Risk Factors and Adverse Events Poorly Predict Infections and Hypogammaglobulinemia in Granulomatosis with Polyangiitis Patients Receiving Rituximab

Background. 29 GPA patients from the Northern Norway vasculitis disease registry received rituximab (RTX) induction and maintenance. 24% and 31% had, respectively, severe and chronic infections while 45% had hypogammaglobulinemia and 28% discontinued RTX due to hypogammaglobulinemia. The aim of the...

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Bibliographic Details
Published in:Autoimmune Diseases
Main Author: Emilio Besada
Format: Article in Journal/Newspaper
Language:English
Published: Hindawi Limited 2016
Subjects:
Online Access:https://doi.org/10.1155/2016/8095695
https://doaj.org/article/1b9dccf8178247aca089ecd16ef94368
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Summary:Background. 29 GPA patients from the Northern Norway vasculitis disease registry received rituximab (RTX) induction and maintenance. 24% and 31% had, respectively, severe and chronic infections while 45% had hypogammaglobulinemia and 28% discontinued RTX due to hypogammaglobulinemia. The aim of the study was to examine how known predictors and adverse events interacted with adverse events using structural statistical methods. Methods. Five predictors (age, cyclophosphamide, total Ig and CD4/CD8 ratio prior RTX, and type of RTX maintenance regimen) and 4 adverse events (severe and chronic infections, hypogammaglobulinemia, and RTX discontinuation) were modeled in principal component and redundancy analyses. Results. The 5 predictors explained 51% of the variance of the GPA cohort. Models including cyclophosphamide exposure and total Ig level predicted best adverse events. However total Ig level has low R squared. The 2 best combinations of adverse events explained 13% of the variance of the predictors and adverse events. Only chronic infections were associated with combination of all adverse events (P=0.014). Hypogammaglobulinemia did not seem associated with the other adverse events. Conclusions. Traditional risk factors for infections and hypogammaglobulinemia seemed to poorly predict adverse events in our GPA cohort.