| Summary: | Background: Adherence to medications is crucial in patients with severe asthma in light of the negative clinical impact and costs of non-adherence. Adherence to omalizumab has not been well studied in real-world settings. The aim of this study was to assess adherence to omalizumab and evaluate treatment effectiveness in relation to adherence. Methods: This was a retrospective, observational, and multicenter real-world study. Omalizumab dose, timing of administration, and duration of treatment (<2 years; 2–4 years; > 4 years) were analyzed. Adherence was evaluated by examining rates of expected and missing doses. Good adherence (<10% of doses missed) and poor adherence (>10% doses missed) were determined. For effectiveness in relation to adherence of omalizumab we considered asthma exacerbations, hospitalizations, asthma control test (ACT), and Forced Expiratory Volume in 1 s (FEV1). Results: A total of 196 patients were evaluated, and 161 were suitable for data analyses. Good adherence was shown in 90.7% of patients and poor adherence in 9.3%. Considering adherence in relation to treatment duration: <2 years, 87.8% of patients were adherent (expected doses, 1186; missed doses, 53); 2–4 years, 85.9% were adherent (expected doses, 2985; missed doses, 127); >4 years, 100% were adherent (expected doses, 6120; missed doses, none). Indices of efficacy between pre- and post-treatment showed significant improvement (p < 0.001). The effectiveness indices between pre- and post-treatment, among adherent and non-adherent patients, ACT, and asthma exacerbations both showed significant differences (p = 0.043 and p = 0.049, respectively). Binomial logistic regression analysis showed that increasing age, better ACT score, and 14-day timing were significantly associated with increased adherence to therapy. Conclusions: High adherence to omalizumab was demonstrated in a real-world setting, which was associated with better outcomes and control of asthma. Keywords: Severe asthma, Omalizumab, Adherence, Efficacy, Real-world
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