Rheumatoid Arthritis Patients In Sustained Remission Research Articles

Contrast-enhanced ultrasound as a valuable tool to detect minimal inflammation in RA patients in sustained remission

ObjectiveThe study aimed to explore the utility of contrast-enhanced ultrasound (CEUS) as a tool for detecting minimal inflammation in rheumatoid arthritis (RA) patients in sustained remission (SR) and to correlate the findings with Disease Activity Score 28 (DAS28) status scores and various ultrasound (US) scores.Patients and methodsThirty RA patients in SR (minimum 6 months), 12 with active disease, and 10 healthy controls were included. Clinical evaluations and US assessments were performed, including grayscale US (GSUS), power Doppler US (PDUS), and Global OMERACT-EULAR Synovitis Score (GLOESS). The CEUS was performed in the two most active joints and was scored semi-quantitatively (SQ) and quantitatively.ResultsHealthy controls and remission RA patients had similar total US scores. Active RA patients had higher US scores than the healthy and remission groups, with statistically significant differences in all the groups compared to the healthy group. However, significant differences were only observed in the GSUS and GLOESS when comparing active RA patients with the remission group. Ninety-five joints were selected for the CEUS, and we detected more microvascularization with the SQ CEUS score than with the PDUS in all groups (18 vs. 58% in the remission group; p-value 0.006). The weighted Cohen's kappa for the intra-rater and inter-rater IACUS CEUS score was 0.714 (confidence interval 0.610–0.819, p-value < 0.001) and 0.540 (confidence interval: 0.419–0.662, p-value < 0.001), respectively. Spearman's correlation between the SQ CEUS and quantitative scores was 0.655.ConclusionFor the majority of RA patients in SR, conventional US may fail to detect microvascularization potentially related to the subclinical disease. The CEUS may be helpful for this purpose.

Treatment patterns of individualized real-life tapering approaches based on shared decision-making in rheumatoid arthritis.

To provide real-world evidence on patient-individual tapering patterns of disease-modifying antirheumatic drugs (DMARDs) in rheumatoid arthritis (RA) patients in daily clinical practice. Data obtained through acontrolled prospective cohort study in Germany conducted from July 2018 to March 2021 were analyzed. Participants consist of RA patients in sustained remission who were eligible for DMARD tapering at enrolment. Data from RA patients who experienced tapering of DMARDs at least once during the observational period (n = 200) were used. Descriptive analyses of medical outcomes at baseline and at time of first tapering, time to first tapering, tapering patterns by substance group, and tapering intensity were documented. We did not observe meaningful differences in either disease activity or quality of life measures between substance groups at enrolment, time of first tapering, and at 6or 12months after tapering. Median time until first tapering varied between substance groups (csDMARDs: 108days; bDMARDs: 189days; combination: 119days). Most patients received one iteration of tapering only (147/200patients, 73.5%). Dose reduction was applied for patients treated with csDMARDs (79/86patients, 91.8%), spacing of interval was the most frequent strategy for patients treated with bDMARDs only (43/48patients, 89.5%). Necessity for increased DMARD dosage was observed in only 10% of patients (20/200). Tapering intensity by substance was overall heterogenous, indicating high individualization. We identify highly heterogeneous tapering patterns between substance groups and within substances. Identification and recognition of patient-individual approaches of tapering will help to further improve the management of RA for both patients and rheumatologists.

Impact of sustained remission on quality of life among women with rheumatoid arthritis and systemic lupus erythematosus: a prospective observational study

BackgroundHealth-related quality of life (HRQOL) as a patient reported outcome plays important roles in the life of patients with RA (rheumatoid arthritis) and SLE (Systemic lupus erythematosus) as well as their families. Evaluating the impact of sustained remission on HRQOL is important and could be of potential help in daily practice. Thus, we aimed to assess and compare prospectively the impact of sustained remission on HRQOL in Saudi RA and SLE female cohorts.ResultsSixty-two female patients with active RA and 34 female patients with active SLE fulfilled the inclusion-, entry- and follow-up criteria. At baseline, the SLE patients had significantly better SF-36 scores than the RA patients. In both groups, significant correlations were found between disease activity and physical (PCS) and mental (MCS) components summary of the SF-36 (all p’s ≤ 0.001). In sustained remission, both SLE and RA patients showed significant improvements of the SF-36 scores (p < 0.001) compared to baseline. RA patients in sustained remission had a significantly better general health, bodily pain and physical functioning, and total PCS scores (p < 0.001) than those with SLE.ConclusionsBoth SLE and RA patients in sustained remission showed strongly improved HRQOL. In sustained remission, RA patients had comparable or better HRQOL than SLE patients.

Advanced machine learning for predicting individual risk of flares in rheumatoid arthritis patients tapering biologic drugs

BackgroundBiological disease-modifying anti-rheumatic drugs (bDMARDs) can be tapered in some rheumatoid arthritis (RA) patients in sustained remission. The purpose of this study was to assess the feasibility of building a model to estimate the individual flare probability in RA patients tapering bDMARDs using machine learning methods.MethodsLongitudinal clinical data of RA patients on bDMARDs from a randomized controlled trial of treatment withdrawal (RETRO) were used to build a predictive model to estimate the probability of a flare. Four basic machine learning models were trained, and their predictions were additionally combined to train an ensemble learning method, a stacking meta-classifier model to predict the individual flare probability within 14 weeks after each visit. Prediction performance was estimated using nested cross-validation as the area under the receiver operating curve (AUROC). Predictor importance was estimated using the permutation importance approach.ResultsData of 135 visits from 41 patients were included. A model selection approach based on nested cross-validation was implemented to find the most suitable modeling formalism for the flare prediction task as well as the optimal model hyper-parameters. Moreover, an approach based on stacking different classifiers was successfully applied to create a powerful and flexible prediction model with the final measured AUROC of 0.81 (95%CI 0.73–0.89). The percent dose change of bDMARDs, clinical disease activity (DAS-28 ESR), disease duration, and inflammatory markers were the most important predictors of a flare.ConclusionMachine learning methods were deemed feasible to predict flares after tapering bDMARDs in RA patients in sustained remission.