Clinical response to benralizumab can be predicted by combining clinical outcomes at 3 months with baseline characteristics. Issue 2 (11th April 2023)

• Record Type: Journal Article
• Authors: Kroes, Johannes A.; de Jong, Kim; Hashimoto, Simone; Zielhuis, Sander W.; van Roon, Eric N.; Sont, Jacob K.; ten Brinke, Anneke
• Is Part Of: ERJ open research. Volume 9:Issue 2(2023)
• Publication Date: 2023-04-11
• Subjects: Respiratory organs -- Diseases -- Periodicals; Respiration -- Periodicals; Respiration; Respiratory organs -- Diseases; Respiratory organs -- Diseases -- Treatment; Respiratory Tract Diseases; Electronic journals; Fulltext; Internet Resources; Periodicals; Periodical; 616.2005
• DOI: 10.1183/23120541.00559-2022
• Languages: English
• ISSNs: 2312-0541

Abstract

Background: Benralizumab is highly effective in many, but not all, patients with severe asthma. Baseline characteristics alone are insufficient to predict an individual's probability of long-term benralizumab response. The objectives of the present study were to: 1) study whether parameters at 3 months, in addition to baseline characteristics, contribute to the prediction of benralizumab response at 1 year; and 2) develop an easy-to-use prediction tool to assess an individual's probability of long-term response. Methods: We assessed the effect of benralizumab treatment in 192 patients from the Dutch severe asthma registry (RAPSODI). To investigate predictors of long-term benralizumab response (≥50% reduction in maintenance oral corticosteroid (OCS) dose or annual exacerbation frequency) we used logistic regression, including baseline characteristics and 3-month Asthma Control Questionnaire (ACQ-6) score and maintenance OCS dose. Results: Benralizumab treatment significantly improved several clinical outcomes, and 144 (75%) patients were classified as long-term responders. Response prediction improved significantly when 3-month outcomes were added to a predictive model with baseline characteristics only (area under the receiver-operating characteristic (AUROC) 0.85 versus 0.72, p=0.001). Based on this model, a prediction tool using sex, prior biologic use, baseline blood eosinophils, forced expiratory volume in 1 s, and at 3 months OCS dose and ACQ-6 was developed which classified patients into three categories with increasing probability of long-term response (95% CI): 25% (3–65%), 67% (57–77%) and 97% (91–99%), respectively. Conclusion: In addition to baseline characteristics, treatment outcomes at 3 months contribute to the prediction of benralizumab response at 1 year in patients with severe eosinophilic asthma. Prediction tools as proposed in this study may help physicians optimise the use of costly biologics. Baseline characteristics and OCS dose and ACQ score at 3 months help predict long-term clinical response to benralizumab. Clinical tools, such as proposed in this study, could help clinicians predict future response to benralizumab. https://bit.ly/3XS9nDd