Rheumatoid arthritis patients' perspective on the use of prediction models in clinical decision-making.

OBJECTIVES: A rapidly expanding number of prediction models is being developed aiming to improve rheumatoid arthritis (RA) diagnosis and treatment. However, few are actually implemented in clinical practice. This study explores factors influencing the acceptance of prediction models in clinical decision-making by RA patients. METHODS: A qualitative study design was used with thematic analysis of semi-structured interviews. Purposive sampling was applied to capture a complete overview of influencing factors. The interview topic list was based on pilot data. RESULTS: Data saturation was reached after 12 interviews. Patients were generally positive about the use of prediction models in clinical decision-making. Six key themes were identified from the interviews. First, patients have the need for information on prediction models. Second, factors influencing trust in model-supported treatment are described. Third, patients envision the model to have a supportive role in clinical decision-making. Fourth, patients hope to personally benefit from model-supported treatment in various ways. Fifth, patients are willing to contribute time and effort to contribute to model input. And lastly, we discuss the theme on effects of the relationship with the caregiver in model-supported treatment. CONCLUSION: Within this study RA patients were generally positive about the use of prediction models in their treatment given some conditions were met and concerns addressed. The results of this study can be used during the development and implementation in RA care of prediction models in order to enhance patient acceptability.

Clinical Validation of a Primary Antibody Deficiency Screening Algorithm for Primary Care.

PURPOSE: The diagnostic delay of primary antibody deficiencies (PADs) is associated with increased morbidity, mortality, and healthcare costs. Therefore, a screening algorithm was previously developed for the early detection of patients at risk of PAD in primary care. We aimed to clinically validate and optimize the PAD screening algorithm by applying it to a primary care database in the Netherlands. METHODS: The algorithm was applied to a data set of 61,172 electronic health records (EHRs). Four hundred high-scoring EHRs were screened for exclusion criteria, and remaining patients were invited for serum immunoglobulin analysis and referred if clinically necessary. RESULTS: Of the 104 patients eligible for inclusion, 16 were referred by their general practitioner for suspected PAD, of whom 10 had a PAD diagnosis. In patients selected by the screening algorithm and included for laboratory analysis, prevalence of PAD was ~ 1:10 versus 1:1700-1:25,000 in the general population. To optimize efficiency of the screening process, we refitted the algorithm with the subset of high-risk patients, which improved the area under the curve-receiver operating characteristics curve value to 0.80 (95% confidence interval 0.63-0.97). We propose a two-step screening process, first applying the original algorithm to distinguish high-risk from low-risk patients, then applying the optimized algorithm to select high-risk patients for serum immunoglobulin analysis. CONCLUSION: Using the screening algorithm, we were able to identify 10 new PAD patients from a primary care population, thus reducing diagnostic delay. Future studies should address further validation in other populations and full cost-effectiveness analyses. REGISTRATION: Clinicaltrials.gov record number NCT05310604, first submitted 25 March 2022.

What is the best target in a treat-to-target strategy in rheumatoid arthritis? Results from a systematic review and meta-regression analysis.

OBJECTIVES: A treat-to-target (T2T) strategy has been shown to be superior to usual care in rheumatoid arthritis (RA), but the optimal target remains unknown. Targets are based on a disease activity measure (eg, Disease Activity Score-28 (DAS28), Simplified Disease Activity Indices/Clinical Disease Activity Indices (SDAI/CDAI), and a cut-off such as remission or low disease activity (LDA). Our aim was to compare the effect of different targets on clinical and radiographic outcomes. METHODS: Cochrane, Embase and (pre)MEDLINE databases were searched (1 June 2022) for randomised controlled trials and cohort studies after 2003 that applied T2T in RA patients for ≥12 months. Data were extracted from individual T2T study arms; risk of bias was assessed with the Cochrane Collaboration tool. Using meta-regression, we evaluated the effect of the target used on clinical and radiographic outcomes, correcting for heterogeneity between and within studies. RESULTS: 115 treatment arms were used in the meta-regression analyses. Aiming for SDAI/CDAI-LDA was statistically superior to targeting DAS-LDA regarding DAS-remission and SDAI/CDAI/Boolean-remission outcomes over 1-3 years. Aiming for SDAI/CDAI-LDA was also significantly superior to DAS-remission regarding both SDAI/CDAI/Boolean-remission (over 1-3 years) and mean SDAI/CDAI (over 1 year). Targeting DAS-remission rather than DAS-LDA only improved the percentage of patients in DAS-remission, and only statistically significantly after 2-3 years of T2T. No differences were observed in Health Assessment Questionnaire and radiographic progression. CONCLUSIONS: Targeting SDAI/CDAI-LDA, and to a lesser extent DAS-remission, may be superior to targeting DAS-LDA regarding several clinical outcomes. However, due to the risk of residual confounding and the lack of data on (over)treatment and safety, future studies should aim to directly and comprehensively compare targets. PROSPERO REGISTRATION NUMBER: CRD42021249015.

Development of a primary care screening algorithm for the early detection of patients at risk of primary antibody deficiency.

BACKGROUND: Primary antibody deficiencies (PAD) are characterized by a heterogeneous clinical presentation and low prevalence, contributing to a median diagnostic delay of 3-10 years. This increases the risk of morbidity and mortality from undiagnosed PAD, which may be prevented with adequate therapy. To reduce the diagnostic delay of PAD, we developed a screening algorithm using primary care electronic health record (EHR) data to identify patients at risk of PAD. This screening algorithm can be used as an aid to notify general practitioners when further laboratory evaluation of immunoglobulins should be considered, thereby facilitating a timely diagnosis of PAD. METHODS: Candidate components for the algorithm were based on a broad range of presenting signs and symptoms of PAD that are available in primary care EHRs. The decision on inclusion and weight of the components in the algorithm was based on the prevalence of these components among PAD patients and control groups, as well as clinical rationale. RESULTS: We analyzed the primary care EHRs of 30 PAD patients, 26 primary care immunodeficiency patients and 58,223 control patients. The median diagnostic delay of PAD patients was 9.5 years. Several candidate components showed a clear difference in prevalence between PAD patients and controls, most notably the mean number of antibiotic prescriptions in the 4 years prior to diagnosis (5.14 vs. 0.48). The final algorithm included antibiotic prescriptions, diagnostic codes for respiratory tract and other infections, gastro-intestinal complaints, auto-immune symptoms, malignancies and lymphoproliferative symptoms, as well as laboratory values and visits to the general practitioner. CONCLUSIONS: In this study, we developed a screening algorithm based on a broad range of presenting signs and symptoms of PAD, which is suitable to implement in primary care. It has the potential to considerably reduce diagnostic delay in PAD, and will be validated in a prospective study. Trial registration The consecutive prospective study is registered at clinicaltrials.gov under NCT05310604.

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).

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.

BACKGROUND: Biological disease-modifying antirheumatic drugs (bDMARDs) are effective in the treatment of rheumatoid arthritis. However, as bDMARDs may also lead to adverse events and are expensive, tapering them is of great clinical interest. Tapering according to disease activity-guided dose optimization (DGDO) does not seem to affect long term remission rates, but flares are frequent during this process. Our objective was to develop a model for the prediction of flares during bDMARD tapering using data from routine care and to evaluate its potential clinical impact. METHODS: We used a joint latent class model to repeatedly predict the probability of a flare occurring within the next 3 months. The model was developed using longitudinal data on disease activity (DAS28) and other routine care data from two clinics. Predictive accuracy was assessed in cross-validation and external validation was performed with data from the DRESS (Dose REduction Strategy of Subcutaneous tumor necrosis factor inhibitors) trial. Additionally, we simulated the reduction in number of flares and bDMARD dose when implementing the model as a decision aid during bDMARD tapering in the DRESS trial. RESULTS: Data from 279 bDMARD courses were used for model development. The final model included two latent DAS28-trajectories, bDMARD type and dose, disease duration, and seropositivity. The area under the curve of the final model was 0.76 (0.69-0.83) in cross-validation and 0.68 (0.62-0.73) in external validation. In simulation of prediction-aided decisions, the mean number of flares over 18 months decreased from 1.21 (0.99-1.43) to 0.75 (0.54-0.96). The reduction in he bDMARD dose was mostly maintained, increasing from 54 to 64% of full dose. CONCLUSIONS: We developed a dynamic flare prediction model, exclusively based on data typically available in routine care. Our results show that using this model to aid decisions during bDMARD tapering may significantly reduce the number of flares while maintaining most of the bDMARD dose reduction. TRIAL REGISTRATION: The clinical impact of the prediction model is currently under investigation in the PATIO randomized controlled trial (Dutch Trial Register number NL9798).

Identification and prediction of difficult-to-treat rheumatoid arthritis patients in structured and unstructured routine care data: results from a hackathon.

BACKGROUND: The new concept of difficult-to-treat rheumatoid arthritis (D2T RA) refers to RA patients who remain symptomatic after several lines of treatment, resulting in a high patient and economic burden. During a hackathon, we aimed to identify and predict D2T RA patients in structured and unstructured routine care data. METHODS: Routine care data of 1873 RA patients were extracted from the Utrecht Patient Oriented Database. Data from a previous cross-sectional study, in which 152 RA patients were clinically classified as either D2T or non-D2T, served as a validation set. Machine learning techniques, text mining, and feature importance analyses were performed to identify and predict D2T RA patients based on structured and unstructured routine care data. RESULTS: We identified 123 potentially new D2T RA patients by applying the D2T RA definition in structured and unstructured routine care data. Additionally, we developed a D2T RA identification model derived from a feature importance analysis of all available structured data (AUC-ROC 0.88 (95% CI 0.82-0.94)), and we demonstrated the potential of longitudinal hematological data to differentiate D2T from non-D2T RA patients using supervised dimension reduction. Lastly, using data up to the time of starting the first biological treatment, we predicted future development of D2TRA (AUC-ROC 0.73 (95% CI 0.71-0.75)). CONCLUSIONS: During this hackathon, we have demonstrated the potential of different techniques for the identification and prediction of D2T RA patients in structured as well as unstructured routine care data. The results are promising and should be optimized and validated in future research.