{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T17:20:59Z","timestamp":1775841659576,"version":"3.50.1"},"reference-count":36,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Ther Adv Respir Dis"],"published-print":{"date-parts":[[2023,1]]},"abstract":"<jats:sec>\n                    <jats:title>Objective:<\/jats:title>\n                    <jats:p>To explore whether baseline clinical biomarkers and characteristics can be used to predict the responsiveness of omalizumab.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods:<\/jats:title>\n                    <jats:p>We retrospectively analyzed a cohort of patients with severe asthma who received omalizumab treatment and collected their baseline data and relevant laboratory examination results along with case records of omalizumab treatment responsiveness after 16\u2009weeks. We compared the differences in variables between the group of patients that responded to omalizumab therapy and the non-responder group, and then performed univariate and multivariate logistic regression. Finally, we analyzed the difference in response rate for subgroups by selecting cut-off values for the variables using Fisher\u2019s exact probability method.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>This retrospective, single-center observational study enrolled 32 patients with severe asthma who were prescribed daily high-dose inhaled corticosteroids and long-acting \u03b22 receptor agonists on long-acting muscarinic receptor antagonists with or without OCS. Data on age, sex, BMI, bronchial thermoplasty, FeNO, serum total IgE, FEV1, blood eosinophils, induced sputum eosinophils, blood basophils, and complications were not significantly different between the responder and non-responder groups. In the univariate and multivariate logistic regression, all the variants were not significant, and we were unable to build a regression model. We used normal high values and the mean or median of variables as cut-off values to create patient subgroups for the variables and found no significant difference in the omalizumab response rate between the subgroups.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion:<\/jats:title>\n                    <jats:p>The responsiveness of omalizumab is not associated with pretreatment clinical biomarkers, and these biomarkers should not be used to predict the responsiveness of omalizumab.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/17534666231170821","type":"journal-article","created":{"date-parts":[[2023,5,6]],"date-time":"2023-05-06T07:07:01Z","timestamp":1683356821000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":8,"title":["Role of clinical biomarkers in predicting the effectiveness of 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