PT  - JOURNAL ARTICLE
AU  - E. M. Heather
AU  - K. Payne
AU  - M. J. Harrison
AU  - B. Control Centre Consortium
AU  - K. L. Hyrich
AU  - D. P. Symmons
TI  - OP0065 Quantifying the Economic Impact of Serious Infections from Anti-TNFS for Rheumatoid Arthritis: Results from the BSRBR-RA
AID  - 10.1136/annrheumdis-2013-eular.270
DP  - 2013 Jun 01
TA  - Annals of the Rheumatic Diseases
PG  - A72--A72
VI  - 72
IP  - Suppl 3
4099  - http://ard.bmj.com/content/72/Suppl_3/A72.1.short
4100  - http://ard.bmj.com/content/72/Suppl_3/A72.1.full
SO  - Ann Rheum Dis2013 Jun 01; 72
AB  - Background Anti-tumour necrosis factor drugs (anti-TNFs) for adult rheumatoid arthritis (RA) are associated with an increased risk of serious infections (SIs) compared to non-biologic disease modifying drugs (nbDMARDs). SIs, requiring hospitalisation or outpatient treatment with IV antibiotics, will likely impact on the relative cost-effectiveness (CE) of anti-TNFs given the associated increased use of scarce healthcare resources. Objectives To examine whether the length of hospital stay (LoS) following a SI could be predicted on the basis of treatment received and baseline patient characteristics. Methods Patients with RA treated with nbDMARDs or anti-TNFs (adalimumab; etanercept; infliximab) who experienced a SI in the first year of follow-up were identified from the BSRBR-RA database. Patient characteristics recorded at baseline included: age; sex; disease duration; treatment history; comorbidities; HAQ; and DAS28. Data on SIs and related LoS were collated from (i) bi-annual patient diaries (ii) bi-annual rheumatologist questionnaires (iii) death certificate data from the NHS Information Centre. SIs were ascribed to the most recent drug recorded, if diagnosed whilst on-drug or within 90 days of the first missed dose. Patients were (i) censored after a first SI (ii) excluded if switching anti-TNFs within the first year. Kruskal-Wallis tests were used for between-treatment comparisons. A generalised linear regression model (log-link function; negative-binomial distribution) was fitted to identify the key factors driving LoS in hospital following a SI. Potential confounders were included as covariates and missing baseline data imputed. Results Of the 15395 patients contributing 14027 patient years (pyrs) of follow-up (nbDMARDs: 3295 pyrs; anti-TNF: 10732 pyrs), 838 experienced a SI in the first year of treatment. Unadjusted rates of SIs were significantly higher in the anti-TNF cohort: 67.5 (95% CI 62.6-72.5) vs. 35.2 (28.5-41.6) per 1000 pyrs (p<0.001). 83 nbDMARD and 590 anti-TNF patients were hospitalised. The mean LoS did not differ significantly between the nbDMARD and anti-TNF cohort (11.3 vs. 10.7 days; p=0.46). The key factors associated with an increased LoS were: female gender (+2.3 days); age ≥75years (+4.7 days); having 1-3 comorbidities (+2.0 days); failing 1-5 nbDMARDs (+2.3 days). There was no significant difference in the LoS between anti-TNFs. Conclusions Previous findings - that SIs occur more frequently with anti-TNFs compared to nbDMARDs - are confirmed. LoS was related to demographic factors, prior treatment history and co-morbidity but not to current treatment. Nevertheless anti-TNFs appear to result in additional healthcare costs from SI-related hospitalisations due to their increased frequency. These findings are important for understanding the relative economic impact of anti-TNFs and indicate that SIs should be considered when considering the CE of these agents. Acknowledgements EH is supported by an NIHR Methods Fellowship. Disclosure of Interest None Declared