Using real-world data to dynamically predict flares during tapering of biological DMARDs in rheumatoid arthritis: development, validation, and potential impact of prediction-aided decisions

Optimizing withdrawal strategies for anti-TNF-α therapies in rheumatoid arthritis

INTRODUCTION

Rheumatoid arthritis (RA) is a chronic autoimmune disease that significantly impacts patients' quality of life. While treatment options have expanded over the years, including the introduction of tumor necrosis factor-alpha (TNFα) inhibitors (TNFi), optimizing withdrawal strategies for these agents remains a challenge.

AREAS COVERED

This review examines the current evidence on TNFi withdrawal strategies in RA, focusing on factors influencing withdrawal decisions such as disease activity monitoring, treatment response, patient characteristics, and biomarkers. A comprehensive literature search was conducted, including randomized controlled trials, observational studies, and expert guidelines. The pathophysiology of RA, current pharmacological agents, and the treat-to-target strategy are discussed to provide a holistic understanding of RA management.

EXPERT OPINION

Withdrawal strategies could be suitable for certain patients, keeping in mind that several factors influence withdrawal decisions, including treatment response, disease activity and monitoring, and patient characteristics. The decision to withdraw TNFi must balance the benefits against the potential risks of disease flare and long-term treatment-related adverse effects. Combining DMARDs and TNFi early improves outcomes, supporting tapering strategies for cost-effectiveness and flare prevention. Future directions, including precision medicine approaches, patient-centered care models, and health economics analyses, are proposed to further optimize RA management and improve patient outcomes.

The Effect of Artificial Intelligence on Patient-Physician Trust: Cross-Sectional Vignette Study

BACKGROUND

Clinical decision support systems (CDSSs) based on routine care data, using artificial intelligence (AI), are increasingly being developed. Previous studies focused largely on the technical aspects of using AI, but the acceptability of these technologies by patients remains unclear.

OBJECTIVE

We aimed to investigate whether patient-physician trust is affected when medical decision-making is supported by a CDSS.

METHODS

We conducted a vignette study among the patient panel (N=860) of the University Medical Center Utrecht, the Netherlands. Patients were randomly assigned into 4 groups-either the intervention or control groups of the high-risk or low-risk cases. In both the high-risk and low-risk case groups, a physician made a treatment decision with (intervention groups) or without (control groups) the support of a CDSS. Using a questionnaire with a 7-point Likert scale, with 1 indicating "strongly disagree" and 7 indicating "strongly agree," we collected data on patient-physician trust in 3 dimensions: competence, integrity, and benevolence. We assessed differences in patient-physician trust between the control and intervention groups per case using Mann-Whitney U tests and potential effect modification by the participant's sex, age, education level, general trust in health care, and general trust in technology using multivariate analyses of (co)variance.

RESULTS

In total, 398 patients participated. In the high-risk case, median perceived competence and integrity were lower in the intervention group compared to the control group but not statistically significant (5.8 vs 5.6; P=.16 and 6.3 vs 6.0; P=.06, respectively). However, the effect of a CDSS application on the perceived competence of the physician depended on the participant's sex (P=.03). Although no between-group differences were found in men, in women, the perception of the physician's competence and integrity was significantly lower in the intervention compared to the control group (P=.009 and P=.01, respectively). In the low-risk case, no differences in trust between the groups were found. However, increased trust in technology positively influenced the perceived benevolence and integrity in the low-risk case (P=.009 and P=.04, respectively).

CONCLUSIONS

We found that, in general, patient-physician trust was high. However, our findings indicate a potentially negative effect of AI applications on the patient-physician relationship, especially among women and in high-risk situations. Trust in technology, in general, might increase the likelihood of embracing the use of CDSSs by treating professionals.

Reproducibility and rigor in rheumatology research

The pillars of scientific progress in rheumatology are experimentation and observation, followed by the publication of reliable and credible results. These data must then be independently verified, validated, and replicated. Peer and journal-specific technical and statistical reviews are paramount to improving rigor and reproducibility. In addition, research integrity, ethics, and responsible conduct training can help to reduce research misconduct and improve scientific evidence. As the number of published articles in rheumatology grows, the field has become critical for determining reproducibility. Prospective, longitudinal, randomized controlled clinical trials are the gold standard for evaluating clinical intervention efficacy and safety in this space. However, their applicability to larger, more representative patient populations with rheumatological disorders worldwide could be limited due to time, technical, and cost constraints involved with large-scale clinical trials. Accordingly, analysis of real-world, patient-centered clinical data retrieved from established healthcare inventories, such as electronic health records, medical billing reports, and disease registries, are increasingly used to report patient outcomes. Unfortunately, it is unknown whether this clinical research paradigm in rheumatology could be deployed in medically underserved regions.

Tapering glucocorticoids and risk of flare in rheumatoid arthritis on biological disease-modifying antirheumatic drugs (bDMARDs)

INTRODUCTION

Glucocorticoids are still a mainstream of rheumatoid arthritis (RA) treatment. Reducing glucocorticoids should be attempted in all patients. However, choosing the right tapering strategy is challenging. The primary aim of our study is to determine the dose-response association between glucocorticoid tapering and risk of flare in RA.

METHODS

We conducted a case-crossover study to determine the factors associated to higher risk of flare in patients with RA. In case-crossover studies time-varying factors are assessed before events (hazard periods) and before control periods. We defined hazard periods as the 6 months immediately preceding flares of RA. Control periods were the 6 months prior to visits without flare. Exposure of interest was the tapering of glucocorticoids to various doses.

RESULTS

508 patients with RA were included in the study and 267 (52.5%) had at least a flare and served as the case-crossover study population. 1545 visits were available for analysis and 345 (22.3%) flares were recorded. Discontinuation of glucocorticoids (ie, tapering to doses of 0 mg/day) and tapering to 0-2.5 mg/day was associated with higher risk of flare (adjusted OR (aOR) of 1.45, 95% CI: 1.13 to 2.24 and aOR of 1.37; 95% CI: 1.06 to 2.01, respectively). Tapering to doses >2.5 mg/day was not associated with significantly higher risk of flare.

CONCLUSIONS

We found that tapering to doses of >2.5 mg/day was generally effective in terms of risk of flare. Flare risk was higher when glucocorticoids were tapered to doses ≤2.5 mg/day. Our study might help design new tapering strategies in patients with RA on biological disease-modifying antirheumatic drugs.

Tapering biologic DMARDs in rheumatoid arthritis

With the arrival of biologics and the shift toward treat-to-target therapy, the possibility of a sustained clinical response has become an achievable goal for many patients with rheumatoid arthritis (RA). Although biologics have revolutionized the treatment of RA, they are costly, potentially inconvenient, and carry risks of side effects. Whether they can or should be tapered in patients with tight disease control is a matter of clinical uncertainty. The major international rheumatology professional societies have all issued guidelines on this question, but across recommendations, consensus is lacking on how and when to consider therapy de-escalation. Recent evidence suggests that sustained remission or low disease activity is more attainable with dose reduction as opposed to outright discontinuation of biologic therapy, and certain predictors of successful taper have begun to be described. This article will (1) summarize the current evidence base for biologic tapering in RA, (2) outline real-world outcomes findings, (3) review important contextual factors relevant to therapy de-escalation, such as cost-effectiveness considerations and patient perspectives, and (4) conclude by summarizing current guidelines.

Prediction Aided Tapering In rheumatoid arthritis patients treated with biOlogicals (PATIO): protocol for a randomized controlled trial

BACKGROUND

Biological disease-modifying anti-rheumatic drugs (bDMARDs) are effective in the treatment of rheumatoid arthritis (RA) but are expensive and increase the risk of infection. Therefore, in patients with a stable low level of disease activity or remission, tapering bDMARDs should be considered. Although tapering does not seem to affect long-term disease control, (short-lived) flares are frequent during the tapering process. We have previously developed and externally validated a dynamic flare prediction model for use as a decision aid during stepwise tapering of bDMARDs to reduce the risk of a flare during this process.

METHODS

In this investigator-initiated, multicenter, open-label, randomized (1:1) controlled trial, we will assess the effect of incorporating flare risk predictions into a bDMARD tapering strategy. One hundred sixty RA patients treated with a bDMARD with stable low disease activity will be recruited. In the control group, the bDMARD will be tapered according to "disease activity guided dose optimization" (DGDO). In the intervention group, the bDMARD will be tapered according to a strategy that combines DGDO with the dynamic flare prediction model, where the next bDMARD tapering step is not taken in case of a high risk of flare. Patients will be randomized 1:1 to the control or intervention group. The primary outcome is the number of flares per patient (DAS28-CRP increase > 1.2, or DAS28-CRP increase > 0.6 with a current DAS28-CRP ≥ 2.9) during the 18-month follow-up period. Secondary outcomes include the number of patients with a major flare (flare duration ≥ 12 weeks), bDMARD dose reduction, adverse events, disease activity (DAS28-CRP) and patient-reported outcomes such as quality of life and functional disability. Health Care Utilization and Work Productivity will also be assessed.

DISCUSSION

This will be the first clinical trial to evaluate the benefit of applying a dynamic flare prediction model as a decision aid during bDMARD tapering. Reducing the risk of flaring during tapering may enhance the safety and (cost)effectiveness of bDMARD treatment. Furthermore, this study pioneers the field of implementing predictive algorithms in clinical practice.

TRIAL REGISTRATION

Dutch Trial Register number NL9798, registered 18 October 2021, https://www.trialregister.nl/trial/9798 . The study has received ethical review board approval (number NL74537.041.20).