Objective To determine the association between joint structure and gait in patients with knee osteoarthritis (OA). Methods IMI-APPROACH recruited 297 clinical knee OA patients. Gait data was... Show moreObjective To determine the association between joint structure and gait in patients with knee osteoarthritis (OA). Methods IMI-APPROACH recruited 297 clinical knee OA patients. Gait data was collected (GaitSmart®) and OA-related joint measures determined from knee radiographs (KIDA) and MRIs (qMRI/MOAKS). Patients were divided into those with/without radiographic OA (ROA). Principal component analyses (PCA) were performed on gait parameters; linear regression models were used to evaluate whether image-based structural and demographic parameters were associated with gait principal components. Results Two hundred seventy-one patients (age median 68.0, BMI 27.0, 77% female) could be analyzed; 149 (55%) had ROA. PCA identifed two components: upper leg (primarily walking speed, stride duration, hip range of motion [ROM], thigh ROM) and lower leg (calf ROM, knee ROM in swing and stance phases). Increased age, BMI, and radiographic subchondral bone density (sclerosis), decreased radiographic varus angle deviation, and female sex were statistically signifcantly associated with worse lower leg gait (i.e. reduced ROM) in patients without ROA (R2=0.24); in ROA patients, increased BMI, radiographic osteophytes, MRI meniscal extrusion and female sex showed signifcantly worse lower leg gait (R2=0.18). Higher BMI was signifcantly associated with reduced upper leg function for non-ROA patients (R2=0.05); ROA patients with male sex, higher BMI and less MRI synovitis showed signifcantly worse upper leg gait (R2=0.12). Conclusion Structural OA pathology was signifcantly associated with gait in patients with clinical knee OA, though BMI may be more important. While associations were not strong, these results provide a signifcant association between OA symptoms (gait) and joint structure. Show less
In the Innovative Medicine's Initiative Applied Public-Private Research enabling OsteoArthritis Clinical Headway (IMI-APPROACH) knee osteoarthritis (OA) study, machine learning models were trained... Show moreIn the Innovative Medicine's Initiative Applied Public-Private Research enabling OsteoArthritis Clinical Headway (IMI-APPROACH) knee osteoarthritis (OA) study, machine learning models were trained to predict the probability of structural progression (s-score), predefined as >0.3 mm/year joint space width (JSW) decrease and used as inclusion criterion. The current objective was to evaluate predicted and observed structural progression over 2 years according to different radiographic and magnetic resonance imaging (MRI)-based structural parameters. Radiographs and MRI scans were acquired at baseline and 2-year follow-up. Radiographic (JSW, subchondral bone density, osteophytes), MRI quantitative (cartilage thickness), and MRI semiquantitative [SQ; cartilage damage, bone marrow lesions (BMLs), osteophytes] measurements were obtained. The number of progressors was calculated based on a change exceeding the smallest detectable change (SDC) for quantitative measures or a full SQ-score increase in any feature. Prediction of structural progression based on baseline s-scores and Kellgren-Lawrence (KL) grades was analyzed using logistic regression. Among 237 participants, around 1 in 6 participants was a structural progressor based on the predefined JSW-threshold. The highest progression rate was seen for radiographic bone density (39%), MRI cartilage thickness (38%), and radiographic osteophyte size (35%). Baseline s-scores could only predict JSW progression parameters (most P>0.05), while KL grades could predict progression of most MRI-based and radiographic parameters (P<0.05). In conclusion, between 1/6 and 1/3 of participants showed structural progression during 2-year follow-up. KL scores were observed to outperform the machine-learning-based s-scores as progression predictor. The large amount of data collected, and the wide range of disease stage, can be used for further development of more sensitive and successful (whole joint) prediction models. Trial Registration: Clinicaltrials.gov number NCT03883568. Show less
Jansen, M.P.; Roemer, F.W.; Marijnissen, A.K.C.A.; Kloppenburg, M.; Blanco, F.J.; Haugen, I.K.; ... ; Wirth, W. 2023
Objective: Longitudinal weight-bearing radiographic joint space width (JSW) and non-weight-bearing MRI-based cartilage thickness changes often show weak correlations. The current objective was to... Show moreObjective: Longitudinal weight-bearing radiographic joint space width (JSW) and non-weight-bearing MRI-based cartilage thickness changes often show weak correlations. The current objective was to investigate these correlations, and to explore the influence of different factors that could contribute to longitudinal differences between the two methods. Methods: The current study included 178 participants with medial osteoarthritis (OA) out of the 297 knee OA participants enrolled in the IMI-APPROACH cohort. Changes over 2 years in medial JSW (delta JSWmed), minimum JSW (delta JSWmin), and medial femorotibial cartilage thickness (delta MFTC) were assessed using linear regression, using measurements from radiographs and MRI acquired at baseline, 6 months, and 1 and 2 years. Pearson R correlations were calculated. The influence of cartilage quality (T2 mapping), meniscal extrusion (MOAKS scoring), potential pain-induced unloading (difference in knee specific pain scores), and increased loading (BMI) on the correlations was analyzed by dividing participants in groups based on each factor separately, and comparing correlations (slope and strength) between groups using linear regression models. Result: Correlations between delta MFTC and delta JSWmed and delta JSWmin were statistically significant (p < 0.004) but weak (R < 0.35). Correlations were significantly different between groups based on cartilage quality and on meniscal extrusion: only patients with the lowest T2 values and with meniscal extrusion showed significant moderate correlations. Pain-induced unloading or BMI-induced loading did not influence correlations.Conclusions: While the amount of loading does not seem to make a difference, weight-bearing radiographic JSW changes are a better reflection of non-weight-bearing MRI cartilage thickness changes in knees with higher quality cartilage and with meniscal extrusion. Show less
Background: There are multiple measures for assessment of physical function in knee osteoarthritis (OA), but each has its strengths and limitations. The GaitSmart (R) system, which uses inertial... Show moreBackground: There are multiple measures for assessment of physical function in knee osteoarthritis (OA), but each has its strengths and limitations. The GaitSmart (R) system, which uses inertial measurement units (IMUs), might be a user-friendly and objective method to assess function. This study evaluates the validity and responsiveness of GaitSmart (R) motion analysis as a function measurement in knee OA and compares this to Knee Injury and Osteoarthritis Outcome Score (KOOS), Short Form 36 Health Survey (SF-36), 30s chair stand test, and 40m self-paced walk test. Methods: The 2-year Innovative Medicines Initiative-Applied Public-Private Research enabling OsteoArthritis Clinical Headway (IMI-APPROACH) knee OA cohort was conducted between January 2018 and April 2021. For this study, available baseline and 6 months follow-up data (n = 262) was used. Principal component analysis was used to investigate whether above mentioned function instruments could represent one or more function domains. Subsequently, linear regression was used to explore the association between GaitSmart (R) parameters and those function domains. In addition, standardized response means, effect sizes and t-tests were calculated to evaluate the ability of GaitSmart (R) to differentiate between good and poor general health (based on SF-36). Lastly, the responsiveness of GaitSmart (R) to detect changes in function was determined. Results: KOOS, SF-36, 30s chair test and 40m self-paced walk test were first combined into one function domain (total function). Thereafter, two function domains were substracted related to either performance based (objective function) or self-reported (subjective function) function. Linear regression resulted in the highest R-2 for the total function domain: 0.314 (R-2 for objective and subjective function were 0.252 and 0.142, respectively.). Furthermore, GaitSmart (R) was able to distinguish a difference in general health status, and is responsive to changes in the different aspects of objective function (Standardized response mean (SRMs) up to 0.74). Conclusion: GaitSmart (R) analysis can reflect performance based and self-reported function and may be of value in the evaluation of function in knee OA. Future studies are warranted to validate whether GaitSmart (R) can be used as clinical outcome measure in OA research and clinical practice. Show less
Objectives. To assess underlying domains measured by GaitSmart (TM) parameters and whether these are additional to established OA markers including patient reported outcome measures (PROMs) and... Show moreObjectives. To assess underlying domains measured by GaitSmart (TM) parameters 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 R-2) 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 Gaitsmart (TM) may be an additional outcome measure for the evaluation of OA. Show less
Objectives. To assess underlying domains measured by GaitSmart (TM) parameters and whether these are additional to established OA markers including patient reported outcome measures (PROMs) and... Show moreObjectives. To assess underlying domains measured by GaitSmart (TM) parameters 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 R-2) 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 Gaitsmart (TM) may be an additional outcome measure for the evaluation of OA. Show less
