粒细胞性哮喘中的联合能力。
方法：从两项美泊利珠单抗治疗重度嗜酸性粒细胞性哮喘的多中心 3 期临床试验中汇总患者水平数据。我们使用惩罚回归模型来量化严重加重事件的发生率和 5 项哮喘控制问卷（ACQ5）评分的降低程度。通过基尼指数（衡量治疗获益差异）以及预测治疗获益五分位数内的观察治疗获益来量化 15 个协变量预测治疗反应的能力。
结果：患者特征预测治疗反应的能力存在显著差异；协变量在预测哮喘控制的治疗反应方面比预测加重频率的异质性更大（基尼指数 0.35 比 0.24）。严重加重的治疗获益的关键预测因素包括加重史、血嗜酸性粒细胞计数、基线 ACQ5 评分和年龄，而症状控制的预测因素包括血嗜酸性粒细胞计数和鼻息肉的存在。总体而言，平均每年严重加重减少 0.90/年（95%CI，0.87-0.92），ACQ5 评分平均降低 0.18（95%CI，0.02-0.35）。在预测治疗获益最高的 20%的患者中，每年严重加重减少 2.23/年（95%CI，2.03-2.43），ACQ5 评分降低 0.59（95%CI，0.19-0.98）。在预测治疗获益最低的 20%的患者中，每年严重加重减少 0.25/年（95%CI，0.16-0.34），ACQ5 评分降低 0.20（95%CI，-0.51 至 0.11）。
结论：基于多种患者特征的精准医学方法可以指导重度哮喘中的生物治疗，特别是在确定不太可能从治疗中获益的患者方面。患者特征预测治疗反应的能力对哮喘控制的治疗反应比对加重的治疗反应更强。
试验注册：ClinicalTrials.gov 编号，NCT01691521（2012 年 9 月 24 日注册）和 NCT01000506（2009 年 10 月 23 日注册）。
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