Effect of disease-modifying anti-rheumatic drugs on bone structure and strength in psoriatic arthritis patients

Objectives To address whether the use of methotrexate (MTX) and biological disease-modifying anti-rheumatic drugs (bDMARDs) impacts bone structure and biomechanical properties in patients with psoriatic arthritis (PsA). Methods This is a cross-sectional study in PsA patients receiving no DMARDs, MTX, or bDMARDs. Volumetric bone mineral densities (vBMDs), microstructural parameters, and biomechanical properties (stiffness/failure load) were determined by high-resolution peripheral quantitative CT and micro-finite element analysis in the respective groups. Bone parameters were compared between PsA patients with no DMARDs and those receiving any DMARDs, MTX, or bDMARDs, respectively. Results One hundred sixty-five PsA patients were analyzed, 79 received no DMARDs, 86 received DMARDs, of them 52 bDMARDs (TNF, IL-17- or IL-12/23 inhibitors) and 34 MTX. Groups were balanced for age, sex, comorbidities, functional index, and bone-active therapy, while disease duration was longest in the bDMARD group (7.8 ± 7.4 years), followed by the MTX group (4.6 ± 7.4) and the no-DMARD group (2.9 ± 5.2). No difference in bone parameters was found between the no-DMARD group and the MTX group. In contrast, the bDMARD group revealed significantly higher total (p = 0.001) and trabecular vBMD (p = 0.005) as well as failure load (p = 0.012) and stiffness (p = 0.012). In regression models, age and bDMARDs influenced total vBMD, while age, sex, and bDMARDs influenced failure load and stiffness. Conclusion Despite longer disease duration, bDMARD-treated PsA patients benefit from higher bone mass and better bone strength than PsA patients receiving MTX or no DMARDs. These data support the concept of better control of PsA-related bone disease by bDMARDs

Bone Mass, Bone Microstructure and Biomechanics in Patients with Hand Osteoarthritis

The impact of primary hand osteoarthritis (HOA) on bone mass, microstructure, and biomechanics in the affected skeletal regions is largely unknown. HOA patients and healthy controls (HCs) underwent high‐resolution peripheral quantitative computed tomography (HR‐pQCT). We measured total, trabecular, and cortical volumetric bone mineral densities (vBMDs), microstructural attributes, and performed micro–finite element analysis for bone strength. Failure load and scaled multivariate outcome matrices from distal radius and second metacarpal (MCP2) head measurements were analyzed using multiple linear regression adjusting for age, sex, and functional status and reported as adjusted Z‐score differences for total and direct effects. A total of 105 subjects were included (76 HC: 46 women, 30 men; 29 HOA: 23 women, six men). After adjustment, HOA was associated with significant changes in the multivariate outcome matrix from the MCP2 head (p < .001) (explained by an increase in cortical vBMD (Δz = 1.07, p = .02) and reduction in the trabecular vBMD (Δz = −0.07, p = .09). Distal radius analysis did not show an overall effect of HOA; however, there was a gender‐study group interaction (p = .044) explained by reduced trabecular vBMD in males (Δz = −1.23, p = .02). HOA was associated with lower failure load (−514 N; 95%CI, −1018 to −9; p = 0.05) apparent in males after adjustment for functional status. HOA is associated with reduced trabecular and increased cortical vBMD in the MCP2 head and a reduction in radial trabecular vBMD and bone strength in males. Further investigations of gender‐specific changes of bone architecture in HOA are warranted. © 2020 The Authors. Journal of Bone and Mineral Research published by American Society for Bone and Mineral Research

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

Background!#!Biological 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.!##!Methods!#!Longitudinal 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.!##!Results!#!Data 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.!##!Conclusion!#!Machine learning methods were deemed feasible to predict flares after tapering bDMARDs in RA patients in sustained remission

Accuracy, patient-perceived usability, and acceptance of two symptom checkers (Ada and Rheport) in rheumatology: interim results from a randomized controlled crossover trial

Background!#!Timely diagnosis and treatment are essential in the effective management of inflammatory rheumatic diseases (IRDs). Symptom checkers (SCs) promise to accelerate diagnosis, reduce misdiagnoses, and guide patients more effectively through the health care system. Although SCs are increasingly used, there exists little supporting evidence.!##!Objective!#!To assess the diagnostic accuracy, patient-perceived usability, and acceptance of two SCs: (1) Ada and (2) Rheport.!##!Methods!#!Patients newly presenting to a German secondary rheumatology outpatient clinic were randomly assigned in a 1:1 ratio to complete Ada or Rheport and consecutively the respective other SCs in a prospective non-blinded controlled randomized crossover trial. The primary outcome was the accuracy of the SCs regarding the diagnosis of an IRD compared to the physicians' diagnosis as the gold standard. The secondary outcomes were patient-perceived usability, acceptance, and time to complete the SC.!##!Results!#!In this interim analysis, the first 164 patients who completed the study were analyzed. 32.9% (54/164) of the study subjects were diagnosed with an IRD. Rheport showed a sensitivity of 53.7% and a specificity of 51.8% for IRDs. Ada's top 1 (D1) and top 5 disease suggestions (D5) showed a sensitivity of 42.6% and 53.7% and a specificity of 63.6% and 54.5% concerning IRDs, respectively. The correct diagnosis of the IRD patients was within the Ada D1 and D5 suggestions in 16.7% (9/54) and 25.9% (14/54), respectively. The median System Usability Scale (SUS) score of Ada and Rheport was 75.0/100 and 77.5/100, respectively. The median completion time for both Ada and Rheport was 7.0 and 8.5 min, respectively. Sixty-four percent and 67.1% would recommend using Ada and Rheport to friends and other patients, respectively.!##!Conclusions!#!While SCs are well accepted among patients, their diagnostic accuracy is limited to date.!##!Trial registration!#!DRKS.de, DRKS00017642 . Registered on 23 July 2019