Loading…

Do Poor Prognostic Factors in Rheumatoid Arthritis Affect Treatment Choices and Outcomes? Analysis of a US Rheumatoid Arthritis Registry

To characterize patients with rheumatoid arthritis (RA) by number of poor prognostic factors (PPF: functional limitation, extraarticular disease, seropositivity, erosions) and evaluate treatment acceleration, clinical outcomes, and work status over 12 months by number of PPF. Using the Corrona RA re...

Full description

Saved in:
Bibliographic Details
Published in:Journal of rheumatology 2018-10, Vol.45 (10), p.1353-1360
Main Authors: Alemao, Evo, Litman, Heather J, Connolly, Sean E, Kelly, Sheila, Hua, Winnie, Rosenblatt, Lisa, Rebello, Sabrina, Kremer, Joel M, Harrold, Leslie R
Format: Article
Language:English
Subjects:
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:To characterize patients with rheumatoid arthritis (RA) by number of poor prognostic factors (PPF: functional limitation, extraarticular disease, seropositivity, erosions) and evaluate treatment acceleration, clinical outcomes, and work status over 12 months by number of PPF. Using the Corrona RA registry (January 2005-December 2015), biologic-naive patients with diagnosed RA having 12-month (± 3 mos) followup were identified and categorized by PPF (0-1, 2, ≥ 3). Changes in medication, Clinical Disease Activity Index (CDAI), and work status (baseline-12 mos) were evaluated using linear and logistic regression models. There were 3458 patients who met the selection criteria: 1489 (43.1%), 1214 (35.1%), and 755 (21.8%) had 0-1, 2, or ≥ 3 PPF, respectively. At baseline, patients with ≥ 3 PPF were older, and had longer RA duration and higher CDAI versus those with 0-1 PPF. In 0-1, 2, and ≥ 3 PPF groups, respectively, 20.9%, 23.2%, and 26.5% of patients received ≥ 1 biologic (p = 0.011). Biologic/targeted synthetic disease-modifying antirheumatic drug (tsDMARD) use was similar in patients with/without PPF (p = 0.57). After adjusting for baseline CDAI, mean (standard error) change in CDAI was -4.95 (0.24), -4.53 (0.27), and -2.52 (0.34) for 0-1, 2, and ≥ 3 PPF groups, respectively. More patients were working at baseline but not at 12-month followup in 2 (13.9%) and ≥ 3 (12.5%) versus 0-1 (7.3%) PPF group. Despite high disease activity and worse clinical outcomes, number of PPF did not significantly predict biologic/tsDMARD use. This may warrant reconsideration of the importance of PPF in treat-to-target approaches.
ISSN:0315-162X
1499-2752
DOI:10.3899/jrheum.171050