Background. Chronic pain is a common, often disabling condition thought to involve a combination of peripheral and central neurobiological factors. However, the extent and nature of changes in the brain is poorly understood.Methods. We investigated brain network architecture using resting-state fMRI data in chronic back pain patients in the UK and Japan , as well as open data from USA. We applied machine learning and deep learning  methods to explore classification of patients/controls based on network connectivity. We then studied the network topology of the data, and developed a multislice modularity method to look for consensus evidence of modular reorganisation in chronic back pain.Results. Machine learning and deep learning allowed reliable classification of patients in a third, independent open data set with an accuracy of 63%, with 68% in cross validation of all data. We identified robust evidence of network hub disruption in chronic pain, most consistently with respect to clustering coefficient and betweenness centrality. We found a consensus pattern of modular reorganisation involving extensive, bilateral regions of sensorimotor cortex, and characterised primarily by negative reorganisation - a tendency for sensorimotor cortex nodes to be less inclined to form pairwise modular links with other brain nodes. Furthermore, these regions were found to display increased connectivity with the pregenual anterior cingulate cortex, a region known to be involved in endogenous pain control. In contrast, intraparietal sulcus displayed a propensity towards positive modular reorganisation, suggesting that it might have a role in forming modules associated with the chronic pain state.Conclusion. The results provide evidence of consistent and characteristic brain network changes in chronic pain, characterised primarily by extensive reorganisation of the network architecture of the sensorimotor cortex. Maladaptive brain processing of pain is thought to have a primary or facilitative role in many types of chronic pain. In chronic back pain, for example, degenerative musculoskeletal change is considered unlikely in itself to fully explain persistent pain in most patients, and central processes are thought to be critical for the chronification and maintenance of pain. Existing data have identified a broad array of structural and functional brain differences in patients . HoweverOne of the difficulties in identifying robust changes in brain networks underlying chronic pain is that networks are inherently data-rich, and the patterns of disruption may be complex. One way to tackle this is to use machine learning and deep learning methods, and a number of studies have shown how this can be used to successfully build biomarkers (i.e. classifiers) in a range of psychiatric disease  functional connections do not necessarily imply an active role in a disease. A better way of describing and understanding networks is to instead evaluate the underlying topology . Since tWith these issues in mind the aim of the current study was to i) classify, and ii) characterise brain networks in chronic back pain in a multi-site study using resting state fMRI. For classification, we applied machine learning and deep learning classifiers based on data from two sites  as a discovery cohort, and used an open data set  as a validation cohort. For characterisation, we investigated hub disruption across all datasets, and developed a method to identify brain regions that undergo modular reorganisation in the chronic pain state.We recruited adults with chronic musculoskeletal low back pain (CLBP) and approximately age, sex, and IQ-matched adults without CLBP at two sites: Cambridge, UK  and Osaka, Japan . Patients were recruited under the following inclusion criteria: chronic back pain for over 6 months, no other chronic pain condition, no other major neurological or psychiatric disease, and no contraindications to MRI scanning. The study was approved by the East of England NRES Committee, Norfolk, UK (reference 13/EE/0098); and the Ethics Committee for Human and Animal Research of the National Institute of Information and Communications Technology, Japan (reference 20140611). Prior to the participation, all participants gave written informed consent.For all participants, the pain scores were taken in the form of visual analog scale (VAS) and Short-Form McGill Pain Questionnaire. Mood information was collected with Beck Depression Inventory (BDI) and Hamilton Depression Rating Scores. IQ information was collected using the National Adult Reading Test (NART) for the participants in the UK, and the Japanese Adult Reading Test (JART) for the participants in Japan. Demographic information is summarised inOpenPain Project, Department of Physiology, Northwestern University) to provide an additional validation sample for classification, and to add to the data used for network characterisation. For details, see theData availability section below.We used an openly available US data set  equipped with echo planar imaging (EPI) capability and a standard 12-channel phased array head coil either at Addenbrooke’s hospital  or CiNet . Participants remained supine and wore MR-compatible headphones with their heads immobililised with cushioned supports during scanning. Resting-state functional MRI (rsfMRI) was acquired using a single-shot EPI gradient echo T2*-weighted pulse sequence with the following parameters: for the participants in the UK - TR 2000 ms, TE = 30 ms, FA = 78 degrees, BW = 2442 Hz, FOV = 192 × 192 mm (covering the whole brain), acquisition matrix = 64 × 64, 32 axial slices with a interleaved slice order of 3.0mm slice thickness with 0.75mm inter-slice gap, 300 volumes; for the participants in Japan - TR 2500 ms, TE = 30 ms, FA = 80 degrees, BW = 2367 Hz, FOV = 212 × 212 mm (covering the whole brain), acquisition matrix = 64 × 64, 41 axial slices with an ascending slice order of 3.2mm slice thickness with 0.8mm inter-slice gap, 234 volumes. A high-resolution three-dimensional volumetric acquisition of a T1-weighted structural MRI scan was collected using a MPRAGE pulse sequence: for the participants in the UK - TR = 2300 ms, TE = 2.98 ms, time of inversion = 900 ms, FA = 9 degrees, BW = 240 Hz, FOV = 256 × 256 mm, 176 sagittal slices of 1mm slice thickness with no inter-slice gap, acquisition matrix = 256 × 256; for the participants in Japan - TR = 2250 ms, TE = 3.06 ms, time of inversion = 900 ms, FA = 9 degrees, BW = 230 Hz, FOV = 256 × 256 mm, 208 sagittal slices of 1mm slice thickness with no inter-slice gap, acquisition matrix = 256 × 256.High-resolution T1-weighted anatomical imaging and a resting-state functional imaging were performed for each participant, and all those images were preprocessed with SPM8  on Matlab . The first five volumes were discarded to allow for T1 equilibration. Slice timing was adjusted to the intermediate slice and all images were realigned to the first volume of each scan with an estimation based on all the voxels, a fine sampling distance of 2 mm, and a 7th order B-spline interpolation to ensure an accurate head motion correction with the estimated 6 rigid-body head motion parameters. After T1 weighted structural image was co-registered to the mean EPI volume, tissue segmentation of the structural image into three tissue classes; gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF), based on the T1-weighted image contrast was performed in the common Montreal Neurological Institute (MNI) space. The relevant parameters estimated in the tissue segmentation were applied to warp functional images into MNI152 template space with a 2 × 2 × 2 mm spatial resolution. Subsequently, smoothing was applied with a 6 × 6 × 6 mm FWHM Gaussian kernel.3 grid in MNI space); a band-pass filter with a transmission range from 0.008 to 0.1 Hz; regression out of the nuisance regressors from a mask of white matter, cerebrospinal fluid, and the whole brain based on the segmentation of individual T1 weighted image, and three translational and three rotational head motion parameters. To protect against motion artifact in inter-regional correlation, we performed scrubbing  and the Anatomical Automatic Labeling (AAL) package  after the scrubbing showed more than 3 interquartile ranges above the upper quartile or below the lower quartile. Subsequently a 140 × 140 Pearson’s full correlation matrix was computed on all pairs of each of intra-regional average time-series of the ROIs.OpenPain Project” . Anatomical MRI data in the test data set were provided with skull stripping during preprocessing. We chose to exclude six participants (three CLBP patients and three controls) from the US validation test set that had lost a small part of brain coverage in their anatomical image during the skull stripping procedure. Note that the US dataset differed in the exclusion of patients with a BDI score of over 19.A classification model built from UK and Japan data sets was tested on an open data set available from the “W. Each entry ofW corresponds to a particular feature, in this case a connectivity measure between two brain regions, and is interpreted as the contribution of the feature to separating the classes. However, it is important to note that the predictions are based on all features. Linear kernel SVMs have only one hyperparameter,C, controlling the trade-off between the width of the margin separating the two classes and the number of misclassified samples. To assess the predictive performance of the SVM classifier we ran two validation models: i) pooling together the UK and Japan data as the training dataset, and using the US data as an independent validation dataset ; ii) pooling the three datasets together  to increase power and testing the performance of the classifier using a stratified Leave-Two-Subjects-Out (LTSO) cross-validation (CV) . LTSO CV allowed for one subject of each class to be left out for testing, and the remaining subjects from both classes to be used for training in each CV fold. To account for multi-site effects, the pairs that were left out were always from the same acquisition site (stratification).We used a Support Vector Machine (SVM) classifier  based onDue to the slightly higher number of controls compared to patients, we also bootstrapped 100 models in both validation approaches. In other words, each time we ran the whole validation model we randomly selected a balanced sample (as large as possible) with an equal number of patients and controls. The predictive accuracies were averaged across bootstraps.p < 0.05, uncorrected).Feature selection was carried out on the training data using a univariate two-sample t-test : we FishC parameter was optimised using grid search (between 10–3 to 103) and a Leave-3-Out CV on the training data. This CV was nested within the LTSO CV in the second validation model.For both validation approaches the SVMWe used as performance measures the accuracy (percentage of correctly classified samples) and both sensitivity and specificity . The obtained results were tested for statistical significance (i.e. how unlikely the results would be if the classifier was randomly attributing the class labels) using a permutation approach, where we repeat the entire classification procedure  1, 000 times, each time permuting the labels (patient or control) .python 2.7.12 and thescikit-learn 0.17.1 machine learning library . We used a CVAE as a classifier based on log-likelihood. We emphasize that the CVAE is not based on the 140 × 140 Pearson’s full correlation matrices but is based on 140-dimensional vectors, each corresponding to the intra-regional average signal intensities of the 140 ROIs at one time point. Leti-th ROI obtained from a subject at time pointj. Each sample is a 140-dimensional vector:jx =T and all the samples obtained from a subject is represented by the setX = {jx}. The CVAE consists of two neural networks called encoder and decoder. The encoder accepts a samplejx and the conditiony of a subject, and infers a posterior probability of the latent variablejz, which is considered to correspond to a nuisance component unrelated to the disease such as something comes into the subject’s mind at the time pointj. The decoder accepts the latent variablejz and the conditiony, and generates an artificial samplejx̃. After training the encoder and decoder jointly, the CVAE reconstructs given signaljx under the conditiony accurately, and the reconstruction error indicates (an upper bound of) the negative log-likelihood – logp(jx|y) of the given signaljx  of all the samplesX of a subject was equal to the summation of the log-likelihood of each sample, i.e., logp(X|y) = ∑j logp(jx|y).We used a conditional variational autoencoder (CVAE) based on the 140 ROIs to classify subjects  that the subject belongs to the classy is assumed asp(y|X) =p(X|y)p(y)/p(X) ∝p(X|y)p(y) according to Bayes’ theorem. Therefore,p(X|y = 1)p(y = 1) >p(X|y = 0)p(y = 0) indicates that the subject is classified into the classy = 1. Finally, we assumed thatp(y = 1) =p(y = 0) = 0.5 for adjusting the imbalance.Given the samplesn0,n1,n2, andn3. The hidden layers employed ReLU and layer normalization as their activation functions, and the output layers employed identity function. The conditiony was represented by the bias terms of the second hidden layer of the encoder and the first hidden layer of the decoder. The CVAE was trained by Adam optimization algorithm with the parameter α = 10–3, β1 = 0.9, and β2 = 0.999. For each learning iteration, 10 patients and 10 controls were randomly chosen from each site, and 50 samples were randomly chosen from each of the chosen subjects, indicating that a mini-batch comprised 2000 samples. The number of units were searched for within the ranges ofn0 = 140,n1 ∈ {50, 100, 200},n2 ∈ {50, 100, 200}, andn3 ∈ {5, 10} using a Leave-Four-Subjects-Per-Group-Out CV forvalidation model 1 and a 10-fold CV forvalidation model 2, where the 10-fold CV was nested within the LTSO CV. We also usedpython 2.7.12 for this analysis.We used the encoder and the decoder consisting of 4 layers: The number of units were denoted byp(y) of thei-th ROI at the time pointj using the trained CVAE. A large difference between the marginal log-likelihood logp(y) given the different class labelsy = 0 andy = 1 indicates that thei-th ROI largely contributes to the classification. Hence, we defined ℒi =∑j logp(y =c) – logp(y = 1 –c)) given the correct labelc as the contribution weight of thei-th ROI.We can obtain a marginal log-likelihood logTo study the topology of brain networks, we thresholded the Pearson’s full correlation matrices to a produce binary adjacency matrix (consisting of 1’s and 0’s) for each subject. Each of the correlation matrices was thresholded in an adaptive manner to produce an adjacency matrix with a 10 % link density. This value was chosen based on previous studies that have found such a link density to provide optimal discriminative ability , we examEach of the HDIs, defined as a summary of profile of nodal topological metrics in either a patient compared with the cohort of healthy controls or a control out of the cohort of healthy controls compared with the rest of the cohort of healthy controls , were compared between groups by two-sample two-tailed t-tests and the differences were assigned statistical significance at p values less than 0.05. It should be noted that the way HDI is calculated makes it intrinsically susceptible to data noise, which will tend to produce significant values. In addition, because the control group are used to define the normal values against which the patients are compared, we cannot use the individual values for classification, since the values are not indendent (i.e. there is ‘information leak’ between the classification sets).In response to reviewers’ comments, we also considered if the HDI was robust to either removal of the top 5% of nodes, or random removal of 90% of nodes, which it was  key differences between patients and control groups, we next probed the network’s modular structure. Our approach was designed to focus on the differences in brain network modularity between patients and control groups by identifying a measure of the consensus modularity pattern across all of the data. We did this by using a new method based on calculation of the multislice modularity  than pain patients (n = 71), we selected a subset of 70 subjects randomly from each group to match the estimation between each group. Since the modularity estimation is a probabilistic procedure, we repeated this 1000 times, selecting the 70 subjects randomly each time, and computed the average agreement matrix across all repetitions.The agreement matrix was estimated across all three data sets, separately for the pain patients and the control groups. Since there are slightly more control  below an arbitrary threshold ofp < 0.01. However we had prior hypotheses related to the 3 sets of regions commonly implicated in chronic pain: sensorimotor cortices, insular-cingulate cortices, and striatal-medial prefrontal cortex.To statistically evaluate the modularity of each ROI, we performed an approximate permutation test, in which we mixed and randomly resampled the pain and control subjects into two groups, and repeated the full analysis. We did this also 1000 times, and calculated the one-sidedThis was based on the same preprocessing pipeline as above. Voxel-wise maps of connectivity, based on Fisher-transformed correlations of voxel-based BOLD time series, were computed to evaluate regions that were more or less correlated in patients than controls. This was based on a bilateral pgACC seed was a 6mm diameter sphere centered on , based on our recent study identifying this region in endogenous control of persistent pain in healthy subjects . The T1 image of one subject from the UK study site was not available; the analysis is therefore based on the remaining 164 subjects. Anatomical T1 images were segmented into tissue classes using SPM’s new segment function. The resulting grey matter probability maps were normalized to the MNI space using the DARTEL toolbox  perseverhttps://doi.org/10.1101/223446).An earlier version of this article can be found on bioRxiv  and correlation as features for classification, the SVM framework correctly predicted 70%, p-value < 10–3, of patients (sensitivity) and 56%, p-value < 10–3, of controls (specificity), corresponding to a total accuracy of 63%, p-value < 10–3. Usingvalidation model 2  and the same features, the SVM framework correctly predicted 68%, p-value < 10–3, of patients (sensitivity) and 67%, p-value < 10–3 of controls (specificity), corresponding to a total accuracy of 68%, p-value < 10–3. The classification results are summarised inUsingvalidation model 1 . To obtain the depression-related labels we divided the subjects according to their Beck Depression Inventory (BDI) scores: BDI ≥ 3 (depressed), BDI < 3 (not-depressed). This was done to generate a roughly equal division of the data set into two groups, with ‘low’ and ‘high’ BDI scores, accepting the fact that this value has no particular clinical significance. Using a higher value (BDI=10) produced unequal groups and although the classifier did not produce above-chance classification on this basis, this is difficult to interpret given the limited power. Note that there was no correlation between pain VAS and BDI scores.To test whether these results were driven by confounds such as gender  or depression  we tested two new models where instead of the original labels (patient or control) we used ‘male’ and ‘female’ and ‘depressed’ and ‘not-depressed’, respectively. We used exactly the same SVM framework described above and–3), sensitivity = 68% , specificity = 50% . This result was expected given that the depression labels are highly correlated with the pain labels.The accuracy of the gender model was only 48%  with sensitivity = 55%  and specificity = 41% . The accuracy of the depression model, although statistically significant, was lower than with the pain-related labels: accuracy = 59%  correlated with the BDI score for each individual. The correlation was found to be low (0.22) and not statistically significant . This result together with the two confound models are consistent with the hypothesis that the classifier is primarily related to pain.n1 = 100,n2 = 50, andn3 = 10, which achieved the best validation accuracy forvalidation model 1 . The CVAE correctly predicted 55% of patients (sensitivity) and 72% of controls (specificity) on average of 100 trials, corresponding to a total accuracy of 63%. Recall that we built a modelkp(X|y) fork-th trial. We used the likelihoods ∏kkp(X|y) of all the 100 models for an ensemble. Then, ∏kkp(X|y = 1)p(y = 1) > ∏kkp(X|y = 0)p(y = 0) indicates that the subject is classified into the classy = 1. The ensemble achieved a total accuracy of 68%. Contribution weights varied over trials, and were relatively evenly matched across contributing nodes:Deep Learning algorithms represent a novel approach to classification for complex data sets, and have recently been applied to neuroimaging data . Here, wvalidation model 2  and using an LTSO CV), the CVAE correctly predicted 56% of patients (sensitivity) and 71% of controls (specificity) on average of 10 trials, corresponding to a total accuracy of 64%. The ensemble of the 10 trials achieved a total accuracy of 68%.Usingdegree (the number of connections for each node) in chronic pain, with evidence from both in human chronic back pain patients and rodent pain models  matrix , changes were seen across widespread bilateral sensorimotor cortical regions. We also saw significant changes in right inferolateral prefrontal cortex, bilateral temporal cortical regions, and left intraparietal sulcus.To statistically evaluate the values, we performed a permutation test of sum reorganisation estimation, to yield one-sidedThe modular reorganisation analysis allows us to consider separately positive and negative reorganisation values see also. In sensFinally, since recent research has highlighted a potentially important role for the pregenual anterior cingulate cortex (pgACC) in endogenous control of persistent pain, which would provide a mechanistic link to psychological theories of pain which highlight resilience and fear-avoidance. We therefore tested whether pgACC connectivity was different in the patient group . We found pgACC showed enhanced connectivity with regions of sensorimotor cortex, including several areas overlapping those identified in our network modularity reorganization analysis ,Table 8The results show that there are sufficient brain network changes in chronic back pain to allow reliable classification. Furthermore, the way in which networks are changed follows a characteristic pattern, with global disruption of hub connectivity and modular reorganisation. In particular, we show that bilateral sensorimotor cortical regions undergo the substantial reorganisation, including in regions that also carry predictive weight in classification.Since chronic pain dominates many aspects of cognition and action, the existence of widespread connectivity changes is not unexpected . Sensorimotor cortex has been consistently implicated in chronic pain (A particularly interesting finding in our data is that (left) intraparietal cortex displays a pattern of significant network change that is characterised by a relatively large amount of positive modular reorganisation. That is, it appears to enhance its modular links with other brain regions, hinting at a potentially important role in generating the chronic pain state. As an unexpected finding it should be interpreted cautiously, but it could relate to the regions involvement in perceptual-motor coordination and  or multiThe observation that pgACC - sensorimotor cortex is enhanced in chronic pain offers a potential link between networks involved in sensorimotor reorganisation and those involved in motivational and affective processing. Notably, the pgACC is a key node in the pain modulatory network - widely connected to cortical regions associated with pain and reward value and decision-making, and critically connected to the descending control system. It has been proposed to modulate pain based on the amount of prospective learnable information that pain onset or offset carries, based on computational estimates uncertainty  may have a better ability to detection small regions that are important, but greatly increase the numbers of features for classification, which can lead to spurious over-fitting and worse generalisation of the results.In terms of classification methods, the accuracy of the SVM is comparable with that seen in other machine learning-based disease biomarkers that have used independent validation cohorts . Application form and license information can be foundhere. Data is available by emailingdecnef-db-admin@atr.jp.Pre-processed fMRI data from both UK and Japan are available at thehere, and access to data is provided by registeringhere.Raw data are available from the OpenPain Project (OPP), which is where we sourced the validation fMRI data . Licence information is foundhttp://www.mlnl.cs.ucl.ac.uk/pronto/. The code for modularity reorganisation is available at:http://doi.org/10.5281/zenodo.1183399  was co-written by MR and is available at1183399 . Toolbox The present study by Mano et al. examined differences in brain network topology between 41 patients with chronic low back pain and 56 matched healthy controls, recruited at two sites in UK and Japan, using resting-state functional and structural MRI data. They performed an inter-regional correlation analysis over 140 predefined ROIs and classified the resultant connectivity matrices using Support Vector Machines into patient and control groups, and further validated the classifier on an independent dataset from the US (OpenPain Project) as well as on the total datasets. They described hub disruption over clustering coefficient, betweenness centrality, and degree; and highlighted brain modular reorganization at 7 specific areas including the left intraparietal sulcus in patients. They concluded feasibility of graph theory-based machine-learning classification in differentiating chronic pain patients and healthy controls. First, I would praise the authors' tremendous, sophisticated computational efforts in utilizing graph-theory analysis and machine-learning. Their findings on the specific network alterations and reorganized modules in chronic low back pain would potentially be good candidates for further inquiry into their pathophysiological significance, as they also stated in the conclusion remarks. So far they did not examine possible correlations between those graph theory-based and psychophysical parameters. Considering highly mathematical, theoretical nature of graph-theory analysis, it might rather be a good idea to reexamine more physiologically straightforward indices like functional connectivity and gray matter volume at those networks and modules that showed alteration in patients. A further correlation analysis with behavioral parameters would also be a plus. I was impressed by a dominant distribution of modular reorganization over the vast sensorimotor cortical areas. Those areas, as well as the intraparietal sulcus, might well be involved in plastic, functional and anatomical changes in chronic pain patients. As the authors mentioned, it might accord with many earlier neuroimaging studies as well as our recent unpublished data on functional connectivity and gray matter volume in those areas. I would be eager to see further results on possible associations between the current findings and the other imaging/behavioral parameters. Although hub disruption over the whole brain in patients might imply some global network reorganization or dysfunction, it remains unclear what kind of alterations in either pain perception, cognition, or other brain functions might be relevant to such phenomena. Could it be associated with, as it were, "well-being" of brain function as a whole? I would appreciate the authors' further analysis and comments on this significant finding. Overall, I would welcome and approve the present paper as it will add an indispensable piece of knowledge to cerebral pathophysiology of chronic pain.I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. We thank the reviewers for their thoughtful and detailed evaluation. The new manuscript includes a supplement where we summarise the key issues that have been raised together by the 3 reviewers, and detail our response to them and the associated changes to the original manuscript that are in the revised version of the manuscript. We also want to highlight the valuable general commentary provided by the reviewers in relation to the broader aspects of network changes and biomarkers for chronic pain, and believe the open reviews offer a valuable perspective which greatly enhances the publication as a whole. This study investigates case-control differences between individuals with chronic back pain and matched controls across three independent cohorts. The authors take several complementary approaches to characterizing network-level changes in functional connectivity, including Support Vector Machine (SVM) classification of patients vs. controls, classification based on a deep neural network conditional variation auto encoder (CVAE), analysis of ‘hub disruption’, a global measure of functional network reorganization, and a modular reorganization analysis that assesses group-level changes in which regions belong to the same clusters (or ‘modules’) based on functional connectivity. Overall, the analyses are sophisticated and informative. One strength of the manuscript is that they assess the strength and of their results in terms of utility as a biomarker. Many studies show differences between patients and controls - but a key question unanswered by traditional analyses is how large those differences are, and whether they are related to back pain with sufficient effect sizes to allow correct classification at the individual-person level. Mano et al. take a prediction approach, demonstrating 70% sensitivity and 58% specificity for cases (6 months plus of back pain) vs. controls, when applied to a new cohort, and similar accuracy in another type of cross-validation. The CVAE performed similarly. This demonstrates that the results are replicable across studies and have at least limited utility as a biomarker. Embedded in these tests, and others, is another strength of the paper: the demonstration of generalizability. A recent trend in the literature is the sharing and pooling of data across sites. This allows for the aggregation of larger, more diverse samples, which is important for power, for the fidelity of the “picture” of brain changes with chronic pain and other disorders, and for establishing findings that generalize across laboratories and samples. This study is commendable in that it pools together data from 158 participants across three cohorts. In addition, the authors have already shared their data with the community. While multi-lab, multi-site analyses are becoming more common in some kinds of research - notably dementia, autism, and Parkinson’s - they are still very rare in chronic pain research. The MAPP pelvic pain consortium studies are a notable exception. This study demonstrates reproducibility across studies and a practice of data-sharing that will really help move the field forward.  A third strength of the study is the attempt to replicate and extend previous findings by Mansour et al. 2016, who found that chronic pain patients show signs of ‘hub disruption’ - changes in the hub status, broadly related to the number and strength of connections and a region’s role in brain networks - of brain regions across the brain. Mano et al. extend earlier results by testing several different hub metrics across the three datasets, and found the most consistent disruptions in clustering coefficient and betweenness centrality. It would be helpful here to also test how well these metrics work as a classifier, and what their correlation is with the severity and duration of chronic pain. As hub disruption does not provide any information about which brain regions and connections are driving the effects - which patterns because different groups may exhibit ‘hub disruption’ for different reasons, , it would also be helpful to further characterize the particular patterns underlying these global changes. Nonetheless, the convergence with previous findings, and generalization across studies, is promising. So what have we learned about the genesis and nature of chronic back pain? The study offers data on two, complementary perspectives. In the SVM analyses, some of the most important differences between patients and controls are found in fronto-striatal and fronto-hippocampal connections. . This matches other recent studies in humans and rodents suggesting that fronto-hippocampal and fronto-striatal circuits play an important role in chronic pain. It would  be helpful to visualize  these findings in the paper and examine convergence with other analyses. The modular reorganization analyses paints a somewhat different picture: Sensorimotor reorganization, and in particular reduced tendency for sensorimotor regions to couple with other regions. Though the jury is still out, it is tempting to speculate that these changes are similar to those that might occur with evoked, nociceptive pain. However, comparable analyses have not yet been done for evoked pain, so it’s difficult to tell.  What is at stake is whether chronic back pain involves reorganization of systems related to nociception - enhanced nociceptive input, reduced descending inhibition, and/or descending facilitation - or whether the key systems are extra-nociceptive, and the pain is generated and maintained in the brain itself. The present study offers findings that are broadly consistent with both hypotheses. Of course, it’s difficult to say much more, because the relationship of these systems with either evoked pain experience or chronic, ongoing back pain intensity was not studied here - and the resolution of the regions that are the units of analysis in current ‘modularity’ analyses is too coarse to say for sure what the relationship is between regional activity and either nociceptive or descending modulatory systems. Nonetheless, this paper is an important step forward.If we’re going to be able to count on measures as biomarkers for pain-linked systems, they’re going to have to be more accurate. They’ll also have to be linked to pain experience or other aspects of (dys)function, as well, and their specificity to pain (or other processes) will have to be assessed.We need advances in how to deal with correlated variables in a multivariate predictive analysis framework. This is noted as a limitation here and some attempts were made to address selective confounds. But socioeconomic status, social standing, early-life adversity, body-mass/overweight, use of medicines and other drugs, sleep, and other variables are correlated with chronic pain incidence. If we think of these as confounds and try to look at the unique effects of back pain above and beyond all its various correlates and risk factors, we will never get anywhere. Mano et al. show that brain activity was predictive of chronic pain status, but not depression status (depressed vs. control). Other tests might be productively performed, including: Are the predicted chronic pain scores  correlated with gender, depression, head movement, or other variables? And are they correlated with any of these more strongly than they correlate with chronic pain status? Mano et al. do report the [presumptive point-biserial] correlation of the pain classifier output with depression scores (r = 0.22), but do not compare this value with the correlation of the pain classifier output with pain. In the future, it will also be important for the field to develop strategies to effectively control for or minimize the influence of nuisance variables  in the course of SVM (or other) model training, identifying patterns optimized or constrained to be unrelated to them.   Overall, this is a very nice effort, and it moves the needle in terms of advancing research on chronic pain. Other future challenges remain as well, of course, though this does not take away from the value of the present work. I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. We thank the reviewers for their thoughtful and detailed evaluation. The new manuscript includes a supplement where we summarise the key issues that have been raised together by the 3 reviewers, and detail our response to them and the associated changes to the original manuscript that are in the revised version of the manuscript. We also want to highlight the valuable general commentary provided by the reviewers in relation to the broader aspects of network changes and biomarkers for chronic pain, and believe the open reviews offer a valuable perspective which greatly enhances the publication as a whole. This is an elegant study, where the authors use 3 different resting state fMRI data in chronic back pain patients and controls, to test for brain network disruptions, and use such metrics to classify subjects using machine learning algorithms. et al.(2016)1. The same issue also applies to the classification using deep learning algorithm. Using network properties and support vector learning algorithm, they demonstrate that patients and controls can be robustly differentiated from each other. The authors use two alternative classifications, sex and depression, as internal controls to test for outcome specificity. While the methods fails to differentiate sex, it was successful on depression. Here, I have some reservations. The depression classification was based on BDI score with a value = 3 used to define presence or absence of depression. It was not clear to me where this cut-off comes from. Certainly in the clinical literature this cut-off is meaningless, as it is too low. I wonder if this cut-off was increased to some value above 10, what the results would look like. In the same vein, it would be nice to see the actual correlation between pain and BDI, both in patients and healthy controls. Perhaps it would be more judicious, to do the classification after correcting one scale with the other. Otherwise, the results are always a mixture of both. See for example the study by Davis The authors next test hub disruption using multiple network topographic measures. Interestingly, the one based on degree count, fails for the Japan data, although this measure was shown to be robust in our own hands, across humans and rodents, and for multiple chronic pain conditions. The observation is important from the viewpoint of across lab replicability of such measure, and the authors should be congratulated for taking such a step, especially in the field of pain research, where not enough is being done along replication. I want to note that they have already uploaded all of their fRMI data into OpenPain, and we are already using their data in a different research direction. A general point that may be important to address regards the specificity of identified networks both for classification and for modularity analyses. The authors emphasize somatosensory nodes as being the most important in the current analysis. In our earlier hub disruption study, we showed that removing the most significant nodes had no effect on hub disruption, and that we could randomly remove 99% of links and still see the same magnitude of disruption. It would be nice if the authors of the current study test the same idea. For example, in the SVM analysis, does classification fail when you remove the top links? Similarly, in the modularity analysis. Such an analysis has the potential of importantly changing the discussion viewpoint. Overall, although I raise points that can further expand on understanding reported results, this is a well done study with important conclusions, using state of the methods.I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. We thank the reviewers for their thoughtful and detailed evaluation. The new manuscript includes a supplement where we summarise the key issues that have been raised together by the 3 reviewers, and detail our response to them and the associated changes to the original manuscript that are in the revised version of the manuscript. We also want to highlight the valuable general commentary provided by the reviewers in relation to the broader aspects of network changes and biomarkers for chronic pain, and believe the open reviews offer a valuable perspective which greatly enhances the publication as a whole.


We describe the use of ultrasound guidance for hyperosmolar dextrose (prolotherapy) injection of the distal calcaneal tendon specifically just anterior to identified enthesophytes in patients with insertional Achilles calcific tendinosis refractory to conservative treatment. This specific technique has not to our knowledge been described or used in literature previously. Insertional Achilles tendinosis is a common chronic overuse injury in both athletes and nonathletes alike. Symptoms can last anywhere from weeks to years and cause significant difficulties in daily activities. Treatments can range widely from rest, NSAIDs, topical medications, physical therapy, various injections, and in extreme cases surgical intervention.Recently, there has been growing interest in prolotherapy injections for this condition –3. ProloFrom August 2014 to March 2016, patients presenting to the Primary Care Sports Medicine Department complaining of heel pain, found to have insertional Achilles calcific tendinosis, unresponsive to conservative treatment  were treated with an ultrasound-guided prolotherapy technique as described below. Institutional review board approval was obtained for this study and informed consent was obtained from all patients.The patient is placed in the prone position. A towel or pillow is placed under the distal tibia or hung off the side of the table to allow the foot to hang freely in neutral position. The skin overlying the heel should be cleaned and prepared in a sterile manner. We used alcohol based swabs. A nerve block can be performed but most patients do well with a topical spray such as ethyl chloride spray.The soft tissues and tendons of the hind foot (Achilles and Plantaris) along with the posterior surface of the calcaneus should be examined in longitudinal and transverse views. A linear or small footprint high frequency (7–12 MHz) ultrasound probe is used. The probe is held on the dorsal aspect of the Achilles tendon over the insertion in long view of the tendon. 1.0 to 2.0 cm depth is all that is needed to visualize areas of the insertion of the Achilles paying particular attention to capturing the entire proximal to distal length of any enthesophyte. Interventions are performed dynamically, with the aid of real-time grey-scale and color Doppler imaging.A 2 mL mixture of 1 mL of 1% lidocaine and 1 mL of 50% dextrose is placed in a syringe. A 25-gauge 1.5 inch standard needle is guided under real-time ultrasound guidance to the insertion of the calcaneal tendon and specifically underneath/anterior to any identified enthesophyte and in very close proximity to calcification. Injections are generally approached from the medial or lateral side in short axis with preference given to shortest injection distance. When the needle tip is clearly visualized at the desired location, 1-2 mL of the solution is slowly injected depending on size of enthesophytic area with 15–20 fenestrations as area becomes anesthetized. Successful targeting of the desired location is confirmed by spread of anechoic fluid within and around the calcaneal tendon just deep/anterior to the enthesophyte , Table 121 patients with clinically diagnosed insertional Achilles tendinosis were referred to the Sports Medicine Department. Of this group, 10 patients were found to have enthesopathy on ultrasound evaluation. This group included 7 males and 3 females with a mean age of 47 years (range 29–57 years). Achilles pain had been present for a mean of 25 months (range 3–120 months) without significant symptomatic improvement. Ultrasound-guided prolotherapy injection was performed on 5 of the 10 patients.None of the treated patients had previously received any type of injection therapy. Patients primary pain complaint was with push off. Initially these five patients had an average pain level of 5/10 by visual analog scale (VAS) at rest and 8/10 with sport activity such as running. The Victorian Institute of Sport Assessment-Achilles questionnaire (VISA-A), a previously validated questionnaire used for assessing the index of severity of Achilles tendinopathy, was also used as an objective measure to assess level of improvement. The VISA-A consists of eight questions that measure domains of pain, function in daily living, and sporting activity. Results range from 0 to 100, where 100 represents the perfect score [All five patients reported drastic pain relief as well as a return to normal gait and sports activity within 8 weeks of receiving prolotherapy. At that time, the average pain level by VAS at rest was 1/10 with two patients reporting complete pain relief at rest. The average pain level by VAS with sport activity was reported as a 3/10. The average VISA-A score after prolotherapy injections was 84, indicating a sizeable clinical improvement in pain and function. Four patients received an isolated prolotherapy injection. One patient received a second injection 4 weeks after the initial injection. All patients denied recurrence of previous pain levels and symptoms.A 42-year-old healthy male presented to the Sports Medicine Department with complaint of left Achilles pain for 10 years. He complained of 5/10 sharp pain when walking and 10/10 with running. The pain significantly limited various activities of daily function. His VISA-A score at that time was 35. The patient had been doing physical therapy in conjunction with eccentric home exercises 5 days per week for the previous 8 weeks. He had also tried activity modification as well as nonsteroidal anti-inflammatory drugs with minimal relief. Plain films of the heel were unremarkable but MRI revealed a longitudinal partial tear of the central fibers of the distal Achilles tendon with chronic inflammatory changes. Ultrasound evaluation in the clinic showed a prominent enthesophyte along with insertional mixed echogenicity of the Achilles tendon. The patient continued physical therapy after prolotherapy injection and within a few weeks the patient reported drastic improvement in pain levels. After two months, he reported near complete resolution of pain with levels decreased to 1/10 at rest and 3/10 with running. His follow-up postprolotherapy VISA-A score was 82 .A 49-year-old female presented to the Sports Medicine Department with the complaint of right Achilles pain for the past 5 months. The patient was an avid runner and reported running about 50 kilometers per week. She reported 4/10 sharp pain when walking and 7/10 with running. Her VISA-A score at that time was 42. She failed conservative therapy that included activity modification, physical therapy, and eccentric exercises, as well as nonsteroidal anti-inflammatory drugs without relief. Plain films of the heel were unremarkable. Ultrasound evaluation showed a prominent enthesophyte in proximity to the insertion of the Achilles tendon. Ultrasound also revealed heterogeneity throughout the Achilles tendon. Similar to the patient in case presentation #1, the patient continued physical therapy after injection and within a few weeks the patient reported drastic improvement. After a couple of months, she reported near complete resolution of pain, with no pain at rest and 1/10 with running. Her follow-up postprolotherapy VISA-A score was 95 .Prolotherapy is increasingly being used as treatment for a variety of musculoskeletal disorders such as lower back pain , knee inLimitation to procedure efficacy is the fact that sometimes prolotherapy is performed using palpation to guide needle placement. Without visualization, there is higher probability of nonoptimal placement of injectable fluid. Ultrasound guidance will not only aid in precise needle placement but also identify which patients are better candidates for injection as demonstrated by visual evidence of calcification.Insertional Achilles tendinopathy will often show sonographic evidence of hypoechoicity, intratendinous tears, and increased tendon size. Some subsets of these patients will show calcification at the insertion and/or evidence of enthesopathy. The patients we have treated for this condition fall into this subset category and expand on positive outcomes reported previously . The difOne limitation to our study given the small sample size is the question of how often prolotherapy injections are needed. Preliminarily, it seems plausible that one injection alone may be adequate given four of our patients only needed one injection. It is however conceivable that some patients with severe refractory cases maybe need additional injections. An additional limitation of our study is that follow-up ultrasound evaluation after clinical improvement was not performed. It would be interesting to see if there was any improvement or regression of calcified areas given the reported improvement in symptoms. It is a possible consideration that the clinical improvement observed is confounded by c-existing pathology of the Achilles tendon as opposed to enthesopathy alone. Previous studies have shown improved sonographic appearance of the tendon after prolotherapy to include neovascularization as well as reduction in tendon size and severity of hypoechoic regions. Regardless of exact etiology of insertional Achilles tendon pain, patients clinically improve with administration of prolotherapy, but more extensive studies and clinical trials are warranted to potentially isolate calcific causes and evaluate the efficacy of our technical approach aimed at injections specifically underneath the enthesopathy.The numbers are small but the reported outcomes in these patients are encouraging. Ultrasound-guided prolotherapy injection seems to be most beneficial where conservative therapy has failed and there is evidence of enthesopathy or calcific findings and the technique is done as specified. Ultrasound guidance should be the technique of choice for prolotherapy injection for this condition as it provides the highest likelihood that the injectable fluid is placed in deep to the enthesophyte or calcified area.


Unrelieved postoperative pain after total knee arthroplasty (TKA) is a significant problem. This longitudinal study investigated how preoperative pain intensity, as well as a comprehensive list of preoperative and perioperative factors, influenced the severity of acute average and worst pain after TKA.Prior to surgery, 203 patients completed a demographic questionnaire, Lee Fatigue Scale, Fatigue Severity Scale, Hospital Anxiety and Depression Scale, and Brief Illness Perception Questionnaire. Brief Pain Inventory was completed prior to surgery as well as through postoperative days (POD) 0 to 4. Clinical data were extracted from medical records.Several factors were associated with higher levels of preoperative and postoperative pain. Lower preoperative average and worst pain intensity scores were associated with increases in average and worst postoperative pain from POD1 to POD4. A higher number of comorbidities, higher C-reactive protein values, and higher pain interference with function were associated with higher preoperative levels of average pain. Older age, higher fatigue levels, and higher scores on identity and emotional responses to osteoarthritis (OA) were associated with higher preoperative levels of worst pain. Lower perceived consequences of OA were associated with higher pain from POD1 to POD4. Males and patients with lower preoperative scores for average pain had higher worst pain following surgery.Patients at higher risk for more severe postoperative pain can be identified through an assessment of pain and other risk factors identified in this study. Future research needs to test the efficacy of interventions that modify patients’ perceptions of living with OA and pain intensity before surgery on short and long term postoperative outcomes. Approximately 12% of adults over 60 years of age suffer from symptomatic osteoarthritis (OA) of the knee . More thFindings from a systematic review of postoperative pain management  noted thHigher preoperative pain , 14 and Therefore, to reduce the negative consequences of severe postoperative pain, the purposes of this longitudinal study were to describe the trajectories of average and worst pain intensity using a 0 to 10 numeric rating scale (NRS) from the day of admission  until POD 4 and to evaluate how preoperative pain intensity in conjunction with demographic, clinical, symptom, and psychological characteristics, impact inter-individual differences in initial levels of as well as in the trajectories of average and worst postoperative pain . The preoperative pain scores were allowed to be part of the pain trajectories in this study, because we assumed that patients’ preoperative pain levels would impact the course of their acute postoperative pain.This study is part of a longitudinal study of pain, symptoms, and health-related quality of life (QOL) in patients who underwent a TKA for OA at a surgical clinic in Oslo, Norway. Patients (n = 203) were included if they were >18 years of age; were able to read, write, and understand Norwegian; and were scheduled for unilateral primary TKA. Patients were excluded if they underwent unicompartmental or revision surgery.Patients received written information about the study either by mail prior to admission or on the day of admission. Patients were admitted to the hospital between 1 to 3 days prior to surgery, usually on the day before surgery. Patients who met the inclusion criteria were invited to participate by a nurse on the day of admission. After obtaining written informed consent, patients completed a questionnaire that assessed demographic characteristics, preoperative pain, preoperative symptoms, and psychological factors. Preoperative clinical tests, comorbidities, body mass index (BMI), and information on medications were obtained from medical records. The completed questionnaires were collected by the nursing staff in sealed envelopes. The study was approved by the Regional Medical Research Ethics Committee of Health South East of Norway (#2011/1755).The presence and intensity of average and worst preoperative pain was assessed on the day of admission using 0 (no pain) to 10 (worst imaginable pain) NRS. Acute postoperative pain was assessed from the day of surgery (DOS) until POD 4. Patients rated their average and worst pain every evening  using a 0 (no pain) to 10 (worst imaginable pain) NRS. These data were returned to the nursing staff in sealed envelopes.The anesthesia, surgery, and postoperative pain management procedures were standardized. All patients received the same posterior cruciate-retaining fixed modular-bearing implant for the TKA. A tourniquet was used during surgery and drains were placed and removed on POD1. Neuraxial block with bupivacaine and sedation were the first choice for anesthesia. Epidural analgesia (EDA), with continuous infusion of bupivacaine 1 milligram/milliliter (mg/ml), adrenaline 2 micrograms (μg)/ml, and fentanyl 2 μg/ml (5 to 12 ml/hour), was used for postoperative pain management. If neuraxial blockade was contraindicated, patients received total intravenous anesthesia and a continuous femoral nerve block (CFNB) with bupivacaine 2.5 mg/ml 4 to 10 ml/hour for postoperative pain management. In most cases, the regional blocks were removed on POD2. Oral acetaminophen 1 gram was given every 6 hours and celecoxib 200 mg and controlled release oxycodone 5 to 20 mg was given every 12 hours unless contraindicated. Immediate release oxycodone 5 mg tablets or intravenous ketobemidone 2.5 to 5 mg were available as rescue medications. If pain control was not satisfactory, low dose ketamine 1.5 μg/kilogram/minute was administered as a short-term intravenous infusion .Mobilization and physical therapy were standardized. All patients were mobilized out of bed and allowed full weight bearing on the operated knee on POD1. Patients received physical therapy on a daily basis with walking, flexion, and extension of the knee beginning on POD1. Most patients were discharged on POD 4.Data on type of implant, American Society of Anesthesiologists (ASA) physical status classification , length Data on preoperative use of pain medications and sleep medications, anesthesia regimen and doses of postoperative pain medications were obtained from patients’ medical records. For preoperative pain medications and sleep medications, whether a patient used a drug or not , was recorded. According to hospital routines, all except one patient who used a nonsteroidal anti-inflammatory drug discontinued its use 7 to 14 days before admission. For postoperative drug use, all opioid analgesics were converted to intravenous (IV) morphine equivalents using the European Association for Palliative Care recommendations for opioid conversion . AverageThe Brief Pain Inventory (BPI): Patients’ level of preoperative pain and its impact on function were assessed using the BPI [ the BPI . The BPI the BPI . Individ the BPI .Fatigue severity: The 5-item Lee Fatigue Scale (LFS) was used to evaluate preoperative fatigue severity. Each item was rated on a 0 to 10 NRS. A total score was calculated as the mean of the 5 items with higher scores indicating higher fatigue severity. The LFS has satisfactory validity and reliability [iability , 29. In Fatigue Interference: The 7-item Fatigue Severity Scale (FSS-7) was used to evaluate fatigue interference during the past week prior to surgery. Patients rated their agreement with 7 statements, using a 7-point Likert scale that ranged from disagree to agree. A total score can range from 1 to 7 with higher scores indicating higher levels of interference. The Norwegian version of the FSS-7 has good psychometric properties [operties . In thisFatigue severity and interference were included as potential predictors in the analysis because fatigue and pain occurred together in a symptom cluster in knee and hip osteoarthritis , 31. TheThe Hospital Anxiety and Depression Scale (HADS) [e (HADS)  was usede (HADS) . In thisThe Brief Illness Perception Questionnaire (BIPQ): The BIPQ [The BIPQ  was usedThe BIPQ . For con2) of 6.8% from a model with seven predictors. A R2 of 2% is considered a small effect. A R2 of 13% is considered a medium effect. A R2 of 26% is considered a large effect [Descriptive statistics and frequency distributions were performed on the preoperative demographic, clinical, symptom, and psychological characteristics of the sample using SPSS version 22 . The distributions of the pain intensity scores were consistently symmetrical, used the full range of values from 0 to 10, and were not generally skewed either to the right or the left at any of the six measurement times. In addition, our sample size was large enough to provide confidence that the assumptions of the statistical models were not violated. Power analyses specific for all of the different elements of the HLM analyses were not performed. A sample of 203 patients provides power of at least 80%, at an alpha of .05, to detect as significant a correlation as small as r = .19. From a multiple regression point of view, a sample of 203 patients provides power of at least 80%, at an alpha of .05, to detect as significant an overall percent of explained variance  as a function of proposed predictors. t value of <2.0, which indicates a lack of significant effect, were excluded from further model testing. All potential significant predictors from the exploratory analysis were entered into the model to predict each individual change parameter. Only predictors that maintained a statistically significant contribution in conjunction with other variables were retained in the final model. A p-value of < .05 indicated statistical significance. The unique contribution of each significant predictor on the pain trajectories for patients’ with higher versus lower scores on the predictor variable, while controlling for all other variables in the model are shown in figures to provide the reader with the clinical meaning of each predictor variable. Effect sizes (ES) were calculated on the differences in pain scores for patients with high versus low scores on a predictor variable, according to Cohen’s coefficient d . A d -value ≥0.40 was considered a clinically meaningful difference between groups [To improve estimation efficiency and construct a parsimonious model, exploratory level 2 analyses were performed. Each potential predictor was assessed to determine whether it would result in a better fitting model if it alone was added as a level 2-predictor. Predictors with a n groups .Of the 245 patients invited to participate, 6 had their surgery canceled and 33 declined participation. A total of 206 patients (84%) were enrolled in the study. Two patients were excluded after surgery because of postoperative disorientation and one patient died from postoperative complications. Of the 206 who were enrolled, 203 (98.5%) patients are included in this analysis.The first HLM analysis examined how pain intensity scores changed within PW1  and within PW2 . For both average and worst pain, the goodness of fit tests for the deviance among the models indicated that a linear fit for PW1 and a cubic fit for PW2 was the best. The estimates for the initial piecewise models are presented in 2) + ß4(PW23). The different combination of values of PW1 and PW2 for each of the six assessment days can be put into the equation yielding a different predicted value for each day. Utilizing the regression coefficients found in 2) + 0.130(PW23). Predicted Worst Pain = 5.512 + -1.279(PW1) + 1.975(PW2) + -0.852(PW22) + 0.101(PW23).The combination of each of the coefficients in the unconditional models determines the slope of the curves for each of the piecewise components’ changes in average and worst pain over time. The unconditional estimated scores for changes in average and worst pain over time are based on the predicted values from the HLM analysis and are illustrated in With no covariates in the model, average pain decreased significantly from prior to surgery to the DOS, increased on POD1 and remained relatively stable through POD2 and POD3, and then increased slightly on POD4. The average postoperative pain intensity scores never exceeded the level of average pain prior to surgery. With no covariates in the model, worst pain decreased from prior to surgery to the DOS; increased on POD1 reaching a peak on POD2 that slightly exceeded preoperative scores, followed by a slight decrease on POD4.As displayed in While average dose of opioids and average preoperative pain intensity only made significant contributions to parts of the change in average pain intensity over time, they improved the overall model fit, and findings from previous studies suggest that they are important predictors of postoperative pain , 14, 48.As displayed in This study is the first to examine the effects of a comprehensive list of demographic, clinical, symptom, and psychological predictors of changes in average and worst postoperative pain over the first four days following TKA. Our analytic method, complementary to previous studies using linear or logistic regression models, allowed us to evaluate more complex pain trajectories during the first four postoperative days as well as the relationships between different predictors throughout the immediate postoperative period.For both average and worst pain over time, a distinct increase in pain intensity occurred on POD2, which coincides with the removal of nerve blocks as well as increases in activity levels. For average pain, all of the postoperative pain scores were lower than the preoperative pain scores, with the highest postoperative score occurring on POD4. This finding may be the result of the intensive postoperative pain management regimen that these patients received. However, for worst pain, the trend was somewhat different. Worst pain scores slightly exceeded the preoperative pain ratings on POD2 and gradually declined until POD4. These findings suggest that the measurement of worst pain might capture different aspects of the pain experience such as elements of dynamic or movement evoked pain. The relatively similar levels of average and worst pain prior to surgery may be attributed to reduced activity levels as a consequence of pain and disability associated with OA of the knee .Several characteristics were identified that may assist clinicians to identify patients at higher risk for increased pain during hospitalization. Lower average and worst preoperative pain intensity scores were associated with slight increases in average and worst postoperative pain intensity scores from POD1 to POD3 Figs  and 4C. d = 0.8). In addion, patients with higher levels of preoperative CRP had higher average preoperative pain. While the effect size for CRP was small (Cohen’s d = 0.2), it is interesting to note that the variance in serum levels was relatively small (range 1 to 20 mg/l), which suggests that even small variations in CRP are associated with higher levels of preoperative pain. In a recent systematic review that evaluated for associations between serum levels of CRP and pain, function, and radiographic changes in OA patients compared to healthy controls [Not surprisingly, patients with higher preoperative pain interference with function had higher average pain scores prior to surgery , as well as patients attributing more symptoms to their OA (Cohen’s d = 0.4) reported higher worst preoperative pain scores (d = 0.5). Since lower perceived consequences of OA were associated with lower average pain scores  and lower pain interference with function  prior to surgery, these patients may have been surprised by the severity of the pain and associated disability following TKA. Illness perceptions are defined as beliefs [Patients with a higher emotional response to OA . However, worst pain scores for women and men followed a similar trend from POD1 to POD4 , clinicians need to take this risk factor into consideration when they individualize a patient’s pain management plan.A higher number of comorbidities was associated with higher average preoperative pain intensity. The associations between higher levels of comorbidity and increased pain and poorer function are well known , 57. WhiSome study limitations warrants consideration. The majority of the sample was female, which is reflective of the OA population . PatientThe study has several strengths. First, this study evaluated a comprehensive list of predictors using novel statistical methods. Secondly, the sample size is relatively large with a minimal amount of missing data. Third, only patients who underwent TKA for OA and received the same implant were included. Fourth, patients with a wide age range were included, which increases the generalizability of our findings. Older patients tend to be excluded from these types of studies .In conclusion, we found that patients with lower average and worst preoperative pain scores had higher postoperative pain over time. Possible modifiable predictors such as perception of OA illness and pre-operative pain intensity were identified. Clinicians may use these factors to identify patients at higher risk for more severe postoperative pain.Future research should focus on the development of a screening tool to identify patients at higher risk and an evaluation of the effects of interventions that modify these risk factors on short and long term pain interference, function and quality of life.S1 Table(DOCX)Click here for additional data file.


N-methyl-D-aspartate and glutamate receptor antagonist, provides analgesic effects accompanied by preservation of protective airway reflexes. In this context, we will carry out a randomized controlled, open-label, multicenter trial to compare a subdissociative dose of ketamine to morphine to provide pain relief in the prehospital setting, in patients with traumatic and non-traumatic pain.Acute pain is a common condition among prehospital patients and prompt management is pivotal. Opioids are the most frequently analgesics used in the prehospital setting. However, opioids are highly addictive, and some patients may develop opioid dependence, even when they are exposed to brief opioid treatments. Therefore, alternative non-opioid analgesia should be developed to manage pain in the prehospital setting. Used at subdissociative doses, ketamine, a noncompetitive This will be a multicenter, single-blind, randomized controlled trial. Consecutive adults will be enrolled in the prehospital setting if they experience moderate to severe, acute, non-traumatic and traumatic pain, defined as a numeric rating scale score greater or equal to 5. Patients will be randomized to receive ketamine or morphine by intravenous push. The primary outcome will be the between-group difference in mean change in numeric rating scale pain scores measured from the time before administration of the study medication to 30 min later.This upcoming randomized clinical trial was design to assess the efficacy and safety of ketamine, an alternative non-opiate analgesia, to manage non-traumatic and traumatic pain in the prehospital setting. We aim to provide evidence to change prescribing practices to reduce exposition to opioids and the subsequent risk of addiction.NCT03236805. Registered on 2 August 2017.ClinicalTrials.gov, ID: The online version of this article (10.1186/s13063-018-2634-3) contains supplementary material, which is available to authorized users. Pain is a common condition among prehospital patients . In AustOpioids are the most frequently prescribed analgesics in the prehospital setting , 4. HoweN-methyl-D-aspartate and glutamate receptor antagonist that decreases central sensitization, “wind-up” phenomena, and pain memory [p = 0.03). Tran et al. performed a cluster randomized trial to compare the analgesic effects of ketamine and morphine in trauma patients, in a prehospital low-resource setting [Therefore, alternative non-opioid analgesia strategy, using agents at lower risk of dependence, should be proposed to manage pain in the prehospital setting . Ketaminn memory –14. Ketan memory . Used atn memory , 17. Reln memory . They re setting . A totalTo do methodological limitations of the previous studies, well-designed multicenter clinical studies to further examine the potential applicability and benefits of subdissociative-dose ketamine in the prehospital setting in trauma and non-trauma patients are needed. In this context, we will carry out a randomized, controlled, open-label multicenter trial to compare a subdissociative-dose ketamine alone to morphine alone to provide pain relief in the prehospital setting in patients with traumatic and non-traumatic pain. Here, we hypothesize that ketamine 20 mg, titrated during a 30-min period with an objective of verbal rating scale pain score of 3 or less, will provide non-inferior analgesia to morphine 3 mg, titrated during the same period, in a group of patients suffering moderate to severe pain in the prehospital setting.This will be a multicenter, single-blind, randomized controlled trial to compare low-dose ketamine to morphine for analgesia in trauma and non-trauma patients in the prehospital setting. We perform a single-blind trial as side effects associated with ketamine can easily be observed . Therefore, blinding may not be complete as it might be possible to determine arm during administration. Moreover, primary outcome will be assessed by the patient using the verbal rating scale, without any possible intervention of the physician in charge of the patient. The trial, named KETAMORPH, has been designed on the basis of the Consolidated Standards of Reporting Trials CONSORT 2010) guidelines [10 guidelThe study was supported by a grant from the French Ministry of Health (PHRC API16/N/059), sponsored by the Nantes University Hospital, and monitored by the Clinical Research Unit Grand Ouest. The study protocol and patient informed consent procedures were approved and received Sud-Méditerranée 2 Institutional Review Board approval .Consecutive adults (18 years or older) will be enrolled in the prehospital setting by emergency medicine services, if they experience moderate to severe, acute, non traumatic and traumatic pain, defined as a numeric rating scale score greater or equal to 5, on a standard 11-point  numeric rating scale. The emergency medical services are ambulance base stations equipped with one or more mobile intensive care units, consisting of an ambulance driver, a nurse, and a senior emergency physician as the minimum team . ExclusiInformed consent may be waived at randomization, because patients will need urgent pain management and because acute pain impairs the ability to provide informed consent. Whenever a patient will be included without written informed consent, such consent will be promptly sought, according to the French Law of Ethics, subsequently from the patient when the pain has decreased . TherefoPatients will be randomly assigned in a 1:1 ratio, using a computer-generated list to ketamine or morphine in the two groups of patients . Development of the randomization list, confirmation of written consent acquisition for all participants, and statistical analyses were conducted by the research manager and statistician, who were independent of any data collection. The randomization list was generated before commencement of the study. We used computer-generated random numbers to generate the allocation sequence, without blocking. The allocation sequence was then implemented in sealed envelopes, opened by the physician in charge of the patient.2) below 94% during the procedure, oxygen will be administered with nasal cannulae-delivering flow rate of 2 L/min, and will be adapted based on SpO2 follow-up.Morphine will be administered by intravenous push, 2 mg (patient weight < 60 kg) or 3 mg (patient weight ≥ 60 kg) every 5 min, as recommended by the French guideline on acute pain management . KetaminThe primary objective of the trial will to show that low-dose ketamine alone is not inferior to morphine alone at 30 min, in prehospital patients who experience moderate to severe, acute, traumatic or non-traumatic pain, defined as a numeric rating scale score greater or equal to 5. The primary outcome will be the between-group difference in mean change in verbal rating scale pain scores among patients receiving ketamine or morphine, measured from the time before administration of the study medication to 30 min later.Secondary endpoints will be: (1) between-group difference in mean change in numeric rating scale pain scores among patients receiving ketamine or morphine, measured from the time before administration of the study medication to 15, 45, 60 min later, and at ED admission, (2) the incidence of rescue analgesia at 30, 45, and 60 min, and at ED admission, (3) the change in vital signs at 15, 45, 60 min and at ED admission, (4) the incidence of adverse events at 15, 45, 60 min and at ED admission, (5) the need to withdraw morphine or ketamine and the use of specific drugs to antagonize severe adverse events at 15, 45, 60 min and at ED admission, (6) weight based dose of study drug (mg/kg dosing) received during the 30-min period, and (7) number of doses of study drug received during the 30-min period. We will actively seek adverse events associated with morphine or ketamine use, including: oxygen desaturation and respiratory depression, hypotension, bradycardia, oversedation, dizziness, disorientation, mood change, nausea and vomiting, as previously reported . Follow-Prior to the trial initiation, study personnel will undergo training sessions on data collection and will be individually tested on data entry as well as outcome assessments. Study data will be collected and managed using Ennov clinical electronic data capture tools hosted at Nantes University Hospital. Ennov clinical is a secure, web-based application designed to support data capture for research studies, providing: (1) an intuitive interface for validated data entry; (2) audit trails for tracking data manipulation and export procedures; (3) automated export procedures for seamless data downloads to common statistical packages; and (4) procedures for importing data from external sources.To assess non-inferiority in the two subgroups of patients , with a non-inferiority margin of 1.3, standard deviation (SD) of 3, α = 5%/2, β = 10%, 448 patients are needed . These parameters are based on estimates of variability from previous works from Chang et al. –26. The No interim analysis is planned.Continuous variables will be summarized using descriptive statistics, i.e. number of subjects, mean, median, SD, interquartile range, and minimum and maximum. Qualitative variables will be summarized by frequency and percentage. Since this is a non-inferiority study, analysis of the primary outcome will be performed on a per-protocol population. Secondary analysis will be performed based on the intention-to-treat (ITT) principle. We will perform chi-square or Fisher’s exact tests as appropriate for qualitative variables, and the Mann-Whitney tests will be used for continuous variables. All statistical tests will be two-sided. The chosen type-I error rate will be α = 0.05. Analyses will be done using Stata software .The funding source will have no role in the study design, data collection, data analysis, data interpretation or writing of the report. All authors agreed to submit for publication.This upcoming randomized clinical trial was design to assess the efficacy of ketamine, an alternative non-opioid analgesic to manage non-traumatic and traumatic pain in the prehospital setting. Pain is a common condition among prehospital patients . OpioidsKetamine has the potential to decrease opioid use in the prehospital setting . HoweverThe KETAMORPH trial should provide high-quality evidence to settle this issue in providing guidance on the use of ketamine in the prehospital setting to manage moderate to severe acute non-traumatic and traumatic pain. If it confirms the efficacy and the safety of the low-dose ketamine for analgesia in the prehospital setting, emergency physicians will have a lever to reduce opioid use and its addictive potential.Recruiting. Recruitment began in November 2017 and is expected to conclude in May 2019. Target enrollment for the study is 498 participants. The trial is registered at ClinicalTrials.gov.Additional file 1:Centers involved in the study. (DOCX 844 kb)Additional file 2:SPIRIT Checklist. (DOC 121 kb)Additional file 3:Consent form. (DOCX 127 kb)