Question: In people with knee osteoarthritis, how much more effective is stratified exercise therapy that distinguishes three subgroups (high muscle strength subgroup, low muscle strength subgroup,... Show moreQuestion: In people with knee osteoarthritis, how much more effective is stratified exercise therapy that distinguishes three subgroups (high muscle strength subgroup, low muscle strength subgroup, obesity subgroup) in reducing knee pain and improving physical function than usual exercise therapy? Design: Pragmatic cluster randomised controlled trial in a primary care setting. Participants: A total of 335 people with knee osteoarthritis: 153 in an experimental arm and 182 in a control arm. Intervention: Physiotherapy practices were randomised into an experimental arm providing stratified ex-ercise therapy (supplemented by a dietary intervention from a dietician for the obesity subgroup) or a control arm providing usual, non-stratified exercise therapy. Outcome measures: Primary outcomes were knee pain severity (numerical rating scale for pain, 0 to 10) and physical function (Knee Injury and Osteoarthritis Outcome Score subscale activities of daily living, 0 to 100). Measurements were performed at baseline, 3 months (primary endpoint) and 6 and 12 months (follow-up). Intention-to-treat, multilevel, regression analysis was performed. Results: Negligible differences were found between the experimental and control groups in knee pain (mean adjusted difference 0.2, 95% CI -0.4 to 0.7) and physical function (-0.8, 95% CI -4.3 to 2.6) at 3 months. Similar effects between groups were also found for each subgroup separately, as well as at other time points and for nearly all secondary outcome measures. Conclusion: This pragmatic trial demonstrated no added value regarding clinical outcomes of the model of stratified exercise therapy compared with usual exercise therapy. This could be attributed to the experimental arm therapists facing difficulty in effectively applying the model (especially in the obesity subgroup) and to elements of stratified exercise therapy possibly being applied in the control arm. Registration: Netherlands National Trial Register NL7463. [Knoop J, Dekker J, van Dongen JM, van der Leeden M, de Rooij M, Peter WFH, de Joode W, van Bodegom-Vos L, Lopuhaa N, Bennell KL, Lems WF, van der Esch M, Vliet Vlieland TPM, Ostelo RWJG (2022) Stratified exercise therapy does not improve outcomes compared with usual exercise therapy in people with knee osteoarthritis (OCTOPuS study): a cluster randomised trial. Journal of Physiotherapy 68:182-190] (c) 2022 Australian Physiotherapy Association. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Show less
Hall, M.; Esch, M. van der; Hinman, R.S.; Peat, G.; Zwart, A. de; Quicke, J.G.; ... ; Bennell, K.L. 2022
Hip and knee osteoarthritis (OA) are leading causes of global disability. Most research to date has focused on the knee, with results often extrapolated to the hip, and this extends to treatment... Show moreHip and knee osteoarthritis (OA) are leading causes of global disability. Most research to date has focused on the knee, with results often extrapolated to the hip, and this extends to treatment recommendations in clinical guidelines. Extrapolating results from research on knee OA may limit our understanding of disease characteristics specific to hip OA, thereby constraining development and implementation of effective treatments. This review highlights differences between hip and knee OA with respect to prevalence, prognosis, epigenetics, pathophysiology, anatomical and biomechanical factors, clinical presentation, pain and non-surgical treatment recommendations and management. (c) 2021 Osteoarthritis Research Society International. Published by Elsevier Ltd. All rights reserved. Show less
The δ-machine is a statistical learning tool for classification based on dissimilarities or distances between profiles of the observations to profiles of a representation set, which was proposed by... Show moreThe δ-machine is a statistical learning tool for classification based on dissimilarities or distances between profiles of the observations to profiles of a representation set, which was proposed by Yuan et al. (J Claasif 36(3): 442–470, 2019). So far, the δ-machine was restricted to continuous predictor variables only. In this article, we extend the δ-machine to handle continuous, ordinal, nominal, and binary predictor variables. We utilized a tailored dissimilarity function for mixed type variables which was defined by Gower. This measure has properties of a Manhattan distance. We develop, in a similar vein, a Euclidean dissimilarity function for mixed type variables. In simulation studies we compare the performance of the two dissimilarity functions and we compare the predictive performance of the δ-machine to logistic regression models. We generated data according to two population distributions where the type of predictor variables, the distribution of categorical variables, and the number of predictor variables was varied. The performance of the δ-machine using the two dissimilarity functions and different types of representation set was investigated. The simulation studies showed that the adjusted Euclidean dissimilarity function performed better than the adjusted Gower dissimilarity function; that the δ-machine outperformed logistic regression; and that for constructing the representation set, K-medoids clustering achieved fewer active exemplars than the one using K-means clustering while maintaining the accuracy. We also applied the δ-machine to an empirical example, discussed its interpretation in detail, and compared the classification performance with five other classification methods. The results showed that the δ-machine has a good balance between accuracy and interpretability. Show less
Blankenstein, N.E.; Rooij, M. de; Ginkel, J. van; Wilderjans, T.F.; Ruigh, E.L. de; Oldenhof, H.C.; ... ; Jansen, L.M.C. 2021
Background Antisociality across adolescence and young adulthood puts individuals at high risk of developing a variety of problems. Prior research has linked antisociality to autonomic nervous... Show moreBackground Antisociality across adolescence and young adulthood puts individuals at high risk of developing a variety of problems. Prior research has linked antisociality to autonomic nervous system and endocrinological functioning. However, there is large heterogeneity in antisocial behaviors, and these neurobiological measures are rarely studied conjointly, limited to small specific studies with narrow age ranges, and yield mixed findings due to the type of behavior examined. Methods We harmonized data from 1489 participants (9-27 years, 67% male), from six heterogeneous samples. In the resulting dataset, we tested relations between distinct dimensions of antisociality and heart rate, pre-ejection period (PEP), respiratory sinus arrhythmia, respiration rate, skin conductance levels, testosterone, basal cortisol, and the cortisol awakening response (CAR), and test the role of age throughout adolescence and young adulthood. Results Three dimensions of antisociality were uncovered: 'callous-unemotional (CU)/manipulative traits', 'intentional aggression/conduct', and 'reactivity/impulsivity/irritability'. Shorter PEPs and higher testosterone were related to CU/manipulative traits, and a higher CAR is related to both CU/manipulative traits and intentional aggression/conduct. These effects were stable across age. Conclusions Across a heterogeneous sample and consistent across development, the CAR may be a valuable measure to link to CU/manipulative traits and intentional aggression, while sympathetic arousal and testosterone are additionally valuable to understand CU/manipulative traits. Together, these findings deepen our understanding of the fundamental mechanisms underlying different components of antisociality. Finally, we illustrate the potential of using current statistical techniques for combining multiple datasets to draw robust conclusions about biobehavioral associations. Show less
Loon, W. van; Vos, F. de; Fokkema, M.; Szabo, B.; Koini, M.; Schmidt, R.; Rooij, M. de 2021
Objectives: Knee osteoarthritis (OA) is characterized by its heterogeneity, with large differences in clinical characteristics between patients. Therefore, a stratified approach to exercise therapy... Show moreObjectives: Knee osteoarthritis (OA) is characterized by its heterogeneity, with large differences in clinical characteristics between patients. Therefore, a stratified approach to exercise therapy, whereby patients are allocated to homogeneous subgroups and receive a stratified, subgroup-specific intervention, can be expected to optimize current clinical effects. Recently, we developed and pilot tested a model of stratified exercise therapy based on clinically relevant subgroups of knee OA patients that we previously identified. Based on the promising results, it is timely to evaluate the (cost-)effectiveness of stratified exercise therapy compared with usual, "nonstratified" exercise therapy.Methods: A pragmatic cluster randomized controlled trial including economic and process evaluation, comparing stratified exercise therapy with usual care by physical therapists (PTs) in primary care, in a total of 408 patients with clinically diagnosed knee OA. Eligible physical therapy practices are randomized in a 1:2 ratio to provide the experimental (in 204 patients) or control intervention (in 204 patients), respectively. The experimental intervention is a model of stratified exercise therapy consisting of (a) a stratification algorithm that allocates patients to a "high muscle strength subgroup," "low muscle strength subgroup," or "obesity subgroup" and (b) subgroup-specific, protocolized exercise therapy (with an additional dietary intervention from a dietician for the obesity subgroup only). The control intervention will be usual best practice by PTs (i.e., nonstratified exercise therapy). Our primary outcome measures are knee pain severity (Numeric Rating Scale) and physical functioning (Knee Injury and Osteoarthritis Outcome Score subscale daily living). Measurements will be performed at baseline, 3-month (primary endpoint), 6-month (questionnaires only), and 12-month follow-up, with an additional cost questionnaire at 9 months. Intention-to-treat, multilevel, regression analysis comparing stratified versus usual care will be performed.Conclusion: This study will demonstrate whether stratified care provided by primary care PTs is effective and cost-effective compared with usual best practice from PTs. Show less
Rooij, M. de; Leeden, M. van der; Esch, M. van der; Lems, W.F.; Meesters, J.J.L.; Peter, W.F.; ... ; Dekker, J. 2020
Objective The objectives of the present study were to: (1) evaluate the effect of an educational course on competence (knowledge and clinical reasoning) of primary care physical therapists (PTs) in... Show moreObjective The objectives of the present study were to: (1) evaluate the effect of an educational course on competence (knowledge and clinical reasoning) of primary care physical therapists (PTs) in treating patients with knee osteoarthritis (KOA) and comorbidity according to the developed strategy; and (2) identify facilitators and barriers for usage. Method The present research was an observational study with a pretest-posttest design using mixed methods. PTs were offered a postgraduate course consisting of e-learning and two workshops (blended education) on the application of a strategy for exercise prescription in patients with KOA and comorbidity. Competences were measured by questionnaire on knowledge (administered before and 2 weeks after the course), and a patient vignette to measure clinical reasoning (administered before the course and after a 6 month period of treating patients). Facilitators and barriers for using the strategy were assessed by a questionnaire and semi-structured interviews. Results Thirty-four PTs were included. Competence (knowledge and clinical reasoning) improved significantly (p < 0.01). Fourteen out of 34 PTs had actually treated patients with KOA and comorbidity, during a 6-month period. The strategy was found to be feasible in daily practice. The main barriers included the limited number of (self-) referrals of patients, limited number of reimbursed treatment sessions by insurance companies and a suboptimal collaboration with (referring) physicians. Conclusion A blended course on exercise therapy for patients with KOA and comorbidity seems to improve PTs' competence through increasing knowledge and clinical reasoning skills. Identified barriers should be solved before large-scale implementation of exercise therapy can take place in these complex patients. Show less
Vos, F. de; Schouten, T.M.; Koini, M.; Bouts, M.J.R.J.; Feis, R.A.; Lechner, A.; ... ; Rombouts, S.A.R.B. 2020
Anatomical magnetic resonance imaging (MRI), diffusion MRI and resting state functional MRI (rs-fMRI) have been used for Alzheimer's disease (AD) classification. These scans are typically used to... Show moreAnatomical magnetic resonance imaging (MRI), diffusion MRI and resting state functional MRI (rs-fMRI) have been used for Alzheimer's disease (AD) classification. These scans are typically used to build models for discriminating AD patients from control subjects, but it is not clear if these models can also discriminate AD in diverse clinical populations as found in memory clinics.To study this, we trained MRI-based AD classification models on a single centre data set consisting of AD patients (N = 76) and controls (N = 173), and used these models to assign AD scores to subjective memory complainers (N = 67), mild cognitive impairment (MCI) patients (N = 61), and AD patients (N = 61) from a multi-centre memory clinic data set. The anatomical MRI scans were used to calculate grey matter density, subcortical volumes and cortical thickness, the diffusion MRI scans were used to calculate fractional anisotropy, mean, axial and radial diffusivity, and the rs-fMRI scans were used to calculate functional connectivity between resting state networks and amplitude of low frequency fluctuations. Within the multi-centre memory clinic data set we removed scan site differences prior to applying the models.For all models, on average, the AD patients were assigned the highest AD scores, followed by MCI patients, and later followed by SMC subjects. The anatomical MRI models performed best, and the best performing anatomical MRI measure was grey matter density, separating SMC subjects from MCI patients with an AUC of 0.69, MCI patients from AD patients with an AUC of 0.70, and SMC patients from AD patients with an AUC of 0.86. The diffusion MRI models did not generalise well to the memory clinic data, possibly because of large scan site differences. The functional connectivity model separated SMC subjects and MCI patients relatively good (AUC = 0.66). The multimodal MRI model did not improve upon the anatomical MRI model.In conclusion, we showed that the grey matter density model generalises best to memory clinic subjects. When also considering the fact that grey matter density generally performs well in AD classification studies, this feature is probably the best MRI-based feature for AD diagnosis in clinical practice. Show less
Vos, F. de; Schouten, T.M.; Koini, M.; Bouts, M.J.R.J.; Feis, R.A.; Lechner, A.; ... ; Rombouts, S.A.R.B. 2020
Anatomical magnetic resonance imaging (MRI), diffusion MRI and resting state functional MRI (rs-fMRI) have been used for Alzheimer's disease (AD) classification. These scans are typically used to... Show moreAnatomical magnetic resonance imaging (MRI), diffusion MRI and resting state functional MRI (rs-fMRI) have been used for Alzheimer's disease (AD) classification. These scans are typically used to build models for discriminating AD patients from control subjects, but it is not clear if these models can also discriminate AD in diverse clinical populations as found in memory clinics.To study this, we trained MRI-based AD classification models on a single centre data set consisting of AD patients (N = 76) and controls (N = 173), and used these models to assign AD scores to subjective memory complainers (N = 67), mild cognitive impairment (MCI) patients (N = 61), and AD patients (N = 61) from a multi-centre memory clinic data set. The anatomical MRI scans were used to calculate grey matter density, subcortical volumes and cortical thickness, the diffusion MRI scans were used to calculate fractional anisotropy, mean, axial and radial diffusivity, and the rs-fMRI scans were used to calculate functional connectivity between resting state networks and amplitude of low frequency fluctuations. Within the multi-centre memory clinic data set we removed scan site differences prior to applying the models.For all models, on average, the AD patients were assigned the highest AD scores, followed by MCI patients, and later followed by SMC subjects. The anatomical MRI models performed best, and the best performing anatomical MRI measure was grey matter density, separating SMC subjects from MCI patients with an AUC of 0.69, MCI patients from AD patients with an AUC of 0.70, and SMC patients from AD patients with an AUC of 0.86. The diffusion MRI models did not generalise well to the memory clinic data, possibly because of large scan site differences. The functional connectivity model separated SMC subjects and MCI patients relatively good (AUC = 0.66). The multimodal MRI model did not improve upon the anatomical MRI model.In conclusion, we showed that the grey matter density model generalises best to memory clinic subjects. When also considering the fact that grey matter density generally performs well in AD classification studies, this feature is probably the best MRI-based feature for AD diagnosis in clinical practice. Show less
We introduce the δ-machine, a statistical learning tool for classification based on (dis)similarities between profiles of the observations to profiles of a representation set consisting of... Show moreWe introduce the δ-machine, a statistical learning tool for classification based on (dis)similarities between profiles of the observations to profiles of a representation set consisting of prototypes. In this article, we discuss the properties of the δ-machine, propose an automatic decision rule for deciding on the number of clusters for the K-means method on the predictive perspective, and derive variable importance measures and partial dependence plots for the machine. We performed five simulation studies to investigate the properties of the δ-machine. The first three simulation studies were conducted to investigate selection of prototypes, different (dis)similarity functions, and the definition of representation set. Results indicate that we best use the Lasso to select prototypes, that the Euclidean distance is a good dissimilarity function, and that finding a small representation set of prototypes gives sparse but competitive results. The remaining two simulation studies investigated the performance of the δ-machine with imbalanced classes and with unequal covariance matrices for the two classes. The results obtained show that the δ-machine is robust to class imbalances, and that the four (dis)similarity functions had the same performance regardless of the covariance matrices. We also showed the classification performance of the δ-machine compared with three other classification methods on ten real datasets from UCI database, and discuss two empirical examples in detail. Show less
Bouts, M.J.R.J.; Grond, J. van der; Vernooij, M.W.; Koini, M.; Schouten, T.M.; Vos, F. de; ... ; Rombouts, S.A.R.B. 2019
Anatomical, diffusion tensor (DTI), and resting-state functional MRI (rs-fMRI) of 30 patients with early stage AD, 23 with bvFTD, and 35 control subjects were collected and used to calculate... Show moreAnatomical, diffusion tensor (DTI), and resting-state functional MRI (rs-fMRI) of 30 patients with early stage AD, 23 with bvFTD, and 35 control subjects were collected and used to calculate measures of structural and functional tissue status. All measures were used separately or selectively combined as predictors for training an elastic net regression classifier. Each classifier's ability to accurately distinguish dementia-types was quantified by calculating the area under the receiver operating characteristic curves (AUC). Overlapping clinical symptoms often complicate differential diagnosis between patients with Alzheimer's disease (AD) and behavioral variant frontotemporal dementia (bvFTD). Magnetic resonance imaging (MRI) reveals disease specific structural and functional differences that aid in differentiating AD from bvFTD patients. However, the benefit of combining structural and functional connectivity measures to-on a subject-basis-differentiate these dementia-types is not yet known. Combining functional and structural connectivity measures improve dementia-type differentiations and may contribute to more accurate and substantiated differential diagnosis of AD and bvFTD patients. Imaging protocols for differential diagnosis may benefit from also including DTI and rs-fMRI. Highest AUC values for AD and bvFTD discrimination were obtained when mean diffusivity, full correlations between rs-fMRI-derived independent components, and fractional anisotropy (FA) were combined (0.811). Similarly, combining gray matter density (GMD), FA, and rs-fMRI correlations resulted in highest AUC of 0.922 for control and bvFTD classifications. This, however, was not observed for control and AD differentiations. Classifications with GMD (0.940) and a GMD and DTI combination (0.941) resulted in similar AUC values (p = 0.41). METHODS BACKGROUND/OBJECTIVE CONCLUSION RESULTS Show less
Vos, F. de; Koini, M.; Schouten, T.M.; Seiler, S.; Grond, J. van der; Lechner, A.; ... ; Rombouts, S.A.R.B. 2018
Several anatomical MRI markers for Alzheimer's disease (AD) have been identified. Hippocampal volume, cortical thickness, and grey matter density have been used successfully to discriminate AD... Show moreSeveral anatomical MRI markers for Alzheimer's disease (AD) have been identified. Hippocampal volume, cortical thickness, and grey matter density have been used successfully to discriminate AD patients from controls. These anatomical MRI measures have so far mainly been used separately. The full potential of anatomical MRI scans for AD diagnosis might thus not yet have been used optimally. In this study, we therefore combined multiple anatomical MRI measures to improve diagnostic classification of AD. For 21 clinically diagnosed AD patients and 21 cognitively normal controls, we calculated (i) cortical thickness, (ii) cortical area, (iii) cortical curvature, (iv) grey matter density, (v) subcortical volumes, and (vi) hippocampal shape. These six measures were used separately and combined as predictors in an elastic net logistic regression. We made receiver operating curve plots and calculated the area under the curve (AUC) to determine classification performance. AUC values for the single measures ranged from 0.67 (cortical thickness) to 0.94 (grey matter density). The combination of all six measures resulted in an AUC of 0.98. Our results demonstrate that the different anatomical MRI measures contain complementary information. A combination of these measures may therefore improve accuracy of AD diagnosis in clinical practice. Hum Brain Mapp 37:1920-1929, 2016. (c) 2016 Wiley Periodicals, Inc. Show less
