Background There is a large variability in clinical response to corticosteroid treatment in patients with asthma. Several markers of inflammation like eosinophils and eosinophil cationic protein (ECP), as well as exhaled nitric oxide (NO), are good candidates to predict clinical response.
Aim We wanted to determine whether we could actually predict a favourable response to inhaled corticosteroids in individual patients.
Methods One hundred and twenty patients with unstable asthma were treated with either prednisolone 30 mg/day, fluticasone propionate 1000 µg/day b.i.d. or fluticasone propionate 250 µg/day b.i.d., both via Diskhaler. They were treated during 2 weeks, in a double-blind, parallel group, double dummy design. We measured eosinophils and ECP in blood and sputum, and exhaled nitric oxide as inflammatory parameters before and after 2 weeks in order to predict the changes in forced expiratory volume in 1 s (FEV1), provocative concentration of methacholine causing a 20% fall in FEV1 (PC20 Mch), and asthma quality of life (QOL). Secondly, to test whether these results were applicable in clinical practice we determined the individual prediction of corticosteroid response.
Results We found that changes in FEV1, PC20 Mch and QOL with corticosteroids were predominantly predicted by their respective baseline value and to a smaller extent by eosinophils in blood or sputum. ECP, measured in blood or sputum, was certainly not better than eosinophils in predicting clinical response to corticosteroids. Smoking status was an additional predictor for change in FEV1, but not for change in PC20 Mch or QOL. Prediction of a good clinical response was poor. For instance, high sputum eosinophils (≥ 3%) correctly predicted an improvement in PC20 Mch in only 65% of the patients.
Conclusion Our findings show that baseline values of the clinical parameters used as outcome parameters are the major predictors of clinical response to corticosteroids. Eosinophil percentage in blood or sputum adds to this, whereas ECP provides no additional information. Correct prediction of clinical response in an individual patient, however, remains poor with our currently used clinical and inflammatory parameters.