Clinical response to benralizumab can be predicted by combining clinical outcomes at 3 months with baseline characteristics.

Abstract

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. 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. 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. 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.