Relationship Between Motion, Using the GaitSmartTM System, and Radiographic Knee Osteoarthritis: An Explorative Analysis in the IMI-APPROACH Cohort

Multicenter study[Abstract] Objectives: To assess underlying domains measured by GaitSmartTMparameters and whether these are additional to established OA markers including patient reported outcome measures (PROMs) and radiographic parameters, and to evaluate if GaitSmart analysis is related to the presence and severity of radiographic knee OA. Methods: GaitSmart analysis was performed during baseline visits of participants of the APPROACH cohort (n = 297). Principal component analyses (PCA) were performed to explore structure in relationships between GaitSmart parameters alone and in addition to radiographic parameters and PROMs. Logistic and linear regression analyses were performed to analyse the relationship of GaitSmart with the presence (Kellgren and Lawrence grade ≥2 in at least one knee) and severity of radiographic OA (ROA). Results: Two hundred and eighty-four successful GaitSmart analyses were performed. The PCA identified five underlying GaitSmart domains. Radiographic parameters and PROMs formed additional domains indicating that GaitSmart largely measures separate concepts. Several GaitSmart domains were related to the presence of ROA as well as the severity of joint damage in addition to demographics and PROMs with an area under the receiver operating characteristic curve of 0.724 and explained variances (adjusted R2) of 0.107, 0.132 and 0.147 for minimum joint space width, osteophyte area and mean subchondral bone density, respectively. Conclusions: GaitSmart analysis provides additional information over established OA outcomes. GaitSmart parameters are also associated with the presence of ROA and extent of radiographic severity over demographics and PROMS. These results indicate that GaitsmartTM may be an additional outcome measure for the evaluation of OA

Predicted and actual 2-year structural and pain progression in the IMI-APPROACH knee osteoarthritis cohort

ClinicalTrials.gov, https://clinicaltrials.gov, NCT03883568[Abstract] Objectives: The IMI-APPROACH knee osteoarthritis study used machine learning (ML) to predict structural and/or pain progression, expressed by a structural (S) and pain (P) predicted-progression score, to select patients from existing cohorts. This study evaluates the actual 2-year progression within the IMI-APPROACH, in relation to the predicted-progression scores. Methods: Actual structural progression was measured using minimum joint space width (minJSW). Actual pain (progression) was evaluated using the Knee injury and Osteoarthritis Outcomes Score (KOOS) pain questionnaire. Progression was presented as actual change (Δ) after 2 years, and as progression over 2 years based on a per patient fitted regression line using 0, 0.5, 1 and 2-year values. Differences in predicted-progression scores between actual progressors and non-progressors were evaluated. Receiver operating characteristic (ROC) curves were constructed and corresponding area under the curve (AUC) reported. Using Youden's index, optimal cut-offs were chosen to enable evaluation of both predicted-progression scores to identify actual progressors. Results: Actual structural progressors were initially assigned higher S predicted-progression scores compared with structural non-progressors. Likewise, actual pain progressors were assigned higher P predicted-progression scores compared with pain non-progressors. The AUC-ROC for the S predicted-progression score to identify actual structural progressors was poor (0.612 and 0.599 for Δ and regression minJSW, respectively). The AUC-ROC for the P predicted-progression score to identify actual pain progressors were good (0.817 and 0.830 for Δ and regression KOOS pain, respectively). Conclusion: The S and P predicted-progression scores as provided by the ML models developed and used for the selection of IMI-APPROACH patients were to some degree able to distinguish between actual progressors and non-progressors

Polymorphisms in the multidrug-resistance 1 gene related to glucocorticoid response in rheumatoid arthritis treatment

A substantial proportion of rheumatoid arthritis (RA)-patients experience an insufficient response to glucocorticoids, an important therapeutic agent in RA. The multidrug-resistance 1 (MDR1) gene product P-glycoprotein (P-gp) is an efflux pump that actively transports substrates, such as glucocorticoids, out of the cell. We investigated if the variation in response might be explained by single-nucleotide polymorphisms (SNPs) in the MDR1 gene. RA-patients treated with intravenous methylprednisolone pulses (n = 18) or oral prednisone/prednisolone (n = 22) were included in a prospective cohort, and clinical response was measured after 5 and 30 days, respectively. The C1236T, G2677A/T, and C3435T SNPs were determined, and the functionality of P-gp was assessed by flow cytometry (Rhodamine efflux assay). Carriage of the G2677A/T SNP was significantly associated with response (OR = 6.18, p = 0.035), the other SNPs showed trends. Stratified for received treatment, the effect was only present in methylprednisolone treated patients. Mutant allele carriage significantly decreased functionality of P-gp in B cells, though had a smaller impact in other PBMC subtypes. Carriage of a MDR1 SNP was related to a response to methylprednisolone in this study, which his suggests that RA-patients carrying wild-type alleles might benefit from P-gp inhibition or administration of glucocorticoid analogues that are non-P-gp substrates

Return to Sport and Work after Randomization for Knee Distraction versus High Tibial Osteotomy: Is There a Difference?

Knee joint distraction (KJD) is a novel technique for relatively young knee osteoarthritis (OA) patients. With KJD, an external distraction device creates temporary total absence of contact between cartilage surfaces, which results in pain relief and possibly limits the progression of knee OA. Recently, KJD showed similar clinical outcomes compared with high tibial osteotomy (HTO). Yet, no comparative data exist regarding return to sport (RTS) and return to work (RTW) after KJD. Therefore, our aim was to compare RTS and RTW between KJD and HTO. We performed a cross-sectional follow-up study in patients <65 years who previously participated in a randomized controlled trial comparing KJD and HTO. Out of 62 eligible patients, 55 patients responded and 51 completed the questionnaire (16 KJDs and 35 HTOs) at 5-year follow-up. The primary outcome measures were the percentages of RTS and RTW. Secondary outcome measures included time to RTS/RTW, and pre-and postoperative Tegner's (higher is more active), and Work Osteoarthritis or Joint-Replacement Questionnaire (WORQ) scores (higher is better work ability). Patients' baseline characteristics did not differ. Total 1 year after KJD, 79% returned to sport versus 80% after HTO (not significant [n.s.]). RTS <6 months was 73 and 75%, respectively (n.s.). RTW 1 year after KJD was 94 versus 97% after HTO (n.s.), and 91 versus 87% <6 months (n.s.). The median Tegner's score decreased from 5.0 to 3.5 after KJD, and from 5.0 to 3.0 after HTO (n.s.). The mean WORQ score improvement was higher after HTO (16 ± 16) than after KJD (6 ± 13; p = 0.04). Thus, no differences were found for sport and work participation between KJD and HTO in our small, though first ever, cohort. Overall, these findings may support further investigation into KJD as a possible joint-preserving option for challenging young knee OA patients. The level of evidence is III

Does disease activity add to functional disability in estimation of utility for rheumatoid arthritis patients on biologic treatment?

Objective. Treatment in general is mostly directly aimed at disease activity, and measures such as the DAS28 might therefore present important additional information. Our aim was to develop and validate a model that uses a combination of disease activity (DAS28) and HAQs to estimate EuroQoL 5-dimension scale (EQ5D) utilities. Methods. Longitudinal data from a cohort study in RA patients from the Utrecht Rheumatoid Arthritis Cohort study Group (Stichting Reumaonderzoek Utrecht) who started treatment with a biologic drug were used for mapping and validation. All 702 observations, including DAS28, HAQ and EQ5D assessed at the same time points, were used. The observations were randomly divided into a subset for development of the model (n = 428 observations) and a subset for validation (n = 274). A stepwise multivariable regression analysis was used to test the association of DAS28 (components) and HAQ (domains) with EQ5D. Model performance was assessed using the explained variance (R2) and root mean square errors. Observed and predicted utility scores were compared to check for under- or overestimation of the scores. Finally, the performance of the model was compared with published mapping models. Results. Lower DAS28 score and HAQ items dressing and grooming, arising, eating, walking and activities were associated with higher EQ5D scores. The final model had an explained variance of 0.35 and a lower root mean square error as compared with other models tested. The agreement between predicted and observed scores was fair. Conclusion. HAQ components estimate EQ5D better than total HAQ. Adding DAS28 to HAQ components does not result in better utility estimations

Does disease activity add to functional disability in estimation of utility for rheumatoid arthritis patients on biologic treatment?

Objective. Treatment in general is mostly directly aimed at disease activity, and measures such as the DAS28 might therefore present important additional information. Our aim was to develop and validate a model that uses a combination of disease activity (DAS28) and HAQs to estimate EuroQoL 5-dimension scale (EQ5D) utilities. Methods. Longitudinal data from a cohort study in RA patients from the Utrecht Rheumatoid Arthritis Cohort study Group (Stichting Reumaonderzoek Utrecht) who started treatment with a biologic drug were used for mapping and validation. All 702 observations, including DAS28, HAQ and EQ5D assessed at the same time points, were used. The observations were randomly divided into a subset for development of the model (n = 428 observations) and a subset for validation (n = 274). A stepwise multivariable regression analysis was used to test the association of DAS28 (components) and HAQ (domains) with EQ5D. Model performance was assessed using the explained variance (R2) and root mean square errors. Observed and predicted utility scores were compared to check for under- or overestimation of the scores. Finally, the performance of the model was compared with published mapping models. Results. Lower DAS28 score and HAQ items dressing and grooming, arising, eating, walking and activities were associated with higher EQ5D scores. The final model had an explained variance of 0.35 and a lower root mean square error as compared with other models tested. The agreement between predicted and observed scores was fair. Conclusion. HAQ components estimate EQ5D better than total HAQ. Adding DAS28 to HAQ components does not result in better utility estimations