Accepted for/Published in: JMIR Medical Informatics
Date Submitted: Dec 29, 2020
Date Accepted: Apr 23, 2021
Predicting biologic therapy outcome in spondyloarthritis' patients using joint models for longitudinal and survival analysis
ABSTRACT
Background:
Rheumatic diseases are one of the most common chronic diseases worldwide. Among them, spondyloarthritis (SpA) is a group of highly debilitating diseases, with an early onset age, significantly impacting patients’ quality of life, health care systems, and society in general. Recent treatment options consist of using biologic therapies, and establishing the most beneficial one according to the patient’s characteristics is a challenge that needs solving. Simultaneously, the emerging availability of electronic medical records (EMR) urges the development of methods that can extract insightful information while handling all the challenges of dealing with complex, real-world data.
Objective:
To achieve a better understanding of spondyloarthritis (SpA) patients’ therapy responses and identify the predictors that affect them, therefore enabling the prognosis of therapy success or failure.
Methods:
A data mining approach based on joint models for the survival analysis of the biologic therapy failure is proposed, considering the information of both baseline and time-varying variables extracted from the electronic medical records of SpA patients from the database Reuma.pt.
Results:
Our results show that being a male, starting the biologic therapy at an older age, having a larger time interval between disease start and initiation of the first biologic and being Human Leukocyte Antigen (HLA)-B27 positive are indicators of a good prognosis for the biological drug survival whilst having the disease beginning or starting the biologic therapy in more recent years, a larger number of education years, and higher values of C-reactive protein (CRP) or Bath Ankylosing Spondylitis Functional Index (BASFI) at baseline are all predictors of a greater risk of failure of the first biologic therapy. The biologic Certolizumab seems promising in terms of drug survival, although the sample size is still small.
Conclusions:
Joint models proved to be a valuable tool for the analysis of electronic medical records in the field of rheumatic diseases, allowing the identification of potential predictors of biologic therapy failure.
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