Predicted Pain Intensity Research Articles

Prediction models for outcomes in people with low back pain receiving conservative treatment: a systematic review

To identify, critically appraise and evaluate the performance measures of the available prediction models for outcomes in people with low back pain (LBP) receiving conservative treatment. In this systematic review, literature searches were conducted in Embase, Medline, CINAHL from their inception until February/2024. Studies containing follow-up assessment (e.g., prospective cohort studies, registry-based studies) investigating prediction models of outcomes (e.g., pain intensity and disability) for people with LBP receiving conservative treatment were included. Two independent reviewers performed the study selection, the data extraction using the Checklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS), and risk of bias assessment using the Prediction model Risk of Bias Assessment (PROBAST). Findings of individual studies were reported narratively taking into account the discrimination and calibration measures of the prediction models. Seventy-five studies developing or investigating the validity of 216 models were included in this review. Most prediction models investigated people receiving physiotherapy treatment and most models included socio-demographic variables, clinical features, and self-reported measures as predictors. The discriminatory capacity of the internal validity of the 27 prediction models for pain intensity varied greatly showing a c-statistic ranging from 0.48 to 0.94. Similarly, the discriminatory capacity for 31 models for disability had the same pattern showing a c-statistic ranging from 0.48 to 0.86. The calibration measures of the internal validity of the prediction models predicting pain intensity and disability showed to be adequate. Only one of three studies testing the external validity of models to predict pain intensity and disability and reported both discrimination and calibration measures, which showed to be inadequate. The prediction models predicting the secondary outcomes (e.g., self-reported recovery, quality of life, return to work) showed varied performance measures for internal validity, and only two studies tested the external validity of models although they did not provide performance the performance measures. Several prediction models have been developed for people with LBP receiving conservative treatment, however most show inadequate discriminatory validity. A few studies externally validated the prediction models and future studies should focus on testing this before implementing in clinical practice.

Conclusions Regarding the Role of Expectations in Placebo Analgesia Studies May Depend on How We Investigate It: A Meta-Analysis, Systematic Review, and Proposal for Methodological Discussions.

Expectations are highlighted as a key component in placebo effects. However, there are different approaches to whether and how placebo studies should account for expectations, and the direct contribution has yet to be estimated in meta-analyses. Using different methodological approaches, this meta-analysis and systematic review examines the extent to which expectations contribute to pain in placebo studies. The databases PubMed, PsycINFO, Embase, and Web of Science were searched for placebo analgesia mechanism studies with numerical measures of both expectations and pain. Thirty-one studies, comprising 34 independent study populations (1566 subjects: patients and healthy participants) were included. Two meta-analyses were conducted: meta-analysis 1, using study-level data, estimated the effect of expectation interventions without taking measures of expectations into account (expectations assumed); and meta-analysis 2, using individual-level data, estimated the direct impact of participants' expectations on pain (expectations assessed). Risk of bias was assessed using the Cochrane risk-of-bias tool. Meta-analysis 1 showed a moderate effect of expectation interventions over no expectation intervention on pain intensity (Hedges g = 0.45, I2 = 54.19). Based on 10 studies providing individual-level data, meta-analysis 2 showed that expectations predicted pain intensity in placebo and control groups ( b = 0.36, SE = 0.05), although inconsistently across study methodologies. Participants' expectations contributed moderately to pain in placebo analgesia studies. However, this may largely be influenced by how we measure expectations and how their contribution is conceptualized and analyzed-both within and across studies.

Rehabilitation-Specific Predictors of Pain Intensity and Physical Activity Levels in Individuals With Acetabular Dysplasia 6 Months After Periacetabular Osteotomy.

Individuals with acetabular dysplasia often report hip joint instability, pain, and poor hip-related function. Periacetabular osteotomy (PAO) is a surgical procedure that aims to reposition the acetabulum to improve joint congruency and improve pain and function. We aimed to examine the influence of presurgery clinical measures on functional recovery following PAO and the associations among clinical outcomes after PAO. We screened 49 potential participants, 28 were enrolled, and 23 completed both study visits (pre-PAO and 6 months post-PAO). We evaluated dynamometer-measured hip and thigh strength, loading patterns during a squat and countermovement jump (CMJ), pain intensity, and device-measured physical activity (PA) levels (light, moderate-to-vigorous PA [MVPA], and daily steps). We used linear regression models to examine the influence of muscle strength (peak torque; limb symmetry index [LSI]) and loading patterns before PAO on pain intensity and PA levels in individuals 6 months following PAO. Additionally, we used Pearson correlation coefficient to examine cross-sectional associations among all variables 6 months following PAO. Lower extremity muscle strength and loading patterns during the squat and CMJ before PAO did not predict pain intensity or device-measured PA levels in individuals 6 months following PAO (p > 0.05). Six months following PAO, higher knee extensor LSI was associated with higher time spent in MVPA (r = 0.56; p = 0.016), higher hip abductor LSI was associated with both lower pain (r = 0.50; p = 0.036) and higher involved limb loading during the squat task (r = 0.59; p = 0.010). Lastly, higher hip flexor LSI was associated with higher CMJ takeoff involved limb loading (r = 0.52; p = 0.021) and higher involved hip extensor strength was associated with higher CMJ landing involved limb loading (r = 0.56; p = 0.012). Six months after PAO, higher hip and thigh muscle strength and strength symmetry were associated with lower pain, higher PA levels, and greater normalized limb loading during dynamic movement tasks.

Statistical learning shapes pain perception and prediction independently of external cues.

The placebo and nocebo effects highlight the importance of expectations in modulating pain perception, but in everyday life we don't need an external source of information to form expectations about pain. The brain can learn to predict pain in a more fundamental way, simply by experiencing fluctuating, non-random streams of noxious inputs, and extracting their temporal regularities. This process is called statistical learning. Here, we address a key open question: does statistical learning modulate pain perception? We asked 27 participants to both rate and predict pain intensity levels in sequences of fluctuating heat pain. Using a computational approach, we show that probabilistic expectations and confidence were used to weigh pain perception and prediction. As such, this study goes beyond well-established conditioning paradigms associating non-pain cues with pain outcomes, and shows that statistical learning itself shapes pain experience. This finding opens a new path of research into the brain mechanisms of pain regulation, with relevance to chronic pain where it may be dysfunctional.

Statistical learning shapes pain perception and prediction independently of external cues

The placebo and nocebo effects highlight the importance of expectations in modulating pain perception, but in everyday life we don’t need an external source of information to form expectations about pain. The brain can learn to predict pain in a more fundamental way, simply by experiencing fluctuating, non-random streams of noxious inputs, and extracting their temporal regularities. This process is called statistical learning. Here, we address a key open question: does statistical learning modulate pain perception? We asked 27 participants to both rate and predict pain intensity levels in sequences of fluctuating heat pain. Using a computational approach, we show that probabilistic expectations and confidence were used to weigh pain perception and prediction. As such, this study goes beyond well-established conditioning paradigms associating non-pain cues with pain outcomes, and shows that statistical learning itself shapes pain experience. This finding opens a new path of research into the brain mechanisms of pain regulation, with relevance to chronic pain where it may be dysfunctional.

Night time heart rate predicts next-day pain in fibromyalgia and primary back pain.

Primary chronic pain is pain that persists for over 3 months without associated measurable tissue damage. One of the most consistent findings in primary chronic pain is its association with autonomic hyperactivation. Yet whether the autonomic hyperactivation causes the pain or results from it is still unclear. It is also unclear to what extent autonomic hyperactivation is related to experienced pain intensity in different subtypes or primary chronic pain. Our first aim was to test lagged relationships between the markers of autonomic activation (heart rate) and pain intensity to determine its directionality. The main question here was whether autonomic biomarkers predict pain intensity or whether pain intensity predicts autonomic biomarkers. The second aim was to test whether this relationship is different between people with primary back pain and people with fibromyalgia. Sixty-six patients with chronic pain were observed over an average of 81 days. Sleep heart rate and heart rate variability were measured with a wearable sensor, and pain intensity was assessed from daily subjective reports. The results showed a predictive relationship between sleep heart rate and next-day pain intensity (P < 0.05), but not between daily pain intensity and next night heart rate. There was no interaction with the type of chronic pain. These findings suggest that autonomic hyperactivation, whether stress-driven or arising from other causes, precedes increases in primary chronic pain. Moreover, the present results suggest that autonomic hyperactivation is a common mechanism underlying the pain experience in fibromyalgia and chronic back pain.

Longitudinal Associations Between Pain, Risk for Posttraumatic Stress Disorder, Posttraumatic Stress Symptoms, and Pain Characteristics in Children After Unintentional Injury.

Approximately 20% of children demonstrate persistent posttraumatic stress symptoms (PTSS) after unintentional injury, with more severe pain intensity predicting concurrent and later PTSS. Examining additional pain characteristics like pain behaviors, impairment related to pain, and subjective experiences of pain might provide additional insight into the mechanisms that reinforce relationships between risk for posttraumatic stress disorder (PTSD), PTSS, and pain. During hospitalization for unintentional injury, the Screening Tool for Predictors of PTSD (STEPP) was administered and the highest pain score was collected. One month later, the Child PTSD Symptom Scale and PROMIS questionnaires assessed PTSS and pain characteristics respectively, including intensity, interference, behaviors, and quality. Correlations between PTSS and PROMIS questionnaires were significant. STEPP predicted future PTSS and all PROMIS questionnaires. The highest pain score predicted future PTSS, as well as pain interference and pain behavior, and did not predict pain intensity and pain quality. When STEPP and highest pain score were combined into a single regression, STEPP and highest pain score predicted future PTSS but only STEPP continued to predict all PROMIS questionnaires. PTSD risk significantly predicted PTSS and pain characteristics 1 month later. The highest pain score predicted future PTSS and several pain characteristics but no longer had predictive value for pain-related outcomes when combined with PTSD risk. These results indicate that risk factors for PTSD are stronger predictors than pain-related risk factors in predicting pain outcomes. Addressing PTSD risk, as well as pain intensity during hospitalization, may result in improved outcomes for children with unintentional injury.

Predicting Pain Intensity after Mandibular Third Molar Surgery using Cold Pressor Testing: A Quasi-Experimental Study.

Patients complain differently about their experiences of post-operative pain. However, clinicians are often guided by their own viewpoints despite the highly subjective nature of pain resulting in suboptimal post-operative pain management. This impacts negatively on the quality of life of patients in the immediate post-operative period. Investigating patients' pain behavior in the pre-operative period may therefore help to predict the intensity of post-operative pain, thereby assisting in identifying patients who are at risk of greater pain after third molar surgery, and allocating extra resources for pain control. This study aims to predict the intensity of pain after third molar surgery by correlating post-operative pain perception with the sensory-discriminative dimension and affective response to a cold pressor stimulus. This study is a quasi-experimental study that was conducted in the oral surgery clinic of (name of hospital in the title page). The procedure was undertaken in an isolated clinic cubicle with well-controlled room temperature and minimal distraction. Study participants were recruited by convenience sampling. Forty-three consenting participants, 20 years and above, who met eligibility criteria were subjected to pre-operative cold pressor testing. Subsequently, third molar surgery was done and post-operative pain intensity was recorded at specific intervals. Data analyses were completed using IBM SPSS version 25. The Kolmogorov-Smirnov (KS) test was used to test for normality of continuous variables. Ordinal regression was used to test for contributory value of pre-operative measurement while Spearman's rank correlation test was used to test their degree of relationships with post-operative pain P < 0.05. The median pain threshold was 20 s (Interquartile Range, IQR 12.75-32.25) and the median pain tolerance was 33 s (IQR = 23.00-54.00) from the cold pressor test. The peak median pain score in this study was reached at 3-h after the last stitch. There was a statistically significant predictive effect of both variables on post-operative pain at 3-h. Sensory-discriminative dimension and affective response to cold pressor test are significant predictors of peak post-operative pain after impacted mandibular third molar surgery.

Examination of Conditioned Pain Modulation in Myofascial TMD With Consideration of Temporal Summation

Research on myofascial temporomandibular disorder (mTMD) has often focused on potential dysfunction in endogenous pain modulation. However, studies on the specific inhibitory and facilitatory components of endogenous pain modulation using conditioned pain modulation (CPM) and temporal summation of second pain (TSSP) have shown mixed results. This study aimed to 1) examine whether women with mTMD demonstrated efficient CPM compared to controls; 2) explore the association between independent measures of CPM and TSSP in women with mTMD relative to controls; and 3) determine whether resulting modulatory profiles differentially predicted pain intensity among cases. All participants were recruited from dental clinics. Cases were women who met the research diagnostic criteria for mTMD. Controls did not have facial pain on exam and were selected to be sociodemographically similar to cases. CPM and TSSP were assessed via independent psychophysical protocols. CPM was examined in linear mixed models predicting pain thresholds adjusted for age and stratified by TSSP. Mean CPM was estimated at a 2.2 (SD = 2.8) degree increase in pain thresholds (P ≤ .001), similar in cases and controls (P = .67). CPM was less efficient in cases with enhanced TSSP (P = .031), but not in controls. Although the double-pronociceptive profile of both low CPM and high TSSP trended higher among cases than controls, it did not predict higher levels of pain intensity among cases. This study does not support deficient inhibitory endogenous pain modulation in mTMD, but results suggest that inhibitory and facilitatory pain modulation should be examined concomitantly in the study of endogenous pain modulation. PerspectiveThis manuscript presents a novel examination of inhibitory modulation by the level of facilitatory modulation in mTMD. The findings and approach may prove useful for mechanistic researchers studying endogenous pain modulation and clinical researchers seeking to jointly examine conditioned pain modulation and temporal summation in future research on chronic pain.

Does the Number of Comorbidities Predict Pain and Disability in Older Adults With Chronic Low Back Pain? A Longitudinal Study With 6- and 12-Month Follow-ups.

People who live longer often live with multimorbidity. Nevertheless, whether the presence of multimorbidity affects pain and disability in older adults with chronic low back pain (LBP) remains unclear. The aim of this study was to investigate whether multimorbidity predicts pain intensity and disability at 6- and 12-month follow-ups in older adults with chronic LBP. This was a prospective, longitudinal study with 6- and 12-month follow-ups. Participants with chronic LBP (age ≥ 60 years) were recruited and interviewed at baseline, 6 months, and 12 months. Self-reported measures included the number of comorbidities, assessed through the Self-Administered Comorbidity Questionnaire, pain intensity, assessed with the 11-point Numerical Rating Scale, and disability, assessed with the Roland-Morris Disability Questionnaire. Data were analyzed using univariate and multivariate regression models. A total of 220 participants were included. The number of comorbidities predicted pain intensity at 6-month (β= 0.31 [95% CI: 0.12 to 0.50]) and 12-month (β= 0.29 [95% CI: 0.08 to 0.50]) follow-ups. The number of comorbidities predicted disability at 6-month (β= 0.55 [95% CI: 0.20 to 0.90]) and 12-month (β= 0.40 [95% CI: 0.03 to 0.77]) follow-ups. The number of comorbidities at baseline predicted pain and disability at 6-month and 12-month follow-ups in older adults with chronic LBP. These results highlight the role of comorbidities as a predictive factor of pain and disability in patients with chronic LBP, emphasizing the need for timely and continuous interventions in older adults with multimorbidity to mitigate LBP-related pain and disability.

Investigating the Influential Factors of Mild Water-Filtered Infrared-A Whole-Body Hyperthermia for Pain Relief in Fibromyalgia: A Mixed-Methods Approach Focusing on Predictors and Patient Perspectives.

Fibromyalgia syndrome (FMS) is a chronic condition characterized by chronic widespread pain, persistent fatigue, and disrupted sleep, significantly impacting well-being. Mild water-filtered infrared-A (wIRA) whole-body hyperthermia (WBH) is emerging as a promising pain management approach to FMS. Within the present randomized controlled trial (ClinicalTrials ID: NCT05135936), FMS patients underwent six sessions of mild wIRA-WBH over 3 weeks. Their pain levels were assessed at baseline and at week 12, while body core temperature and plateau phase duration were monitored during WBH. Qualitative interviews were conducted at week 12. Results from this mixed-methods study revealed that baseline pain intensity and plateau phase duration significantly predicted pain intensity at week 12. Thematic analysis of the interviews revealed diverse patient experiences with the treatment, with all patients reporting improvements in perceived pain and overall well-being. The onset and duration of pain relief varied among individuals. Overall, the findings suggest that the duration of the plateau phase may serve as an indicator for long-term pain reduction, although individual factors may influence treatment outcomes. Despite varying experiences, a prevailing trend of positive patient evaluations emerged. This study sheds light on the potential of wIRA-WBH as a therapeutic option for alleviating pain for and enhancing the well-being of FMS patients.

Hospital admission is associated with disability and late musculoskeletal pain in individuals with long COVID.

The acute clinical repercussions of SARS-CoV-2 infection have been widely studied. However, the possible late repercussions of long COVID have not yet been well defined in the literature. To identify the presence of pain and musculoskeletal disability in patients with Long COVID and also to identify predictive factors for pain intensity in this population. In this cross-sectional and retrospective observational study individuals with Long COVID symptoms were included. It was collected musculoskeletal disability measures, data from patient-related outcome measures and variables from a COVID-19 outpatient service database. Associations and sub-group analyses were performed considering the variables pain, disability and hospitalization. Linear regression was performed to identify predictive factors for pain intensity in Long COVID patients. We evaluated 195 patients and most of them (57%) presented musculoskeletal pain in one area of the body. Pain sub-group presented worse disability indices and worse clinical course during hospitalization. Hospitalized patients presented worse disability indices comparing to non-hospitalized. Significant correlations were found between pain and days of non-invasive oxygen support (r = 0.21; p = 0.003); days in intensive care unit (r = 0.22; p = 0.002) and days in invasive mechanical ventilation (r = 0.35; p = 0.001). Hospitalized individuals showed a higher chance of presenting late musculoskeletal pain (OR = 1.42: 95%CI 1.09-2.04). Days in intensive care unit (β = 0,234: P = 0,001) and days in invasive mechanical ventilation (β = 0.764: P = 0.001) were predictors of pain intensity [F(2,192) = 18.559; R2 = 0.231; p = 0.001]. Individuals with Long COVID presented musculoskeletal pain and disability. Hospitalized patients showed a greater chance of having musculoskeletal pain. Days in intensive care unit and days in invasive mechanical ventilation were predictors of late musculoskeletal pain intensity.

How Treatment Motivation Predicts Favorable Outcomes in Interdisciplinary Multimodal Pain Treatment Among Patients with Chronic Primary Pain

As motivation for psychological treatment at intake has been shown to predict favorable outcomes after an inpatient stay, this study aimed to further characterize the different components of psychological treatment motivation that predict favorable treatment outcomes. 294 inpatients with chronic primary pain participating in an interdisciplinary multimodal pain treatment in a tertiary psychosomatic university clinic completed a battery of psychological questionnaires at intake and discharge. Treatment motivation was assessed at intake using the scales of the FPTM-23 questionnaire, while pain intensity, pain interference, anxiety, and depression were assessed both at intake and discharge. After treatment, pain intensity, pain interference, anxiety, and depression were significantly reduced. While higher levels on the FPTM-23 scale of suffering predicted smaller decreases in anxiety after treatment, higher scores on the scale of hope, i.e., lower levels of hopelessness, predicted lower levels of pain interference, anxiety, and depression after treatment. None of the scales of treatment motivation predicted pain intensity levels after treatment. Above and beyond providing symptom relief, reducing hopelessness and fostering hope regarding the treatment process and outcome might help clinicians treat patients with chronic primary pain more effectively.

The role of depression and anxiety in pain perception

Background: Depression and anxiety are associated with increased perception of pain severity. Because patients with a depressive disorder and anxiety often report pain, their sensitivity to experimental pain is controversial, probably due to differences in sensory testing methods and the lack of normal values. Material and methods: The study was conducted on 140 selected subjects. The pain test was performed using a technique, called the submaximal effort tourniquet technique. Before the start of the study, a set of psychometric inventories and tests was prepared (visual analog scale, Beck Depression Inventory, Spielberger’s State and Trait Anxiety Inventory). Results: No differences in pain perception have been found in men and women as well as in relation to age, thus gender and age cannot be a predictor in pain perception. The anxiety has no effect on pain perception. The depression can be considered a predictor of pain intensity because a change in depression levels determines a change in pain intensity perception at the 3rd minute. If the depression category was changed from a patient with no depression to one with mild depression, pain intensity at minute 3 increased by approximately one point on the visual analog scale (B=.954, CI95% .200, 1.709, p=.014). Conclusions: Depression can be considered a predictor in the evolution of pain perception. Not so much the depression score, but the increase in the severity of depression can predict the evolution of pain perception.

Development of a tool for objectively measuring somatic pain in the low back region based on a longitudinal diagnostic study conducted in Karachi, Pakistan

ObjectivesThis study aimed at devising an objective method for quantifying pain in the low back region using pressure pain threshold (PPT) and demographic factors as predictive variables.MethodsThe research was conducted...

Sensory processing sensitivity in adolescents reporting chronic pain: an exploratory study

Sensory processing sensitivity (SPS) describes a genetically influenced trait characterized by greater depth of information processing, lower sensory threshold, and ease of overstimulation. It is hypothesized that SPS plays a crucial role in the context of chronic pain.Objectives: This exploratory study examined SPS as a correlate of pain intensity and pain-related disability in a sample of adolescents reporting chronic pain. Adolescents reporting chronic pain were contacted through social media and through specialized pain clinics. Participants completed online questionnaires on their levels of SPS, pain features, emotion regulation, and quality of life. A series of analysis of variances (ANOVAs) were calculated to detect differences between 3 SPS groups (ie, high, medium, and low sensitivity) regarding emotion regulation, quality of life, and pain features. Multiple linear regressions were conducted to predict pain intensity, pain-related disability, and quality of life. In total, 103 participants completed the survey (68.9% female, Mage 17.9). Back pain was the most frequently reported pain location. Proportion of highly sensitive individuals was large (45.68%). The ANOVA revealed significant differences between sensitivity groups related to quality-of-life subscales, namely, for physical (F(2, 100) = 7.42, P < 0.001), emotional (F(2, 100) = 6.11, P < 0.001), and school functioning (F(2, 100) = 3.75, P = 0.03). High sensitivity was not predictive of pain but of health-related quality of life. Our results indicate that SPS is an important and prevalent characteristic to consider in the context of chronic pain in adolescents, specifically regarding the quality of life.

Greater functional connectivity between the ventral frontal cortex and occipital cortex in herpes zoster patients than post-herpetic neuralgia patients.

This study aimed to compare whole brain network between herpes zoster (HZ) patients and post-herpetic neuralgia (PHN) patients, as well as to investigate the associations between whole brain network changes and pain intensity and the accuracy of classifying between different types of pain. PHN patients (n = 50) and HZ patients (n = 50) and healthy controls (HCs) (n = 50) underwent resting-state functional magnetic resonance imaging (rs-fMRI). Functional connectivity and global and local graph theory metrics were calculated by using Dosenbach-160 atlas. The relationship between neuroimaging indicators and clinical scales was evaluated using correlation analysis, and receiver operating characteristic (ROC) curves evaluated the feasibility of classifying PHN and HZ patients using specific neuroimaging indicators. (1) 10 greater average connectivities were found in HZ group among the default mode, frontoparietal, cingulo-opercular, sensorimotor, occipital networks (ONs), and cerebellum (p < 0.001). (2) HZ patients exhibited higher global efficiency than those in the PHN and HCs (t = 2.178, p = 0.038). (3) Multiple linear regression analyses indicated that functional connectivity between the ventral frontal cortex in the cingulo-opercular network and the occipital gyrus in the ON influenced the visual analog score pain scores (β = 4.273; p = 0.004). The variation of functional connectivity between ventral frontal cortex in the cingulo-opercular network and occipital gyrus in the ON may be a robust neuroimaging marker of the transition from HZ to PHN patients. Whole-brain network analysis may be effective in distinguishing HZ and PHN patients and predicting pain intensity.

Morphometric similarity networks discriminate patients with lumbar disc herniation from healthy controls and predict pain intensity.

We used a recently advanced technique, morphometric similarity (MS), in a large sample of lumbar disc herniation patients with chronic pain (LDH-CP) to examine morphometric features derived from multimodal MRI data. To do so, we evenly allocated 136 LDH-CPs to exploratory and validation groups with matched healthy controls (HC), randomly chosen from the pool of 157HCs. We developed three MS-based models to discriminate LDH-CPs from HCs and to predict the pain intensity of LDH-CPs. In addition, we created analogous models using resting state functional connectivity (FC) to perform the above discrimination and prediction of pain, in addition to comparing the performance of FC- and MS-based models and investigating if an ensemble model, combining morphometric features and resting-state signals, could improve performance. We conclude that 1) MS-based models were able to discriminate LDH-CPs from HCs and the MS networks (MSN) model performed best; 2) MSN was able to predict the pain intensity of LDH-CPs; 3) FC networks constructed were able to discriminate LDH-CPs from HCs, but they could not predict pain intensity; and 4) the ensemble model neither improved discrimination nor pain prediction performance. Generally, MSN is sensitive enough to uncover brain morphology alterations associated with chronic pain and provides novel insights regarding the neuropathology of chronic pain.

Automatic Estimation of Self-Reported Pain by Trajectory Analysis in the Manifold of Fixed Rank Positive Semi-Definite Matrices.

We propose an automatic method to estimate self-reported pain based on facial landmarks extracted from videos. For each video sequence, we decompose the face into four different regions and the pain intensity is measured by modeling the dynamics of facial movement using the landmarks of these regions. A formulation based on Gram matrices is used for representing the trajectory of landmarks on the Riemannian manifold of symmetric positive semi-definite matrices of fixed rank. A curve fitting algorithm is used to smooth the trajectories and temporal alignment is performed to compute the similarity between the trajectories on the manifold. A Support Vector Regression classifier is then trained to encode extracted trajectories into pain intensity levels consistent with self-reported pain intensity measurement. Finally, a late fusion of the estimation for each region is performed to obtain the final predicted pain level. The proposed approach is evaluated on two publicly available datasets, the UNBCMcMaster Shoulder Pain Archive and the Biovid Heat Pain dataset. We compared our method to the state-of-the-art on both datasets using different testing protocols, showing the competitiveness of the proposed approach.

Clinical factors and pre-surgical depression scores predict pain intensity in cardiac surgery patients

BackgroundSevere pain is prevalent in cardiac surgery patients and can increase cardiac complications, morbidity and mortality. The objectives of the study were to assess perioperative pain intensity and to assess predictors of pain post-cardiac surgery, including clinical characteristics and depression.MethodsA total of 98 cardiac surgery patients were included in the study. Pain intensity was assessed using a Numerical Rating System. Pain was measured one day pre-operatively and recorded daily from Post-operative Day 2 to Day 7. Clinical data were recorded and depression scores were assessed using the Center for Epidemiological Study of Depression (CES-D).ResultsPain intensity increased significantly during hospitalization from pre-operative levels, surging at 2 days post-operatively. Predictors of high pain intensity were high pre-operative CES-D scores, female gender, cardiac function, smoking and high body mass index (BMI). Significantly higher pre-operative CES-D scores were found in patients with severe pain compared to patients with no pain to moderate pain (18.23 ± 1.80 vs 12.84 ± 1.22, p = 0.01 pre-operatively). Patients with severe pain (NRS 7–10) had significantly higher levels of white blood cells (WBC) compared to patients with no pain-moderate pain (NRS 0–6), (p = 0.01). However, CES-D scores were only weakly correlated maximum WBC levels perioperatively.ConclusionPain intensity significantly increased following surgery, and was associated with depressive symptoms, female sex, cardiac function, BMI, and smoking. These factors may serve as a basis for identification and intervention to help prevent the transition from acute pain to chronic pain.