Polypharmacy is Associated With Treatment Response and Serious Adverse Events

Results From The British Society for Rheumatology Biologics Register for Rheumatoid Arthritis

Katie Bechman; Benjamin D. Clarke; Andrew I. Rutherford; Mark Yates; Elena Nikiphorou; Mariam Molokhia; Sam Norton; Andrew P. Cope; Kimme L. Hyrich; James B. Galloway

Disclosures

Rheumatology. 2019;58(10):1767-1776. 

In This Article

Abstract and Introduction

Abstract

Objective: To evaluate whether polypharmacy is associated with treatment response and serious adverse events (SAEs) in patients with RA using data from the British Society for Rheumatology Biologics Register (BSRBR-RA).

Methods: The BSRBR-RA is a prospective observational cohort study of biologic therapy starters and a DMARD comparator arm. A logistic regression model was used to calculate the odds of a EULAR 'good response' after 12 months of biologic therapy by medication count. Cox proportional hazards models were used to identify risk of SAEs. The utility of the models were compared with the Rheumatic Disease Comorbidity Index using Receiver Operator Characteristic and Harrell's C statistic.

Results: The analysis included 22 005 patients, of which 83% were initiated on biologics. Each additional medication reduced the odds of a EULAR good response by 8% [odds ratios 0.92 (95% CI 0.91, 0.93) P < 0.001] and 3% in the adjusted model [adjusted odds ratios 0.97 (95% CI 0.95, 0.98) P < 0.001]. The Receiver Operator Characteristic demonstrated significantly greater areas under the curve with the polypharmacy model than the Rheumatic Disease Comorbidity Index. There were 12 547 SAEs reported in 7286 patients. Each additional medication equated to a 13% increased risk of an SAE [hazard ratio 1.13 (95% CI 1.12, 1.13) P < 0.001] and 6% in the adjusted model [adjusted hazard ratio 1.06 (95% CI 1.05, 1.07) P < 0.001]. Predictive values for SAEs were comparable between the polypharmacy and Rheumatic Disease Comorbidity Index model.

Conclusion: Polypharmacy is a simple but valuable predictor of clinical outcomes in patients with RA. This study supports medication count as a valid measure for use in epidemiologic analyses.

Introduction

Polypharmacy, the prescribing of multiple drugs for an individual, is rising in prevalence. Half of patients over 65 are prescribed five or more medications.[1] This has quadrupled over the last 20 years,[1] a consequence of an ageing population with comorbidities, and therapeutics advances with treatment guidelines advocating multiple medications.

In the treatment of RA, prescribing of multiple DMARDs is advocated, with recommendation to commence combination therapy early.[2–4] Together with the day-to-day use of other medications to manage pain and to counter side effects, polypharmacy in this cohort is intensified. Despite dramatic improvements in the prognosis of RA, morbidity remains high.[5,6] This is a consequence of the comorbidity burden, which has increased considerably over recent decades,[7] with an increased prevalence of cardiovascular disease, infections, malignancy and psychiatric illness.[8–10] Comorbidities are associated with worse quality of life and functional status,[11] and are a confounder in analysis of clinical outcomes in RA.[12]

In epidemiological research, comorbidity indices select and weight illnesses to quantify collective burden. The choice of model depends upon patient population and outcome of interest. The Rheumatic Disease Comorbidity Index (RDCI) is composed of 11 weighted past or present comorbid conditions and performs well in predicting disease-specific outcomes; including disability, medical costs, hospitalization and death.[11,13,14] Comorbidity indices are reliant on accurate reporting, which is influenced by reporting methods i.e. physician vs patient.[15] When correctly recorded, a binary code denotes the presence of a comorbidity, but does not reflect its severity. Medication count and polypharmacy are gaining interest as surrogates of comorbidity burden. There has been an expansion in the use of real-world data captured from routine sources such as electronic health records, where medication use is meticulously recorded (prescribing in UK primary care is almost exclusively electronic). Electronic health records have been utilized to support observational studies, as a stand-alone data source, or following linkage to administrative datasets.[16,17]

The impact of polypharmacy on treatment outcomes in RA is largely unknown. As a surrogate for comorbidity, it may exert a similar effect. This would have important implications when making treatment decisions. From an epidemiological perspective, medication count may prove a valuable tool in case mix adjustment. The primary objective of this study was to evaluate whether polypharmacy associates with treatment outcomes and serious adverse events (SAEs) in RA, and to establish whether polypharmacy represents a surrogate for comorbidity when adjusting for confounding in epidemiologic analyses.

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