Cluster analysis and prediction of treatment outcomes for chronic rhinosinusitis Zachary M. Soler, MD, MSc, J. Madison Hyer, MS, Luke Rudmik, MD, MSc,

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Presentation transcript:

Cluster analysis and prediction of treatment outcomes for chronic rhinosinusitis Zachary M. Soler, MD, MSc, J. Madison Hyer, MS, Luke Rudmik, MD, MSc, Viswanathan Ramakrishnan, PhD, Timothy L. Smith, MD, MPH, Rodney J. Schlosser, MD Journal of Allergy and Clinical Immunology Volume 137, Issue 4, Pages 1054-1062 (April 2016) DOI: 10.1016/j.jaci.2015.11.019 Copyright © 2015 American Academy of Allergy, Asthma & Immunology Terms and Conditions

Fig 1 Clinical algorithm used to define clusters. The above algorithm was used to classify patients into the 5 statistical clusters by using simple clinical measures. Lost Productivity is the number of work days missed in the last 90 days. Printed with permission from Medical University of South Carolina Rhinology. Journal of Allergy and Clinical Immunology 2016 137, 1054-1062DOI: (10.1016/j.jaci.2015.11.019) Copyright © 2015 American Academy of Allergy, Asthma & Immunology Terms and Conditions

Fig 2 SNOT-22 and RSDI overall outcomes between clusters treated with surgery or continuing medication only. *P < .05. Journal of Allergy and Clinical Immunology 2016 137, 1054-1062DOI: (10.1016/j.jaci.2015.11.019) Copyright © 2015 American Academy of Allergy, Asthma & Immunology Terms and Conditions

Fig 3 SNOT-22 outcomes by individual domain between clusters treated with surgery or continuing medication only. *P < .05. Journal of Allergy and Clinical Immunology 2016 137, 1054-1062DOI: (10.1016/j.jaci.2015.11.019) Copyright © 2015 American Academy of Allergy, Asthma & Immunology Terms and Conditions

Fig 4 RSDI outcomes by subscale score between clusters treated with surgery or continuing medication only. *P < .05. Journal of Allergy and Clinical Immunology 2016 137, 1054-1062DOI: (10.1016/j.jaci.2015.11.019) Copyright © 2015 American Academy of Allergy, Asthma & Immunology Terms and Conditions