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- Iannone, Florenzo, et al.
(author)
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Body mass does not impact the clinical response to intravenous abatacept in patients with rheumatoid arthritis. Analysis from the “pan-European registry collaboration for abatacept (PANABA)
- 2017
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In: Clinical Rheumatology. - : Springer Science and Business Media LLC. - 0770-3198 .- 1434-9949. ; 36:4, s. 773-779
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Journal article (peer-reviewed)abstract
- Some evidences suggest that obesity impairs the effectiveness of TNF inhibitors. We examined the impact of body mass index (BMI) on the clinical effectiveness of abatacept in rheumatoid arthritis (RA) patients. This is a pooled analysis of 10 prospective cohorts of RA patients. All patients with available BMI were included in this study. The primary endpoint was drug retention of abatacept in the different BMI categories. Multivariable Cox regression was used to estimate hazard ratios (HRs) for drug discontinuation. A secondary endpoint was EULAR/LUNDEX response rates at 6/12 months. Of the 2015 RA patients initiating therapy with IV abatacept, 380 (18.9%) were classified as obese. Obese patients had more functional disability, and were less often RF positive. The median abatacept retention time was 1.91 years for obese RA patients compared to 2.12 years for non-obese patients (p = 0.15). The risk of abatacept discontinuation was not significantly different for overweight (HR 1.03 (95% CI 0.89–1.19)), or for obese (HR 1.08 (95% CI 0.89–1.30)) compared to normal-weight patients. Rheumatoid factor positivity reduced the risk of abatacept discontinuation (HR 0.83 (95% CI 0.72–0.95)), while previous biologic therapy was positively associated with drug interruption (HRs increasing from 1.68 to 2.16 with the line of treatments). Obese and non-obese patients attained similar rates of EULAR/LUNDEX clinical response at 6/12 months. Drug retention and clinical response rates to abatacept do not seem to be decreased by obesity in RA patients.
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| 2. |
- Kristensen, Lars Erik, et al.
(author)
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Non-pharmacological Effects in Switching Medication : The Nocebo Effect in Switching from Originator to Biosimilar Agent
- 2018
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In: BioDrugs. - : Springer Science and Business Media LLC. - 1173-8804 .- 1179-190X. ; 32:5, s. 397-404
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Journal article (peer-reviewed)abstract
- The nocebo effect is defined as the incitement or the worsening of symptoms induced by any negative attitude from non-pharmacological therapeutic intervention, sham, or active therapies. When a patient anticipates a negative effect associated with an intervention, medication or change in medication, they may then experience either an increase in this effect or experience it de novo. Although less is known about the nocebo effect compared with the placebo effect, widespread interest in the nocebo effect observed with statin therapy and a literature review highlighting the nocebo effect across at least ten different disease areas strongly suggests this is a common phenomenon. This effect has also recently been shown to play a role when introducing a medication or changing an established medication, for example, when switching patients from a reference biologic to a biosimilar. Given the important role biosimilars play in providing cost-effective alternatives to reference biologics, increasing physician treatment options and patient access to effective biologic treatment, it is important that we understand this phenomenon and aim to reduce this effect when possible. In this paper, we propose three key strategies to help mitigate the nocebo effect in clinical practice when switching patients from reference biologic to biosimilar: positive framing, increasing patient and healthcare professionals’ understanding of biosimilars and utilising a managed switching programme.
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| 3. |
- Mongin, Denis, et al.
(author)
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Imputing missing data of function and disease activity in rheumatoid arthritis registers : What is the best technique?
- 2019
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In: RMD Open. - : BMJ. - 2056-5933. ; 5:2
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Journal article (peer-reviewed)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.
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