The placebo response is universally observed in clinical trials of pain treatments, yet the individual characteristics rendering a patient a ‘placebo responder’ remain unclear. Here, in chronic back pain patients, we demonstrate using MRI and fMRI that the response to placebo ‘analgesic’ pills depends on brain structure and function. Subcortical limbic volume asymmetry, sensorimotor cortical thickness, and functional coupling of prefrontal regions, anterior cingulate, and periaqueductal gray were predictive of response. These neural traits were present before exposure to the pill and most remained stable across treatment and washout periods. Further, psychological traits, including interoceptive awareness and openness, were also predictive of the magnitude of response. These results shed light on psychological, neuroanatomical, and neurophysiological principles determining placebo response in RCTs in chronic pain patients, and they suggest that the long-term beneficial effects of placebo, as observed in clinical settings, are partially predictable. People vary in the extent to which they feel better after taking an inert, placebo, treatment, but the basis for individual placebo response is unclear. Here, the authors show how brain structural and functional variables, as well as personality traits, predict placebo response in those with chronic back pain. Placebo analgesia is especially relevant in the management of chronic pain, since most pharmacological treatments have long-term adverse effects or addictive properties2, or show only modest improvements that are insufficient to achieve clinically meaningful amelioration of disability3. Placebo responses are observed universally in almost all randomized placebo-controlled clinical trials (RCT), including those testing chronic pain treatments6. Importantly, the effect size of placebo response is often equivalent to the active treatment studied7 and often even greater than that seen in conventional therapy6. The duration of placebo responses has been shown to be comparable to those achieved with active treatments and the magnitude of placebo responses appear to be increasing in recent RCTs of neuropathic pain conducted in the US7. This implies that the placebo effect will remain a confounding nuisance in clinical testing and practice as long as its underlying properties remain unknown, and as long as approaches that predict this response or reliably harness it for medical benefit are not developed.The placebo response refers to an improvement in symptoms caused by receiving an inert treatment. The phenomenon has been observed across different conditions, biological systems, and treatment types11. In healthy subjects, placebo response and its neural and psychological correlates lack consistency across different routes of administration14. Moreover, the translation of such findings to clinical settings is questionable not only because chronic pain patients exhibit distinct brain anatomy and neurophysiology18 but also because such patients are repeatedly exposed to a myriad of medical rituals which may bias expectations toward treatment. Thus, it is likely that the principles of placebo pill analgesia in clinical settings may not be captured by experimental models of placebo.The neurobiological mechanisms underlying the placebo effect have been primarily studied for acute responses to conditioning-type manipulations or suggestion-based paradigms in healthy individuals, usually performed in the laboratory setting20. Others have shown that brain functions may actually predispose chronic pain patients to respond to a placebo treatment. RCTs comparing a lidocaine patch to placebo treatment in chronic low back pain and duloxetine to placebo treatment in osteoarthritis indicated that placebo response was predicted, respectively, by functional connectivity of the medial prefrontal with the insula21 and the dorsolateral prefrontal cortex with the rest of the brain22. Unfortunately, these studies have the caveat of not relying on a no treatment arm to control for the natural history of the patients , and thus do not account for inherent variability in measurements of brain activity when comparing baseline to post-treatment periods. One study has addressed these confounds by comparing active placebo to an inactive control group in major depressive disorder, showing that increased placebo-induced μ-opioid neurotransmission in limbic regions was predictive of greater improvement of symptoms in response to subsequent antidepressant treatment23. Similarly controlled studies in chronic pain patients are necessary to investigate placebo responses in clinical settings.A number of brain imaging studies have examined placebo response in the setting of a RCT. Some of these studies have demonstrated changes in brain functions as a consequence of placebo effects24.This prospective cohort study included four neuroimaging sessions, a large battery of questionnaires assessing personality traits and pain characteristics, and a proper no treatment arm that allowed us to disentangle placebo pill-related analgesia from non-specific effects. Given that the RCT placebo effect is embedded in predictable psychology and neurobiology and given that metabolic activity and functional connectivity change following placebo response, we designed a comprehensive RCT with two identical treatment periods (placebo or active treatment), each followed by a washout period. This allowed us to investigate the duration of response and potential differences in within-subject occurrence/re-occurrence of placebo response. The repeat brain imaging sessions were used: (1) to identify functional networks  predictive of placebo response prior to placebo exposure; and (2) to test stability of identified networks post-exposure to placebo, as such networks are known to be malleable and may change as a function of learned associations27 and clinical placebo response23. We also hypothesized that other psychological and neurophysiological determinants, which would be more specific to the clinical setting and to chronic pain patients will also contribute to the prediction of placebo response. The second aim of this study was to examine the longitudinal effect of treatment exposures on brain properties. Based on prior studies20, we hypothesized that some components of the functional networks predisposing patients to placebo response would change as a consequence of placebo response while others would remain stable.The primary aim of this study was to identify the psychological factors and the brain properties collected prior to placebo treatment that would determine placebo response. Given previous results, we hypothesized that responses to placebo pills are predetermined by functional connectivity between regions such as the medial and lateral prefrontal cortex, the anterior cingulate cortex, and the subcortical limbic structures, since they have been implicated in both experimentalConsistent with our predictions, we demonstrate-specific psychological factors, anatomical properties, and functional coupling of the lateral prefrontal cortex predetermine placebo pill responses. We show that components of the response-predictive functional network showed transient properties, dependent on the placebo response. Finally, a fully cross-validated algorithm applied on psychological factors and functional connectivity prior to exposure to placebo treatment successfully predicted response magnitude to placebo pill treatment.28), the McGill Pain Questionnaire (MPQ) sensory and affective scales, and the PainDetect. The covariance matrix across the different pain measurements at baseline is presented in Supplementary Figure This neuroimaging-based placebo RCT was conducted in 129 CBP patients assessed for eligibility, 63 of whom were analyzed  with the 20 patients comprising the no treatment arm (NoTx); for this, we used the EMAs collected with the phone app. On average, PTx showed stronger diminution in pain intensity compared to NoTx  . The response rate indicated that placebo pill ingestion increased the frequency of CBP patients showing pain reduction—more patients were classified as responders in the placebo treatment group (PTxResp 24/43: response rate of 55%) compared to the no treatment group . This demonstrates that placebo pill ingestion increased the response rate when considering within-subject pain entries.Next, the 63 patients were dichotomized into Resp and NonR based on a permutation test performed on the EMAs collected with the phone app (baseline vs. treatments 1 or 2) to determine within-subject improvement of symptoms . After completing all the analyses, the real labeling was revealed during a public lab meeting. The results are reported only when the real labeling of patients could be properly identified based on the statistical tests.First, we sought to identify psychological parameters predisposing CBP patients to the placebo pill response from a battery of 15 questionnaires with 38 subscales  were not different between the groups . The volumes of the NAc, amygdala, and hippocampus were first examined because they represent risk factors for developing pathological emotional statesans Fig. . Importaans Fig. . The difans Fig. . Whole-bans Fig. . These aans Fig. . The ide22. We performed a modularity analysis segregating the functional networks into 6 communities, and restricted all analyses to the default mode network (DMN), sensorimotor (SM), and frontoparietal (FP) communities, due to their overlap with placebo-related networks observed in OA. Subcortical limbic regions were added along with the PAG because of their involvement for placebo response and in pain chronification  or the DLPFC (Broadman area 46). Precisely, PTxResp displayed stronger connections for the link VLPFC-PreCG, and weaker connections for links VLPFC-rACC and VLPFC/DLPFC-PAG Fig. . As expeWe then examined the variability of each of these anatomical and functional brain measurements in patients of the NoTx arm. We observed no changes across the visits, indicating stability of the measure without placebo effects  procedure where placebo outcome of each patient was predicted using an independent training sample. Within each We initially used data from the questionnaires to classify the patients into response groups (binary variable approach). Within each training sample set, the scores of the normalized 38 subscales were used to build the support vector machine (SVM) classifier. SVM classification achieved an accuracy of 0.72 in classifying placebo pill response . Sensitivity of this approach was 0.73 , and specificity was 0.71  Fig. .Fig. 5Maz-score of 1.96). Moreover, adding features from rsfMRI or brain anatomy to the questionnaire data did not improve the classifier’s accuracy (accuracy of 0.65).We next trained a classifier using brain imaging data entering either brain anatomy or rsfMRI as predictors, but they failed at classifying PTxResp and PTxNonR above chance level  regression to predict the magnitude of placebo response (continuous variable approach), using a nested LOOCV procedure. The model was trained in As with the previous approach, we initially predicted the magnitude of response using just the combination of questionnaire data Fig. . The scan-1 patients training sample set, we performed feature selection to identify links correlating with the magnitude of response  prior to the LASSO regression. These connections were used to train a predictive model using tenfolds cross-validations for tuning the LASSO parameters, which was tested in the left-out patient . Furthermore, a linear regression entering the value of the predicted magnitude of response from rsfMRI and the value of the predicted analgesia from the questionnaires data revealed that both models explained independent variance of the actual response, suggesting that they are complementary to one another , indicating that the model was more predictive in individuals ingesting placebo pills.We finally tested whether our models were specific for placebo pill analgesia or if they were predicting unspecific improvement of symptoms. We first used univariate statistics to compare NoTxResp with NoTxNonResp on the brain properties dissociating PTxResp from PTxNonR Figs.  and 4. T37), mechanisms described for placebo response in healthy subjects experiencing acute pain38 , as well as novel systems associated with placebo pill response in chronic pain . Machine learning applied to questionnaire and brain imaging data could, only in part, classify placebo response and predict the individuals’ magnitude of placebo response. Given the moderate to large effect size of RCT placebo effect , our results imply that gaining a better understanding of placebo pill response has important clinical utility. Our results demonstrate the psychological, functional, and anatomical determinants of the placebo response and suggest that once patients begin a placebo treatment, their individual pain relief may be predicted in the context of a RCT.This is the first brain imaging RCT specifically designed to study chronic pain patients receiving placebo pills compared to a no treatment arm. Daily pain ratings from a smart phone revealed that patients receiving placebo pills showed stronger pain reduction and a higher response rate compared to patients in the no treatment arm, indicating that placebo pills successfully induced analgesia that could not be explained by the natural history of the patient or the mere exposure to the study. Our results show a multiplicity of biological systems, partially overlapping with complex inter-relationships, underlying placebo pill response. The identified systems seem to encompass brain properties known to be involved in chronic pain maintenance or in the transition to chronic pain  without changing the trajectory of pain quality (which improved in time regardless of placebo pill ingestion and/or placebo response). Even various measures of pain intensities—daily ratings, memory, and NRS—provided slightly different information about the extent of analgesia and pain fluctuations. This highlights the importance of examining a multiplicity of pain-related outcomes as the analgesic properties of any given treatment may not be constrained to a single dimension of the pain experience, and the measures used to capture analgesia may be differentially influenced by different factors. Most current RCTs assessing new treatments are designed with a single primary outcome representing a “gold standard”, thus likely missing out on the complexity of treatment effects that chronic pain may exhibit. Our behavioral results stress the importance of moving away from a single, cross-sectional pain measurement, and demonstrate that distinct dimensions respond differentially to placebo pill ingestion, impacting mainly perceived magnitude but not its qualities. It is however possible that placebo might only impact the intensity of pain while a successful active pharmacological treatment would improve both intensity and qualities. This remains an open but important area of inquiry.Our study design included two washout periods in order to determine stability and within-subject re-occurrence of response. The use of EMAs allowed us to determine that placebo analgesia started on the first day of treatment, but the return to baseline levels of pain started only several days after washout. Thus, the washout periods were proven too short to test the re-occurrence of response. Instead, our data showed a carryover effect of the placebo response after discontinuation of treatment.43—optimism, anxiety, extraversion, neuroticism—successfully differentiated placebo responders from non-responders in our chronic pain patients. In our patients, placebo pill response was driven primarily by a combination of a greater openness to experience, increased emotional awareness, decreased distraction about pain and discomfort, augmented capabilities in describing inner experiences, and higher sensitivity to non-painful situations. Our results reveal that placebo response can be predicted from an ability to recognize subtle cues in the body regarding emotional and physical well-being, to remain attentive to these cues and emotions by not ignoring or suppressing them, and to choose to accept these states as opposed to becoming worried or burdened by them. These factors of personality were able to differentiate PTxResp and PTxNonR as well as predict the magnitude of placebo response in new patients. These results are critical, as questionnaires are easy to administer and may be sufficient to predict placebo pill response in chronic pain.Associating a psychological profile with placebo pill response in CBP departs from the literature regarding placebo in healthy subjects. None of the often-cited personality traits in placebo literature9 through the PAG25, a mechanism that can be reversed by naloxone44. Besides these anti-nociceptive circuits, the placebo effect is also dependent on subcortical circuitry involved in reward/aversion prediction error, as well as higher-order frontal mechanisms (including the DLPFC and the OFC) involved in context generation, expectation of treatment outcomes, and emotional appraisal of events39. As such, levels of activation in the DLPFC and the OFC are believed to represent the strongest predictors of experimental placebo response in healthy controls45. The present results indicate that these systems are also part of the placebo pill response in CBP patients, although direct correspondences between functional networks and regional activity remain uncertain. The coupling of the DLPFC and rACC with anti-nociceptive circuitry is also consistent with our previous observation that these regions were predictive of placebo response in OA patients22. Therefore, there are close correspondences in the mechanisms underlying placebo pill response across different types of pain  and in different settings (RCT vs. laboratory).In healthy individuals, the placebo response recruits endogenous pain pathways acting upon the opioid system to regulate descending inhibition from the rACC12. Brain networks predicting the magnitude of response in a fully cross-validated procedure further reveal the contribution of limbic circuitry, TPJ, and prefrontal connectivity (DLPFC-PAG and OFC-PreCG) for placebo pill response. Because the model was not stable in the post-treatment visits, its capacity for predicting magnitude of response in a new set of chronic pain patients remains to be determined in further studies. The procedure was nevertheless informative regarding the neurophysiological contributors to the placebo response. Our original experimental design included two washout periods  specifically designed to monitor perturbations in functional features, like VLPFC/DLPFC-PAG, and to observe whether following washout, they would continue to track placebo pill responders. Unfortunately, the pain trajectories showed sustained effects of placebo analgesia during washout periods that were proven too short to be informative regarding within subject replication of placebo effects.For the placebo response-related network differentiating PTxResp from PTxNonR, the DLPFC-PreCG, and DLPFC-rACC links persisted in time and across visits. However, the initial VLPFC/DLPFC-PAG connections differentiating PTxResp and PTxNonR dissipated, likely due to this network being more dependent on learned expectations and thus requiring novel reinforcing experiences to become reactivated. As the VLPFC/DLPFC-PAG includes commonly described brain regions associated with placebo response in healthy subjects, its dissipation with exposure is consistent with evidence pointing to the failure of placebo re-exposure in healthy subjects in replicating the original placebo response46. Here, many of these potential confounds were accounted for by incorporating novel methodological strategies such as: including a no-treatment arm documenting the natural history of the patients, using smart phone technology accounting for natural fluctuations of pain outside of the clinical setting, collecting multiple pain outcome measures, performing analyses blindly using one real code and two scrambled codes to minimize bias, and utilizing machine learning methods to estimate predictability in a fully cross-validated procedure.Several pitfalls have been raised when trying to predict complex behaviors like the placebo response47. Despite the complexity of the phenomenon, our results challenge this assumption as placebo response could be partially predicted in chronic pain patients. Precisely, machine learning applied to psychological factors or functional connectivity showed that magnitude of response was predictable. The predictions from both models were not correlated to one another and they were independent predictors of analgesia. Importantly, the joint prediction from both models was more accurate in the PTx arm compared to the NoTx, suggesting a certain level of specificity unique to placebo analgesia. Together, our results contribute to the placebo literature by demonstrating the existence of psychological and neurobiological principles determining the placebo response in RCTs.Some have argued that clinical trials do not provide an appropriate context to study the psychobiology of placebo because they are contaminated by uncontrollable confounds and that instead placebo should be studied in a controlled environment, such as a laboratoryThe total duration of the study lasted ~15 months. The first patient was seen on 11/06/2014, and the last patient was seen on 02/04/2016. During that time, 129 participants with chronic low back pain (CBP) were initially recruited from the general population and clinical referrals via hospital databases and advertising in the community. Patients were assessed for general eligibility via self-report using a screening intake form that the lab has used for years in other studies; this screening interview covered co-morbid health and psychological conditions, MRI safety, concomitant medication dosages and indications, pain levels/location/duration, current and previous illicit drug/alcohol use, litigation status, and overall willingness to be in a research study. To meet inclusion criteria, individuals had to be 18 years or older with a history of lower back pain for at least 6 months. This pain should have been neuropathic , with no evidence of additional co-morbid chronic pain, neurological, or psychiatric conditions. Individuals had to agree to stop any concomitant pain medications and had to be able to use a smartphone or computer to monitor pain twice a day. Additionally, the enrolled patients had to report a pain level of at least 5/10 during the screening interview, and their averaged pain level from the smartphone app needed to be higher than 4/10 during the baseline rating period (explained below) before they were randomized into a treatment group. Finally, for safety precautions, clinical measurements taken at visit 1 were required to be within the pre-specified healthy range (as determined by the standards utilized by Northwestern University Feinberg School of Medicine Laboratory Services Department) and all participants passed the MRI safety screening requirements at each scanning visit. Informed consent was obtained from all participants on their first visit.may receive a placebo or they may receive an active treatment). Therefore, the 5 participants randomized in the active treatment group were not analyzed. The final sample size included 20 CBP patients randomized to the no treatment group and 43 CBP patients randomized to the placebo treatment group; demographics for these individuals can be found in Supplementary Table Supplementary Figure d effect sizes for differences in pain with a 2-week placebo treatment; this was based on preliminary results from a different study that was ongoing at the time this RCT was being planned. For responders, we anticipated a mean decrease of 30 units on a 0–100 scale, with an estimated standard deviation of 15; this results in an effect size estimate of 2.0. In non-responders, the mean decrease in pain was anticipated to be negligible and we did not expect to have enough power to detect this. Power analyses performed in G*Power, version 3.1.3, indicated that we would have ample power—even with a conservative estimated effect size of d = 1.0, power would be 80% for a sample size of n = 17 per group, which would also permit detection of interaction effects. In addition, it ensured adequate sample sizes even assuming some attrition in each group. For brain imaging contrasts, we thought that 20 per group should be adequate given preliminary fMRI results and earlier studies; for T1 results, our earlier studies indicated that 20/group for within-subject contrasts would have been adequate but possibly just at the limit for whole-brain contrasts to detect between-group differences. Therefore, we ended up aiming for a sample size of n = 20 per group to achieve effect sizes of about 1.0 . Since, we did not classify patients as “responders” or “non-responders” until after the study, we had no way of knowing exact stratification of groups during the study, which resulted in slightly uneven group sizes.The number of patients recruited was based both on the power analysis and on our previous experience with attrition rates in studies with similar patient populations; the final sample sizes based on the following effect size estimates were approved by the sponsor (NCCIH) prior to starting the study. We estimated our statistical power using the Cohen’s https://www.clinicaltrials.gov/ct2/show/NCT02013427). The study consisted of 6 visits spread over ~8 weeks  institute matched the appropriate treatment drug with patients’ randomization code; critically, only this NUCATS member had access to the document linking patient IDs to randomization IDs, and this linking information was only made available if a serious adverse event (SAE) occurred (which did not happen). After these procedures, study coordinators picked up the blinded agent from NUCATS for storage and dispensing; all drugs were stored at room temperature in a locked cabinet within the lab and monitored daily for temperature changes, bottle counts, and expiration dates. The double blind for treatment groups was maintained by the identical encapsulation of the study agent—blue pills were either Naproxen (500 mg) or placebo (lactose) and bi-colored pills were either Esomeprazole (20 mg) or placebo.Each person assigned to treatment received a mixture of blue and bi-colored pills. This way, neither the participants nor the researchers knew which treatment the participant had received. For those assigned to the no-treatment group, no blind was maintained, as both study staff and participants knew that they were not receiving the study agent. Once ~50% of all participants had been entered into the study, a preliminary analysis of the electronic pain rating data was completed in order to confirm that there were participants who were experiencing a diminution in pain (no action was taken).Visit 1: Participants were screened for eligibility and consented on visit 1. Following informed consent, a blood sample was drawn , vital signs were taken , and a medical professional completed a physical examination and took a comprehensive pain history. Participants were then asked to complete a battery of 29 questionnaires regarding basic demographics, pain, mood, and personality  scans (details are presented below). Following the imaging protocol, the patients completed another battery of questionnaires, a subset of which were repeated from the first visit to track longitudinal changes in pain. They were asked whether they had experienced any changes in health status since the last visit. Additionally, they were asked to verbally recall their average pain levels over the previous 2 weeks, and over the preceding week. This self-reported recalled pain was referred to as “pain memory” and was used as an alternative outcome measure of pain levels.At the end of this visit, participants were randomized into one of three groups: no-treatment, placebo treatment (lactose) or active treatment . Participants in the treatment groups were instructed to take a blue pill with a bi-colored pill in the morning and again at night with plenty of water, and they were asked to record this in their electronic rating app. Note that study staff never informed participants about the odds for receiving active versus placebo treatment—this is important, as the goal was to have participant’s own baseline expectations influence whether or not they responded to the placebo treatment. Both treatment and no treatment groups continued to receive rescue medication to use if needed, and all participants were asked to continue rating their pain twice a day until visit 3. The duration of this first treatment period was ~2 weeks long.Visit 3: Patients returned at visit 3 and were queried about their memory of their pain, any changes in health since the last visit, and rescue medication usage. If on treatment, patients were asked to report any side effects experienced and bring back any unused medication so that study staff could calculate their treatment compliance. Participants underwent another scanning session that was identical to the one completed at visit 2 and completed another set of questionnaires with some repeated from the previous visit. At the end of visit 3, individuals assigned to the treatment group were told that the study agent would be temporarily discontinued until their next visit so that the effects of the agent could “washout” of their system. Again, all participants were given rescue medication to use if needed and were asked to continue using their app twice a day until the next visit. This first washout period was ~1 week long.Visit 4: Patients returned at visit 4, where all measurements and procedures from visit 2 were repeated identically, including the scanning session and questionnaires. Again, they were queried about their pain memory, rescue medication usage, and changes in health. The study agent was reintroduced to those individuals allocated to one of the treatment groups according to the same regimen described above . During the consent process and treatment administration, patients were informed that they were receiving the same treatment as the one administered during the first treatment period so that expectations were not explicitly manipulated in anyway. All participants were given rescue medication and asked to rate their pain and mood twice a day, as with previous visits. Like the first treatment period, the second treatment period was also ~2 weeks in length.Visit 5: Following this period, participants returned for visit 5, where all measurements and procedures from visit 3 were repeated identically. Patients underwent the same scanning procedures as on visits 2–4. Finally, patients filled out a series of questionnaires about their pain, some of which were repeated from the last visits. As before, those participants allocated to a study agent had their treatment discontinued for a second washout period, which was also ~1-week long. Participants continued to use their electronic app twice daily and were given rescue medication if needed.Visit 6: Patients returned for the last visit during which they were again queried about their pain memory, changes to health, and rescue medication usage. During this visit, the patients completed a semi-structured, open-ended exit interview with a designated staff member. They were asked more detailed questions about their pain and medical history, quality of life, overall mood, and time in the study. Participants finished with a final battery of questionnaires and were asked to return study smartphones, if applicable. There were no scanning procedures on this visit. Any ratings submitted for the duration of the study were totaled, and in addition to their visit compensation, participants received their compensation for the electronic app at this time.; Supplementary Figure Each patient’s pain was monitored electronically using an application designed specifically for the study . It was later noticed that 3 additional participants had met this exclusion criteria but accidentally continued in the study. One person was assigned to no-treatment and was discontinued as a protocol deviation before study completion; the other two individuals finished the study in the placebo treatment group but were not included in the analysis.To verify that pain levels remained within the inclusion criteria specified above, all participants’ ratings were closely monitored for the first 2 weeks of the study as part of a run-in/baseline pain period. Individuals not meeting this level were deemed ineligible and did not continue in the study , many participants exceeded this number of app ratings in 24 h due to over-compliance, reassessment of their pain, and/or cellular service problems. If pain ratings were entered within 30 min of each other, only the last rating was kept and taken as indicative of the participant’s final assessment of their pain levels at that time. Any additional ratings outside of this 30 min window were not considered duplicates and were kept as valid entries. Beside this cleaning process, no other changes were made to the ratings. In the instances where participants missed ratings, no attempts were made to interpolate or re-sample the data so that the temporal aspects of the ratings were left intact. The overall compliance of the phone ratings is reported for each group in Supplementary Table t-values obtained from the rearrangement of the pain ratings. The overall t-value obtained between baseline and treatment was used to determine if the null hypothesis could be rejected (p < 0.05) for each of the treatment periods. In the cases where the null hypothesis could not be rejected for either of the treatment periods, the patient would be stratified as a “Non-Responder”. Alternatively, the patient would be stratified as a “Responder” if there was a significant diminution in the pain ratings. The main advantages of using a permutation test is that it takes into consideration the variability across pain ratings during the baseline and treatment periods and it represents a statistically defined cutoff point for response (unlike cutoff points arbitrarily defined by a percentage change in pain). Correcting for autocorrelation in the time series of pain ratings did not change stratification  while the % analgesia rather represents a continuous measure determining the importance of the response.Effect sizes were calculated as the difference between the analgesia in the PTx group and analgesia in the NoTx group, divided by the standard deviation of all the data.28. The memory of pain represents one of the standard pain assessments used by physicians in clinical practice and has been shown to correlate well with daily pain diaries in previous studies48. Other pain outcomes were collected using the McGill pain questionnaire (MPQ), affective and sensory scales, and the pain detect, which have been widely used in both randomized clinical trials and research labs, although their utilization in placebo-only trials remains minimal49. The neuropathic pain scale (NPS) was initially administered but not included as a main pain outcome since we aim to dissociate measurements of intensity from qualities of pain, while the NPS represents a combination of intensity and qualities of pain.The primary pain outcome measured with the phone app was compared with five additional secondary outcome measures of pain level. The numerical rating scale (NRS) and the memory of pain were reported as the two other primary pain outcomes relying on numerical scales. Their correspondence with the phone app is presented in Fig. 50, and the importance of transparency in data analysis, we followed recommendations by MacCoun and Pearlmutter51 and employed cell scrambling to further blind our data and minimize bias. See Supplementary Table Given the recent issues regarding a lack of reproducibility in scientific findingsBrain imaging data were acquired with a Siemens Magnetom Prisma 3 Tesla. The entire procedure was completed in about 35 min, but an extra 25 min was allocated to install the patients in a comfortable position to keep their back pain at a minimum, and to re-acquire images if the data were contaminated by head motion.High-resolution T1-weighted brain images were collected using integrated parallel imaging techniques  representing receiver coil-based data acceleration methods. The acquisition parameters were: isometric voxel size = 1 × 1 × 1 mm, TR = 2300 ms, TE = 2.40 ms, flip angle = 9°, acceleration factor of 2, base resolution 256, slices = 176, and field of view (FoV) = 256 mm. The encoding directions were from anterior to posterior, and the time of acquisition was 5 min 21 s.Blood oxygen level-dependent (BOLD) contrast-sensitive T2*-weighted multiband accelerated echo-planar-images were acquired for resting-state fMRI scans. Multiband slice acceleration imaging acquires multiple slices simultaneously, which permits denser temporal sampling of fluctuations and improves the detection sensitivity to signal fluctuation. The acquisition parameters were: TR = 555 ms, TE = 22.00 ms, flip angle = 47°, base resolution = 104, 64 slices with a multiband acceleration factor of 8 (8 × 8 simultaneously acquired slices) with interleaved ordering. High-spatial resolution was obtained using isomorphic voxels of 2 × 2 × 2 mm, and signal-to-noise ratio was optimized by setting the field of view (FoV) to 208 mm. Phase encoding direction was from posterior to anterior. The time of acquisition lasted 10 min 24 s, during which 1110 volumes were collected. Patients were instructed to keep their eyes open and to remain as still as possible during acquisition. The procedure was repeated two times.f < 0.1 Hz) and a non-linear spatial filter . Following this, we regressed the six parameters obtained by rigid body correction of head motion, global signal averaged overall voxels of the brain, white matter signal averaged overall voxels of eroded white matter region, and ventricular signal averaged overall voxels of eroded ventricle region. These nine vectors were filtered with the Butterworth band-pass filter before being regressed from the time series. Finally, noise reduction was completed with Multivariate Exploratory Linear Optimized Decomposition into Independent Components (MELODIC tool in FSL) that identified components in the time series that were most likely not representing neuronal activity. Components representing motion artefact were identified if a ratio between activated edge (one voxel) and all activated regions on a spatial component was >0.45, or if ratio between activated white matter and ventricle and whole-brain white matter and ventricles was >0.35. Moreover, noisy components were identified if the ratio between high frequency (0.05–0.1) and low frequency (0.008–0.05) was >1. This ICA regression process was kept very conservative so that only components obviously related to motion or noise were removed.The pre-processing was performed using FMRIB Software Library (FSL) and in-house software. The first 120 volumes of each functional data set were removed in order to allow for magnetic field stabilization. The decision to remove this number of volumes was taken arbitrarily (it was not motivated upon examination of data) and we explored no other option. This left a total of 990 volumes for functional connectivity analyses. The effect of intermediate to large motion was initially removed using fsl_motion_outliers. Time series of BOLD signal were filtered with a Butterworth band-pass filter  = 0.24; p = 0.79) or the second run  = 0.66; p = 0.52).On average, relative head motion was relatively low (mean frame displacement (FD) = 0.11; std 0.07 for the first rsfMRI run, and mean FD = 0.11; std 0.09 for the second run. Importantly, there were no group differences between the mean relative frame displacement for either the first run  located at coordinates showing reliable activity across a set of tasks and from the center of gravity of cortical patches constructed from resting state functional connectivityOne patient from the no treatment arm was excluded from all rsfMRI analyses because of aberrant values in the correlation matrix . This subject was a priori rejected during initial quality check and was never included in any of the analyses.https://sites.google.com/a/brain-connectivity-toolbox.net/bct/)53 to determine consistent community structures across a large number of network partitions54. For each subject, the individual community structure was initially constructed from 100 repetitions of the same network. The group community was then constructed from 100 × 63 patients, generating a total of 6300 networks. The final community structure was created by thresholding the averaged within-module connectivity likelihood matrix at 0.5, meaning that if the likelihood for two nodes belonging to the same module was above 50%, they were considered in the same module. This permitted us to identify six separate communities, including the four communities of interest , the anterior cingulate cortex , the posterior cingulate cortex , and the right somatosensory cortex . We next entered these coordinates as seeds in the Neurosynth analytic tool  and extracted three networks sharing strong connectivity with these seeds: the DMN, the frontoparietal network, and the sensorimotor network. We identified communities corresponding to these networks based on spatial overlap, by multiplying the networks of interest with the nodes pertaining to each community. A total of 113 nodes were affiliated with these communities . We used the Network-Based Statistics toolbox implemented in Matlab57 and freely available on the Neuroimaging Informatics Tools and Resources Clearinghouse (NITRC). The toolbox is specially designed for mass-univariate testing of connections in graphs and for accounting the dependence issue. Briefly, the first step consists of independently test every connection in the network and identify the connections with a test statistic value exceeding a threshold. Then, the toolbox identifies the topological clusters among the set of suprathreshold connections. The fisher-z transformed correlation coefficients z(r) of the significant connections were extracted at each visit and entered in a repeated measured ANOVA testing for an interaction of time with placebo treatment response.Network statistics were performed to identify brain networks predisposing to placebo response (performed on the 122 × 122 connectivity matrix). Group differences were examined using a permutation test (5000 permutations) on the connections of the weighted network (122 × 121 nodes), controlling for false discovery rate (FDR p < 0.05).Gray matter density was examined using voxel-based morphometry from FSLVBM. All T1-weighted images were first brain extracted and then segmented into gray matter, white matter, or cerebrospinal fluid. A common gray matter template was generated for CBP by registering and averaging all gray matter images. The gray matter image of each participant was then registered to the common template using non-linear transformation. A voxel-wise permutation test was used to test the significance of group differences between placebo responders and non-responders to a distribution generated from 5000 permutations of the data for each voxel of the template, using a sigma filter of 3 mm for smoothing. The initial analysis established significance level using the Threshold-Free Cluster Enhancement (TFCE) method (FWE http://surfer.nmr.mgh.harvard.edu/). In brief, the structural processing includes skull stripping, intensity normalization, Taliarch registration, segmentation of the subcortex, reconstruction of the cortical surface, and tessellation of the gray/white matter boundary and pial surface. Following reconstruction of the cortical surface, brains were inflated, averaged across participants to produce a study-specific brain, and then smoothed using a 10 mm full-width at half maximum Gaussian kernel. A direct measure of cortical thickness was calculated using the shortest distance (mm) between the pial surface and gray-white matter boundary at each point or vertex. Cortical thickness analysis for each hemisphere was conducted using FreeSurfer’s Query, Design, Estimate, Contrast (QDEC) graphical interface. The initial vertex-wise comparison was performed between placebo responders and non-responders for each hemisphere. Correction for multiple comparisons was performed using random-field-theory-based significant clusters at p < 0.05. Values of cortical thickness were extracted in the significant cluster surviving multiple comparison and compared between PTxNonR, PTxResp, NoTxNonR, NoTxResp groups using a one-way ANOVA. The values of cortical thickness in the significant cluster were then extracted at each visit and entered in a repeated measured ANOVA.Cortical thickness was examined using Freesurfer software library  was created, where a result = 1 would be indicative of perfect subcortical symmetry, whereas numbers >1 or <1 would indicate asymmetry biased toward the right or left hemispheres, respectively. Volumes and subcortical asymmetry were compared between PTxNonR, PTxResp, NoTx using a one way ANCOVA controlling for peripheral peripheral gray matter volume, age and sex. The effect was tested across all visits using repeated measure ANCOVA controlling for peripheral peripheral gray matter volume, age, and sex.Volumetric analyses of T1-weighted images were performed through automated processes using both FSL (version 5.0.8) and FreeSurfer (version 6) software. We investigated volume differences in 3 subcortical nuclei selected a priori; the NAc, the amygdala, and the hippocampus. After using FSL’s brain extraction tool (BET) to remove the skull from all images, FSL’s integrated registration and segmentation tool (FIRST) was utilized to segment these specific subcortical regions and extract their volume measurementsOver the course of the 6 visits, participants filled out 29 unique questionnaires. These specific self-report measures were chosen for one of 4 reasons: (1) to gather basic information about participants, including demographics and pain/medical history, (2) to track any changes in the quality and/or intensity of pain characteristics as measures of treatment efficacy, (3) to monitor any changes in emotional affect which may have influenced someone’s time in the study or their treatment response, and (4) to capture trait-based qualities, general habits and beliefs, or state-related expectations of individuals that may predispose them to respond to placebo. Questionnaires used to track pain and mood changes overtime were repeated across all study visits. Questionnaires that targeted expectations towards treatment and satisfaction after treatment were conducted twice—either before treatment sessions (visits 2 and 4) or after treatment periods (visits 3 and 5), respectively. In contrast, measures that aimed to identify more stable traits of participants were completed at visit 1, which allowed us to use them as possible predictors of response. Finally, a subset of questionnaires regarding beliefs toward alternative medicines and suggestibility were administered at the final visit after the exit interview. A full list of all questionnaires used, along with descriptions and references, can be found in Supplementary Table 59. It was utilized in order to conserve statistical power, given our relatively small sample size. Of all the self-report data analyzed, <3% was totally missing and thus unable to be filled in as described above.Data from these self-report measures were downloaded directly from REDCap as a CSV file and scored in Excel according to their references. Because all questionnaires were converted to an electronic format in order to be used in REDCap, an option to “skip” a question was provided if the participant did not feel comfortable answering a certain item. If >20% of the data from a given questionnaire  was missing, the person’s data for the questionnaire was not scored; for all other missing data, the mean was used to fill in missing items ; this approach is one of the most commonly used methods in data analysisFor pain measurements converted in %analgesia, outliers were defined as values exceeding 3 standard deviation from the mean and were excluded from the analyses. For each analysis, the number of participant is displayed in each figure.n = 42) and applied to a left-out participant. The purpose of the inner loop was to optimized the parameters of the model through cross-validation. Once the optimal model showing the least amount of error was identified, it was applied to the left-out participant to either classify the patient as a PTxResp or PTxNonR or predict the magnitude of his response. This procedure was applied to build predictive model from rsfMRI, brain anatomy and personality independently.The predictive value of brain imaging and questionnaires data were tested using models of machine learning. We implemented a nested leave-one-out-cross-validation (LOOCV) procedure where models were trained in an inner loop (https://github.com/gregversteeg/CorEx); 40 variables), or averaged connectivity within and between communities. Features selection was also performed using univariate t-test on each connection to identify group differences  or links correlating with the magnitude of response , unsupervised machine learning  volumes of the 16 subcortical structures segmented with FSL FIRST, (3) subcortical volume asymmetry (ratio Right/Left) of these 16 subcortical structures. Because the features were derived from different measurements, the data were normalized.Anatomy: Three different measures were used to predict the magnitude of response using brain anatomy: (1) the averaged cortical thickness in the 74 labels per hemispheres from the Destrieux AtlasQuestionnaires: All self-report measures collected at V1 were entered in the model. Because the questionnaires were on different scales, the data were normalized.n = 42) and applied to a left-out participant. The box constraint and the radial basis function (rbf) kernel were optimized through a tenfolds cross validation strategy within the inner loop. Once the optimal SVM model was identified, it was applied to classify the left-out patient as a PTxResp or PTxNonR.Support vector machine (SVM) was used to discriminate between PTxResp and PTxNonR using fitcsvm function implemented in Matlab. We implemented a LOOCV procedure where SVM models were trained in an inner loop (n = 42) and applied to a left-out participant. The inner loop determined feature selection and lambda regularization parameters using tenfolds cross-validation. The generalization error was estimated by testing the model to the left-out patient. The procedure was performed using the lasso function, implemented in Matlab.We used Least Absolute Shrinkage and Selection Operator (LASSO) regressions to train a model predicting the magnitude of response. Here again, we used a nested LOOCV procedure where models were trained in an inner loop (A description of each statistical test and its exact values are reported in Supplementary Table http://www.openpain.org/. The data are part of a longitudinal study that will generate more than one manuscript. The data will eventually be made available on open pain once these manuscripts are completed. Since then, data are available upon reasonable request.Data from our previous studies are already available on Supplementary InformationPeer Review File


Central post-stroke pain (CPSP) is an under-recognized complication of stroke although it can lead to deterioration in quality of life and impairment in activities of daily living. Its estimated prevalence varies between 18.6 and 49%.To investigate the prevalence and predictors of CPSP in ischemic stroke patients and to find its relationship with somatosensory evoked potentials (SSEPs) and magnetic resonance imaging.Sixty five consecutive patients with recent first attack of ischemic stroke who were admitted to the Neurology Department, Suez Canal University Hospitals were recruited. Patients were subjected to clinical assessment, Hamilton depression rating scale, brain MRI, short-form McGill Pain Questionnaire (SF-MPQ), daily pain rating scale (DPRS), stimulus evoked pain, and SSEPs.n = 23). The mean age of the patients developed CPSP was significantly lower than those without CPSP (p = 0.004). Deep sensory dysfunction was statistically significantly higher among CPSP group than non-CPSP group (p = 0.001). CPSP group showed statistically significant higher prevalence of thalamic stroke (p = 0.007), as well as significant abnormalities in inter-peak interval (IPL) of median and tibial nerves SSEPs (p < 0.05). Thalamic group showed higher abnormalities in IPL of median and tibial nerves compared to extra-thalamic group, but without statistically differences.The total prevalence rate of CPSP was 35.4% (The prevalence of CPSP was found to be 35.4%. Predictors of CPSP include; deep sensory dysfunction, prolongation of tibial N21–P40 IPL, smoking history, age < 50 years, presence of thalamic stroke and prolongation of median N9–N20 IPL. After stroke, central post-stroke pain (CPSP) is not an uncommon complication which may interfere with quality of life. Post-stroke pain (PSP) is a common but not take much attention in stroke research; in different studies its prevalence varies from 18.6 to 49% . CPSP maSSEPs could be of value in detection and assessment of CPSP. SSEPs may document a lesion in the central sensory and lemniscal pathways in patients with CPSP, and give information on the pathological process , the site of the lesion , and the presence of subclinical involvement at other sites. They have diagnostic value, because most diseases causing CPSP do not result in damage or lesion limited to the nociceptive system, but extend to the whole peripheral nerve or involve non-nociceptive pathways .The aim of the work is to investigate the prevalence of central post-stroke pain (CPSP) in ischemic stroke patients, its predictors, and its relationship with somatosensory evoked potentials (SSEPs) and magnetic resonance imaging.The study included 65 consecutive patients with recent first attack of unilateral ischemic stroke admitted to the Neurology Department, Suez Canal University Hospital. We included both genders, age ≥ 18 years. We excluded patients with diabetes, history of old cerebrovascular stroke, disturbed level of consciousness, dementia, aphasia to an extent that patients could not explain themselves, degenerative neurological diseases, peripheral neuropathy, history of brain tumors, history of autoimmune disease, history of psychiatric disorders, and patients showing signs of peripheral neuropathy on nerve conduction studies.A-Clinical assessment:The onset of CPSP from the day of stroke, distribution, character, and severity of painSensory testing—sensory testing was done first on the normal side followed by abnormal sides. Pinprick and touch was tested. For temperature testing, cold metal rod, and for vibration, a 128-Hz tuning fork was used. Joint position sense was tested in toes and fingers using 1° deflection.Hamilton depression rating scale , a multiple choice questionnaire was used to evaluate and to demonstrate the severity of depression .All selected patients were evaluated within 7 days after stroke onset and followed at periodic intervals . The following were done:Other common causes of pain exclusion.Pain with a special neuro-anatomically apparent distribution: Either pain confined in one side in the body and/or face or one on one side of the body with other side involvement of the face.A history revealing stroke: Sudden occurrence of neurological symptoms with pain onset at or after strokeSigns of the special neuro-anatomically apparent distribution by neurological examination: Prediction of sensory negative or positive signs in the painful area, unprompted and/or evoked pain localized within a branch of sensory abnormality, and anatomically apparent distribution of sensory dysfunctionPointing to relevant vascular lesion by imaging: CT or MR brain shows a lesion that can explain the sensory findingsCPSP criteria to be evaluated for each patient based on a grading system for neuropathic pain by Treede and co-workers (2008)  are as fB-MRI brain: Carried out by Philips Achieva 1.5 Tesla MRI scanner, Germany. It was done within 1 week of the onset of stroke. T2, T1, and diffusion weighted images in the sagittal and axial planes were obtained. Lesions detected by MRI were grouped into thalamic and extra-thalamic.C-. The SF-MPQ, a shorter version of the MPQ, is a multidimensional measure of perceived pain in adults with chronic pain. The SF-MPQ consists of three items: pain rating index (PRI), present pain intensity (PPI), and visual analog scale (VAS).Short-form McGill Pain Questionnaire (SF-MPQ): The severity of CPSP was quantified by SF-MPQDaily pain rating scale (DPRS) is 0–10 cm, in which 0 is absent of pain and 10 is the worst pain in last week. The patients recorded the DPRS every day in the last week before the following visit and average was calculated.Stimulus-evoked pain was evaluated for static mechanical allodynia, dynamic mechanical allodynia, cold allodynia, and punctate hyperalgesia.Quantitative assessment of neuropathic pain. We used the following scales:D-Somatosensory evoked potential (SSEPs) studies were done using the Neuropack X1 EMG/EP measuring system MEB-2300, NIHON KOHDEN machine, 4 channels. They were performed for all patients on eligibility (baseline) then performed to the diagnosed patients with CPSP during follow-up and finally performed for the patients who do not experience CPSP at the end of follow-up (6 months).Median SSEP was recorded from the affected hemiparetic limb. Median nerve was stimulated at wrist by 0.1-ms square-wave pulse at 3 Hz, the intensity which make the thumb twitch with no pain. At Erb’s point, active electrode for surface recording was placed, and at opposite parietal cortex 3 cm behind and 7 cm lateral to vertex, mid frontal (Fz) reference was used. For the evaluation of median SSEP, N9 and N20 peak latencies and N9–N20 IPL were recorded.Tibial SSEPs was recorded from the affected hemiparetic limb. Posterior tibial nerve was stimulated under the medial malleolus by 0.1-ms square-wave pulse at 3 Hz, the intensity which make the great toe twitch with no pain. At the spinous process, the recording electrodes were placed on L1 vertebra and 2 cm caudal to Cz’. At L3 and Fz respectively, the reference electrode was placed. For the evaluation of tibial SEP, N21 and P40 peak latencies and N21–P40 IPL were recorded .Possible CPSP is diagnosed if criteria 1, 2, and 3 are met. Probable CPSP is diagnosed if criteria 1, 2, and 3 plus either criteria 4 or 5 are met. Definite CPSP is diagnosed if criteria 1–5 are met.Suez Canal Faculty of Medicine Ethical Committee approved this study in 26/11/2012; number of approval is 831, and all participants signed informed written consent before participating in the study.t test was used; while to test significance of difference between qualitative data, chi-square was used. A probability value  < 0.05 was considered statistically significant; while a probability value  < 0.01 was considered statistically highly significant. Odds ratio (OR) and 95% confidence interval (CI) were assessed using ordinal regression analysis model to test the best fitting predictors.Using SPSS IBM SPSS statistics, collected data were processed . Qualitative data were uttered as numbers and percentages and quantitative data were uttered as means ± SD. To test significance of difference between two means, unpaired The patients’ mean age was 59.6 ± 10.4 years. Male represented 64.6% while females were 35.4%. Though 15.4% of the patients were smokers and 53.8% was hypertensive. Twenty-three patients developed CPSP with prevalence rate of 35.4% that was subdivided as follows: 7.7% during the first week post-stroke, 9.2% during the first month, 7.7% during the third month, and 10.8% during the sixth month of follow-up , with no gender difference. Smokers developed CPSP more than non-smokers . Hypertension, AF, and TIA were not discovered to be significantly different in both groups (p > 0.05). Meanwhile, the prevalence of ischemic heart disease was significantly higher among patients without CPSP (p = 0.043). There were no statistically significant differences between both groups regarding depression .The mean age of the patients who developed CPSP (53.6 + 9.6) was statistically lower than those who did not (62.8 p = 0.001)   (7) Table .The left-sided lesion was significantly more in patients with thalamic compared to extra-thalamic affection  (Table p < 0.01)  Table .Table 3SThalamic location group showed higher abnormalities in SSEP-IPL of median and tibial nerves compared to extra-thalamic group, but without statistically significant differences.The best fitting factors significantly predict CPSP and include presence of deep sensory dysfunction OR 14.5), tibial N21–P40 IPL > 21 ms (OR 8.4), smoking history (OR 5.7), age < 50 years (OR 5.1), presence of thalamic stroke (OR 4.6), and median N9–N20 IPL > 10.8 ms (OR 3.9) (Table .5, tibiaCPSP remains to be an under-recognized sequel of stroke although it can lead to deterioration in quality of life and impairment in activities of daily living . In this+ 9.4 on other group; some studies indicate that CPSP is more prevalent in younger patients [In our study, the mean age of the patients who developed CPSP was 53.6 + 9.6 compared to 62.8 patients  and othepatients .The younger age of development of CPSP can be explained by that posterior territory infarcts, including brainstem and thalamic strokes, which are frequently associated with CPSP are relatively more common in the young age , 9. AlsoIn this study, no gender difference in both groups was detected, same results reported by Andersen et al. 1995 , on the In our study, smoking was a trigging factor, same results reported by Misra and colleague . Deep seIn this study, 47.8% of CPSP patients has thalamic location and 52.2% extrathalamic; the same finding was reported by Boivie et al. 1989  who demoCentral disinhibition, especially at the thalamic level, can cause CPSP. Activity of medial thalamus could be inhibited by lesions of lateral thalamus and cause pain via disturbance of inhibitory pathways between medial and lateral pathways. Inhibitory inter-neurons in thalamic reticular nuclei explain an indirect route of such disinhibition .According to SSEP, our study demonstrates that the mean peak latency and IPL differences were prolonged in patients with CPSP with highly statistically significant difference between both groups. Other studies mentioned that SSEPs are unaffected in Wallenberg’s syndrome and in some of the hemispheric but are generally abnormal when medial lemniscus is involved . On the Using regression analysis, predictors of CPSP were best fit to the presence of deep sensory dysfunction, smoking history, age < 50 years, thalamic stroke, prolonged tibial N21–P40 IPL, and prolonged median N9–N20 IPL. Similar results reported by Klit et al. 2014  who findLimitations of the study includes that large number of patients can be included, longer time of follow-up, management strategy and drugs used for every patient, and social factors for every patient.The prevalence rate of CPSP was 35.4%. Predictors of CPSP include deep sensory dysfunction, prolongation of tibial N21–P40 IPL, smoking history, age < 50 years, presence of thalamic stroke, and prolongation of median N9–N20 IPL.


Post-surgery adjuvant radiotherapy (RT) significantly improves clinical outcomes in breast cancer patients; however, some patients develop cancer or treatment-related pain that negatively impacts quality of life. This study examined an inflammatory biomarker, C-reactive protein (CRP), in RT-related pain in breast cancer.During 2008 and 2014, breast cancer patients who underwent RT were prospectively evaluated for pre- and post-RT pain. Pre- and post-RT plasma CRP levels were measured using a highly sensitive CRP ELISA kit. Pain score was assessed as the mean of four pain severity items  from the Brief Pain Inventory. Pain scores of 4–10 were classified as clinically relevant pain. Multivariable logistic regression analyses were applied to ascertain the associations between CRP and RT-related pain.In 366 breast cancer patients , 17% and 30% of patients reported pre- and post-RT pain, while 23% of patients had RT-related pain. Both pre- and post-RT pain scores differed significantly by race/ethnicity. In multivariable logistic regression analysis, RT-related pain was significantly associated with elevated pre-RT CRP (≥ 10 mg/L) alone (odds ratio (OR) = 2.44; 95% confidence interval (CI) = 1.02, 5.85); or combined with obesity  after adjustment for age and race/ethnicity.This is the first pilot study of CRP in RT-related pain, particularly in obese breast cancer patients. Future larger studies are warranted to validate our findings and help guide RT decision-making processes and targeted interventions.The online version of this article (10.1186/s13058-019-1151-y) contains supplementary material, which is available to authorized users. Breast cancer is the most frequently diagnosed cancer and the second leading cause of cancer death among American women . CompareA recent study reported the presence of racial-ethnic disparities in pain experience upon completion of RT , indicatCRP has been widely used as a robust inflammatory biomarker for many health conditions in both clinical and research settings, and several studies have shown a positive correlation between plasma CRP levels and pain intensity in cancer patients –19; howeTherefore, we aimed to examine the associations between CRP levels and RT-related pain among breast cancer patients who underwent adjuvant RT using a prospective study design. We hypothesized that breast cancer patients with elevated CRP levels would be more likely to report pain, which may identify CRP as an inflammatory biomarker for pain. We also hypothesized that patients with elevated pre-RT CRP may be at higher risk in developing RT-related pain. Pain sensitization is one of the most important risk factors for persistent pain , 23; thuData for the current analysis was obtained from a prospective cohort study  where the goal was to examine the disparity of RT-induced early adverse skin reactions in a racially and ethnically diverse population of breast cancer patients. Briefly, the study recruited breast cancer patients from the Radiation Oncology clinics at the University of Miami Sylvester Comprehensive Cancer Center and Jackson Memorial Hospital in Miami, Florida, between December 2008 and August 2014. Patients were followed up for up to 12 months after the completion of RT. At the time of enrollment, each participant completed a self-administered baseline questionnaire. In addition, participants completed QOL questionnaire on the first day before initiation of RT, on the last day immediately after completion of RT, and at each follow-up visit . The current study only used QOL data collected on the first day of RT  and on the last day of RT (i.e. post-RT). The treating radiation oncologist met patients each week during the radiation treatment and evaluated adverse skin reactions at week 3 (mid-treatment), at week 6 (completion of RT), and at each follow-up visit. We collected blood samples (20 mL) at pre- and post-RT for biomarker data. Blood samples were processed within 2 h of phlebotomy, and the aliquoted plasma samples were stored at − 80 °C until assay. The study was approved by Institutional Review Boards of the University of Miami and Jackson Memorial Hospital, and all patients provided written informed consent.The inclusion criteria were adult (≥ 18 years old at the time of diagnosis) female patients, newly diagnosed with breast cancer (AJCC stage 0–III) who had undergone BCS and planned to receive adjuvant RT to the whole breast with or without regional lymph nodes . Other criteria included patients belonging to one of three racial/ethnic groups  and being able to speak English or Spanish. The exclusion criteria were patients diagnosed with stage IV breast cancer and those that received partial breast irradiation and/or concurrent chemoradiation. Patients with missing pain score and/or CRP level at pre- or post-RT were excluded. To increase the validity of RT-related change in pain score, patients who reported pain due to other acute health conditions unrelated to cancer or radiation (such as shingles or fracture) were also excluded from the analysis after medical record verification.RT was delivered using standard or partially wide photon tangents using 6 and/or 10MV photons with forward planned field-in-field technique to maximize dose homogeneity. Patients received RT to the whole breast ± regional lymph nodes with conventional fractionation  or hypo-fractionation . An additional boost dose of 10–20 Gy without bolus was delivered to the tumor-bed site in most patients. Radiation oncologists contoured target volumes, including the breast and lumpectomy cavity. The treatment plan was completed on the Eclipse or Pinnacle planning systems.All women enrolled in the study filled out the National Surgical Adjuvant Breast and Bowel Project (NSABP) B-39/RTOG 0413 protocol QOL questionnaire pre- and post-RT. This questionnaire measured QOL relating to breast cosmesis, fatigue, treatment-related symptoms, and perceived convenience of care. The section pertaining to treatment-related symptoms included four pain severity items, which were extracted from the Brief Pain Inventory (BPI): “Rate your pain at its worst, at its least, on average in the past four weeks, and now (0 = no pain to 10 = pain as bad as you can imagine).” The pain score was measured as a mean of these four pain severity items; a pain score of 4–10 was used to define the presence of clinically relevant pain because pain ≥ 4 indicates a moderate to severe level of pain, as used in previous studies , 24, 25.Plasma CRP levels were measured using a high-sensitivity CRP enzyme-linked immunosorbent assay (ELISA) kit  according to the manufacturer’s protocol, as previously described . A standDemographic information, self-reported race and ethnicity, comorbidities, and smoking history/status were obtained from a self-administered baseline questionnaire at the time of enrollment. A high correlation was found between the comorbidities reported on the questionnaires and those extracted from medical records , 28. Tum2) were significantly associated with RT-related pain. Odds ratios (ORs) and 95% confidence intervals (95% CIs) were reported. In addition, we performed the receiver operating characteristics (ROC) curve analysis to evaluate whether pre-RT CRP level and/or obesity contribute to RT-related pain. A two-tailed P value < 0.05 was considered statistically significant, and all statistical analyses were performed using SAS 9.4 .We first examined the distributions and frequencies of patient-, tumor-, and treatment-related characteristics overall and by race/ethnicity using the Pearson’s chi-square test or the Fisher’s exact test. The analysis of variance (ANOVA) was used to compare CRP levels by patient characteristics. The Pearson's chi-square test or the Fisher's exact test was used to compare the frequencies of elevated CRP or pain by patient characteristics. Univariable and multivariable logistic regression analyses were used to test whether elevated pre-RT CRP and/or obesity  of the breast, diabetes mellitus, and hypertension compared to HW or NHW women. HW women were more likely to receive hormone therapy (HT) with aromatase inhibitors prior to RT compared to other racial/ethnic groups. For breast cancer surgery, 68% of patients received BCS with or without sentinel lymph node biopsy (SLNB), and 32% received BCS with axillary lymph node dissection (ALND). For systemic therapy, about half of the patients received chemotherapy, 44% initiated HT prior to RT, and 7% began HT during RT. For RT, 84% of patients received conventional fractionation with a mean total dose of 58.2 ± 4.8 (SD) Gy, including an additional boost to the lumpectomy cavity, and 16% were treated with hypo-fractionated regimens. There were no significant differences in RT treatment regimens across the three racial/ethnic groups. Overall, patients reported a significantly higher pain score at post-RT (mean ± SD = 2.8 ± 2.5) compared to pre-RT (mean ± SD = 1.7 ± 2.1). In general, AA and HW patients had significantly higher pre-RT and post-RT pain scores compared to NHW patients.The study population consisted of 366 breast cancer patients: 64% HW, 20% AA, and 16% NHW. The mean ± standard deviation (SD) of age was 56.0 ± 9.1 years. As shown in Table As shown in Table 2, HER2-positive tumor, received trastuzumab alone or taxane+trastuzumab, received ALND, or pre-RT CRP ≥ 10 mg/L, compared to their respective comparison groups. Post-RT pain was more prevalent in patients with AA or HW race/ethnicity, age < 50 years, BMI ≥ 30 kg/m2, at least 2 comorbid conditions, conventional RT fractionation, total RT dose ≥ 60 Gy, or pre-RT CRP ≥ 10 mg/L, compared to their respective counterparts. About 23% of patients had RT-related pain, and it was more frequent in patients with AA or HW race/ethnicity, at least 2 comorbid conditions, conventional RT fractionation, or RT-induced CRP change > 1 mg/L.As shown in Table In Table 2 and pre-RT CRP ≥ 10 mg/L had 4.73-fold elevated risk for RT-related pain  compared to patients with BMI <  30 kg/m2 and pre-RT CRP < 10 mg/L. All models were adjusted for age and race/ethnicity.In Table 2), the red line is for pre-RT CRP ≥ 10 mg/L, and the green line shows the combined effect of obesity and pre-RT CRP ≥ 10 mg/L. The results show some improvements of AUC in the combined BMI and pre-RT CRP model for NHW (AUC = 0.6540) and AA (AUC = 0.6524) patients  all, (B) NHW, (C) HW, and (D) AA patients and their corresponding area under the curve (AUC). The gray line represents the theoretical performance of the variable equivalent to a coin toss. The blue line is for obesity  to post-RT 30%) % to post. Pre-RT % % to poThe CRP level in normal human serum ranges from 0.2 to 10 mg/L; 90% of apparently healthy individuals have CRP levels < 3 mg/L; and only 1% have levels ≥ 10 mg/L. In our study, 13% and 13% of patients had pre-RT and post-RT CRP ≥ 10 mg/L, respectively  were collected only one point in time. The lack of repeated measures prevented us from capturing changes in health status, which may influence CRP and pain levels. Third, some variables that may influence individual patient’s pain experience and CRP level  were not available for this study, thus should be considered for future studies. Fourth, the nature of pain (nociceptive or neuropathic) may be differently influenced by inflammatory responses; however, the detailed pain quality data was not available in the current analysis. Lastly, we used patient-reported information on comorbid conditions, which might introduce reporting bias. However, many studies have reported high reliability of self-reported information when compared to medical records , 28.In summary, our current data show a significant association between elevated pre-RT CRP and RT-related pain in breast cancer patients. More importantly, we demonstrate for the first time that obese patients with pre-RT CRP ≥ 10 mg/L have a significantly increased risk of RT-related pain compared to non-obese patients with pre-RT CRP < 10 mg/L. Therefore, our current data suggest that there is an association between inflammatory responses and RT-related pain. Our results will need to be validated externally in other study populations. If validated, these results pave the way for testing anti-inflammatory agents in reducing RT-related pain.Additional file 1:Figure S1. ROC curves analysis of high pre-RT CRP and/or obesity in RT-related pain. (A) All, (B) NHW, (C) HW, and (D) AA patients and their corresponding AUC for RT-related pain. The grey solid line represents the theoretical performance of the variable equivalent to a coin toss. The blue line represents obesity (BMI≥30), the red line represents pre-RT CRP ≥10 mg/L, and the green line presents the combined effect of obesity and pre-RT CRP ≥ 10 mg/L. (PDF 57 kb)


Introduction: Opioid pain medications are commonly prescribed following orthopedic procedures, with overprescribing of these pain medications implicated as a driver of the current opioid epidemic. In an effort to reduce reliance on opioid pain medications, surgeons are relying on periarticular injections or peripheral nerve blocks. The purpose of this study was to compare numerical rating scale (NRS) pain scores and oral morphine equivalents (OMEs) in patients who underwent primary total knee arthroplasty (TKA) with a periarticular injection alone to those who underwent a collaborative approach with a periarticular injection in the posterior tissue and an adductor canal catheter for anterior knee analgesia.Methods: In this study, 236 patients underwent a primary TKA between December 2017 and April 2018. Forty patients received an adductor canal catheter and 196 underwent a periarticular injection alone.Results: We found no difference in patient demographics between the cohorts (p>0.05). The patients that underwent the collaborative approach with a periarticular injection and adductor canal catheter had lower NRS pain scores on post-operative day 0, 1, and 2 . These patients demonstrated a reduction of 43% in opioid consumption during the hospitalization (P<0.001). These patients also demonstrated improved range of motion (ROM) (96 vs. 92 degrees) on the day of discharge (P=0.013).Conclusion: This study provides strong evidence that in patients undergoing TKA, the collaborative approach with the adductor canal catheter and periarticular injection is associated with lower post-operative pain scores, fewer total OMEs per hospital day, and a greater ROM arc prior to discharge compared to patients receiving a periarticular injection alone. Opioid pain medications are commonly prescribed following orthopedic procedures, with overprescribing of these pain medications implicated as a driver of the opioid epidemic. In one study, it was found that 13% of orthopedic patients became prolonged opioid users (>90 days) following their elective surgery [In 2005, there were 523,000 total knee arthroplasty (TKA) procedures performed in the United States, with these numbers increasingly yearly -7. This In the current study, our primary aim was to compare post-operative numerical rating scale (NRS) pain scores and oral morphine equivalents (OMEs) used during hospitalization between primary TKA patients who underwent a periarticular injection alone to those who underwent a collaborative approach with the periarticular injection placed by the orthopedic surgeon and directed primarily in the posterior soft tissues and a post-operative adductor canal catheter placed by the anesthesiologist for anterior knee coverage. As a secondary aim, we compared the length of stay (LOS) at the hospital and the range of motion (ROM) arc prior to discharge between the two groups.Study subjectFollowing the approval of the Institutional Review Board (IRB), a retrospective review was conducted for all 236 patients who underwent unilateral or staged bilateral primary TKAs at our institution between December 2017 and April 2018. These patients were stratified into two cohorts; those who received a periarticular injection alone (196 patients) and those that received a periarticular injection in addition to an adductor canal pain catheter (40 patients) . There were six patients who underwent staged bilateral TKAs during the study period. For these patients, only the data from the first hospitalization was included in order to satisfy the statistical assumption of independent measurements.Periarticular injection and adductor canal pain catheterThe addition of the adductor canal catheter was surgeon dependent. One surgeon included the adductor catheter on all TKA patients. A second surgeon used the catheter only for patients deemed high risk for poor post-operative pain control as determined by their use of narcotics preoperatively. Two additional surgeons did not use the adductor catheter for their patients. All patients that did not receive an adductor canal catheter underwent a periarticular injection for analgesia.The periarticular injection consisted of 30 mg of ketorolac with weight-based ropivacaine with epinephrine . The medication was diluted in normal saline to a final volume of 120 milliliters. This was injected in the soft tissues around the knee prior to closure, focusing on the periosteum and subcutaneous tissue. For patients who only received the periarticular injection, the block was spread evenly through the anterior and posterior soft tissues. If an adductor catheter was to be placed postoperatively, a large portion of the periarticular injection was directed in the posterior soft tissues with limited anterior soft tissue infiltration for improved posterior pain control.When performed, the catheter  was placed in the adductor canal below the femoral triangle by the anesthesia team. This catheter was inserted postoperatively in the recovery room while the spinal anesthetic was in effect under ultrasound guidance using a linear probe.An infusion through the catheter contained ropivacaine 0.2% with a rate of 6 mL/hr, with an hourly patient-controlled on-demand bolus of 6 mL, and was continued for a duration of four days postoperatively. Daily patient rounds were made by the acute pain service to asses functionality of the adductor canal catheter while in the hospital. Patients were discharged home with the catheter and daily phone calls were made to the patient. The catheters were discontinued by a family member on post-operative day four with no reported difficulties or catheter complications.Data collection and outcomesInformation was collected from chart review regarding baseline patient characteristics , pre-operative visual analog pain score, opioid use prior to surgery) and operative information (side of surgery). The pre-operative pain score was calculated based on the preoperative nursing documentation. Opioid use prior to surgery was defined as the use of a narcotic medication within three months prior to the surgical procedure.Post-operative outcomes were measured and included average, minimum, and maximum NRS pain scores on post-operative days 0, 1, and 2, total OMEs per hospital day, ROM prior to discharge, and LOS. OME data was compiled from a review of the patients’ medication administration record. The NRS pain scores were compiled from nursing documentation, obtained every four hours during the hospital stay per the nursing protocol. These multiple values were then averaged for a single daily value. Although NRS pain scores were measured on post-operative days three and beyond for patients who were still hospitalized, these NRS pain scores were not evaluated as outcomes due to the small number of patients with a LOS longer than two days. The ROM data was extracted from the physical therapy note on the day of discharge. No post-operative falls occurred to any of the patients in the study including patients with adductor canal catheters.Statistical analysisThe sample median and range were used to summarize continuous variables, while number and percentage were used to summarize the categorical measures. Comparisons of baseline characteristics and operative information between patients who did and did not receive an adductor canal pain catheter were made using a Wilcoxon rank sum test or chi-square test.Comparisons of outcomes between patients who did and did not receive an adductor canal pain catheter were made using single-variable  and multivariable regression models that were appropriate for the nature of the given outcome measure. Specifically, linear regression models were used to compare average NRS pain score on post-operative days 0, 1, and 2, total OMEs per hospital day, and ROM arc prior to discharge according to use of an adductor canal pain catheter. Due to their skewed distributions, average NRS pain score on post-operative day 0 and total OMEs per hospital day were examined on the square root scale in linear regression analysis. Regression coefficients and 95% confidence intervals (CIs) were estimated and are interpreted as the difference in the mean outcome measure  between patients who did and did not receive an adductor canal pain catheter.For the ordinal outcome measures of minimum and maximum NRS pain scores on post-operative days 0, 1, and 2 as well as LOS, these were compared according to use of an adductor canal pain catheter using single-variable and multivariable proportional odds logistic regression models. Odds ratios (ORs) and 95% CIs were estimated and are interpreted as the multiplicative increase in the odds of a higher outcome measure for patients who received an adductor canal pain catheter compared to those who did not. The only exception to this was minimum NRS on day 0, which was dichotomized as 0 vs. >0 due to the high concentration of values equal to 0; correspondingly this outcome was analyzed using binary logistic regression models where ORs and 95% CIs were estimated.All multivariable linear regression, proportional odds logistic regression, and binary logistic regression models were adjusted for age, gender, BMI, pre-operative NRS pain score, narcotic use prior to surgery, and side of surgery. All statistical tests were two-sided and p-values of 0.05 or lower were considered as statistically significant. Statistical analysis was performed using R Statistical Software .For the overall cohort of 236 patients, the median age was 70 years (range: 32 - 98 years). There were 101 males (43%) and 135 females (57%). The median BMI was 30 (range: 18 - 52) and 20% of patients were taking narcotics prior to their operative procedure. Patient baseline characteristics and operative information are summarized in Table We found no statistically significant differences in demographics between patients with and without an adductor canal pain catheter . Post-operative outcomes are summarized for the overall patient series in Table Post-operative outcomes are compared between patients with and without an adductor canal pain catheter in Table In comparison to patients who did not receive an adductor canal pain catheter, those patients who did receive a pain catheter had significantly lower NRS pain scores on post-operative days 0, 1, and 2 in both single-variable analysis and multivariable analysis adjusting for age, gender, BMI, pre-operative NRS pain score, narcotic use prior to surgery, and side of surgery   in single-variable and multivariable analysis (both P<0.001) Figure .The adductor canal catheter group also demonstrated a significantly higher ROM arc (median: 96 degrees vs. 92 degrees) prior to discharge in both single-variable analysis P=0.043) and multivariable analysis (P=0.013) . There was no significant difference in post-operative LOS between the two treatment groups in single-variable analysis (P=0.59) or multivariable analysis (P=0.93).Post-operative pain can be difficult to control and the lack of relief can lead to significant distress to patients, decreased rehabilitation participation, increased the LOS in the hospital, and increased hospital costs -19. OpioThe ACB has gained recent popularity as an effective alternative to periarticular injections alone, with several studies showing equivalent pain relief ,13. The In the current investigation, we observed significantly lower average NRS pain scores in the adductor canal catheter group at post-operative day 0, day 1, and day 2 compared to the periarticular injection alone. While statistically significant, the decrease in one point on the NRS scale is likely not clinically significant. However, combined with the improved pain score was a 43% reduction in mean daily OMEs in the catheter group compared to the periarticular group. This demonstrates a substantial reduction in narcotics required to alleviate pain postoperatively.In addition to improved pain control with fewer narcotics, the adductor canal catheter group also demonstrated an improved ROM arc compared to the periarticular group on the day of discharge. Specifically, the multivariable analysis indicated that mean ROM arc was greater than 5 degrees higher for the adductor canal catheter patients. This data is supported by previous research that demonstrated improved quadriceps muscle strength, better ambulation ability, and faster functional recovery with an adductor canal catheter for TKA patients -13. SimiSeveral limitations of this study are important to note. First, this was a retrospective review and suffers from limitations inherent with that type of study design. Second, these results were from a single center with anesthesiologists trained in the placement of the adductor canal catheter and therefore the results may not be reproducible at all centers. Finally, the sample size of the adductor pain catheter group was relatively limited. However, we were able to demonstrate a number of statistically significant differences in post-operative outcomes between patients who did and did not receive an adductor canal pain catheter despite the relatively small sample size of the former group.The results of this study provide strong evidence that in patients undergoing TKA, receiving an adductor canal catheter with a periarticular injection directed primarily at the posterior soft tissue structures is associated with lower post-operative NRS pain scores, fewer total OMEs per hospital day, and a greater ROM arc prior to discharge compared to patients receiving a periarticular injection alone. This suggests that a collaborative approach between the orthopedic surgeon and anesthesiologist may provide the optimal strategy for post-operative pain control. Future studies are needed to determine if these benefits continue to be observed following hospital discharge.