TBA (17A152)

Does comorbidity adversely impact upon treatment response in patients with rheumatoid arthritis? A retrospective analysis of routine care data

Author(s)

Catherine Hughes, Nicola Gullick

Department(s)/Institutions

King's College Hospital, London, UK

Introduction

Patients with Rheumatoid Arthritis (RA) have on average 1.6 comorbidities, and the number of comorbidities increases with age.

The 2016 the European League Against Rheumatism (EULAR) published a list of six comorbidities of particular importance as they occur more frequently, affect clinical outcomes and often they are poorly managed.

These are: cardiovascular disease, malignancy, infections, gastrointestinal disease, osteoporosis and depression.

Aims/Background

The aim of this project was to examine the link between comorbidity and treatment response in RA patients treated with biologic agents.

Method

A systematic literature review of EMBASE and Ovid Medline was carried out up until 7th January 2017.

Retrospective data was gathered from the King’s College London Virtual Biologics Clinic on patients who attended this clinic from 2013-2016.

Statistical analysis was completed using STATA to assess the impact of comorbidity on treatment response.

Results

The literature review identified 5 articles and 2 abstracts, all of which showed that comorbidity impacts negatively on treatment response in patients who are treated with biologic agents.

Baseline characteristics are in Table 2. Age and BMI were significantly increased in patients with comorbidity compared to those with no comorbidity. Patients with multi-morbidity were more likely to receive a DMARD as well as their biologic treatment compared to those without multi-morbidity.

There were no statistical significant findings in terms of DAS change, EULAR good response and DAS28 remission in those with or without comorbidity and with or without multi-morbidity.

Further analysis using logistic regression examining those with comorbidity and a combination of predictor factors (age, gender, BMI, DMARD use, smoking status) showed a trend towards statistical significance (p=0.057).

Conclusions

Despite limited available research, the literature review supported the hypothesis that greater comorbidity burden and older age at disease onset could reduce chances of achieving a good treatment response.

Retrospective data analysis of the King’s dataset did not support this hypothesis, however there were limitations in the routine data collected.

Certain trends in the data suggest that age, gender, BMI, DMARD use, smoking status may impact on ability to achieve a good EULAR response.