| 1. |
- Lauper, Kim, et al.
(författare)
-
Oral glucocorticoid use in patients with rheumatoid arthritis initiating TNF-inhibitors, tocilizumab or abatacept : Results from the international TOCERRA and PANABA observational collaborative studies
- 2024
-
Ingår i: Joint Bone Spine. - 1297-319X. ; 91:2
-
Tidskriftsartikel (refereegranskat)abstract
- Objective: To evaluate and compare the use of oral glucocorticoids with three classes of bDMARDs in patients with rheumatoid arthritis (RA). Methods: We included patients from 13 observational registries treated with a TNF-inhibitor, abatacept or tocilizumab and with available information on the use of oral glucocorticoids. The main outcome was oral glucocorticoid withdrawal. A McNemar test was used to analyse the change in the use of glucocorticoids after 1 year. Kaplan-Meier estimates and Cox regressions, adjusted for patient, treatment, and disease characteristics, were used to evaluate glucocorticoid discontinuation in patients with glucocorticoids at baseline. Because of heterogeneity, analyses were done by registers and pooled using random-effects meta-analysis. Results: A total of 12,334 participants treated with TNF-inhibitors, 2100 with tocilizumab and 3229 with abatacept were included. At one-year, oral glucocorticoid use decreased in all treatment groups (odds ratio for stopping vs. starting of 2.19 [95% CI 1.58; 3.04] for TNF-inhibitors, 2.46 [1.39; 4.35] for tocilizumab; 1.73 [1.25; 2.21] for abatacept). Median time to glucocorticoid withdrawal was ≈2 years or more in most countries, with a gradual decrease over time. Compared to TNF-inhibitors, crude hazard ratios of glucocorticoid discontinuation were 0.65[0.48–0.87] for abatacept, and 1.04 [0.76–1.43] for tocilizumab, and adjusted hazard ratios were 1.1 [0.83–1.47] for abatacept, and 1.30 [0.96–1.78] for tocilizumab. Conclusion: After initiation of a bDMARD, glucocorticoid use decreased similarly in all treatment groups. However, glucocorticoid withdrawal was much slower than advocated by current international guidelines. More effort should be devoted to glucocorticoid tapering when low disease activity is achieved.
|
|
| 2. |
- Mongin, Denis, et al.
(författare)
-
Imputing missing data of function and disease activity in rheumatoid arthritis registers : What is the best technique?
- 2019
-
Ingår i: RMD Open. - : BMJ. - 2056-5933. ; 5:2
-
Tidskriftsartikel (refereegranskat)abstract
- Objective To compare several methods of missing data imputation for function (Health Assessment Questionnaire) and for disease activity (Disease Activity Score-28 and Clinical Disease Activity Index) in rheumatoid arthritis (RA) patients. Methods One thousand RA patients from observational cohort studies with complete data for function and disease activity at baseline, 6, 12 and 24 months were selected to conduct a simulation study. Values were deleted at random or following a predicted attrition bias. Three types of imputation were performed: (1) methods imputing forward in time (last observation carried forward; linear forward extrapolation); (2) methods considering data both forward and backward in time (nearest available observation - NAO; linear extrapolation; polynomial extrapolation); and (3) methods using multi-individual models (linear mixed effects cubic regression - LME3; multiple imputation by chained equation - MICE). The performance of each estimation method was assessed using the difference between the mean outcome value, the remission and low disease activity rates after imputation of the missing values and the true value. Results When imputing missing baseline values, all methods underestimated equally the true value, but LME3 and MICE correctly estimated remission and low disease activity rates. When imputing missing follow-up values at 6, 12, or 24 months, NAO provided the least biassed estimate of the mean disease activity and corresponding remission rate. These results were not affected by the presence of attrition bias. Conclusion When imputing function and disease activity in large registers of active RA patients, researchers can consider the use of a simple method such as NAO for missing follow-up data, and the use of mixed-effects regression or multiple imputation for baseline data.
|
|
