Classifying chronic pain using multidimensional pain-agnostic symptom assessments and clustering analysis

Chronic pain conditions present in various forms, yet all feature symptomatic impairments in physical, mental, and social domains. Rather than assessing symptoms as manifestations of illness, we used them to develop a chronic pain classification system. A cohort of real-world treatment-seeking patients completed a multidimensional patient-reported registry as part of a routine initial evaluation in a multidisciplinary academic pain clinic. We applied hierarchical clustering on a training subset of 11,448 patients using nine pain-agnostic symptoms. We then validated a three-cluster solution reflecting a graded scale of severity across all symptoms and eight independent pain-specific measures in additional subsets of 3817 and 1273 patients. Negative affect–related factors were key determinants of cluster assignment. The smallest subset included follow-up assessments that were predicted by baseline cluster assignment. Findings provide a cost-effective classification system that promises to improve clinical care and alleviate suffering by providing putative markers for personalized diagnosis and prognosis.

More Insight on the Role of Personality Traits and Sensitivity to Experimental Pain

Purpose

The present study aimed to assess the influence of personality traits on the variability of sensitivity to pain in two distinct groups of healthy subjects with low versus high sensitivity to pain (LSP vs HSP, respectively).

Methods

Healthy subjects (n=156) were allocated to two groups according to their tolerability to cold stimulation (cold pressor test, CPT, 1°C). Group LSP (n=76) reached the cut-off time of 180±0 sec, and a size matched group of HSP (n=80) tolerated the CPT for an average of 10.5±3.4 sec only. Subjects from both groups completed the self-reported pain sensitivity questionnaire (PSQ), the Pain Catastrophizing Scale (PCS), and the Neuroticism Extraversion Openness - Five Factor Inventory (NEO-FFI).

Results

In comparison to the LSP group, HSP individuals had higher scores of PSQ (p<0.001), catastrophizing (p=0.001), and extraversion (p=0.01). By adjusting for age and gender, mediation analyses revealed that catastrophizing mediated the relationship between neuroticism and pain sensitivity, both in the allocation of subjects to a certain group of sensitivity to pain (LSP or HSP, B=0.02 95% CI: 0.006–0.040) and in the PSQ score (B=0.01 95% CI: 0.001–0.023).

Conclusion

These results, which were demonstrated by two different prisms (CPT and PSQ), point to the potential of the five-factor inventory and pain catastrophizing scale as tools for identifying specific personality traits associated with a high sensitivity to pain.

Multi-sensory Responsiveness and Personality Traits Predict Daily Pain Sensitivity

Background: A continuous effort has been devoted to identifying factors that contribute to individual differences in pain perception. Amongst the personality traits, Neuroticism is assumed to be the most significant moderator of experimental and clinical pain. Multi-sensory responsiveness to daily sensations has been shown to be associated with pain perception. Yet, neither the relationship between personality traits and multi-sensory responsiveness nor the impact of both these factors to pain perception have been examined. Thus, this study aims to explore the contribution of both multi-sensory responsiveness and personality traits to pain perception in a daily context.

Methods: A community-based sample of 204 adults completed the Sensory Responsiveness Questionnaire-Intensity Scale (SRQ-IS); the Big Five Inventory (BFI); and the Pain Sensitivity Questionnaire (PSQ).

Results: The partial eta-square demonstrated that the SRQ-IS Aversive sub-scale score had the strongest relationship with the PSQ-Total score, accounting for 9% of the variation. The regression coefficient relating PSQ-Total score with SRQ-IS Aversive, and BFI sub-scales of Extraversion, Neuroticism and Openness-to-Experience scores was found to be r = 0.39 (p < 0.0001), accounting for 16% of the variance, and yielding a large effect size.

Discussion: To the best of our knowledge this is the first study to report on the interplay between aversive responsiveness to daily sensations and personality traits of Neuroticism, Openness-to-Experience, and Extraversion as contributing factors to daily pain sensitivity, amongst which aversive responsiveness was found as the major contributing factor. This study may broaden the understanding of the pain experience variability, both in practice and in experimental research.

The relationship between sensitivity to pain and conditioned pain modulation in healthy people

BACKGROUND

The relationship between sensitivity to pain and conditioned pain modulation (CPM) - a paradigm reflecting the activity of the endogenous descending analgesic system - is still unclear. This study aimed at investigating CPM magnitude in two distinct subgroups of healthy subjects, presenting low vs. high sensitivity to pain (LSP vs. HSP, respectively), by employing two different thermal paradigms of CPM.

METHOD

Ninety-five healthy subjects (out of 293 tested) were identified as LSP (n = 48) or HSP (n = 47) according to their tolerance time to noxious cold stimulation (Cold Pressor Test, 1 °C). All subjects were exposed to two different paradigms of CPM: 1) Fixed temperature 'test-pain' (TP) where phasic, fixed painful heat stimuli of 47 °C were administered before and during a prolonged 'conditioning stimulus' (cold water at 12 °C for 30 s); and 2) Individually based 'pain-60' where TP was determined as the temperature that induced pain at a magnitude of 60 on a 0-100 rating scale (with the same conditioning stimulus).

RESULT

Using both thermal paradigms, LSP subjects showed decreased CPM magnitudes in comparison to HSP (p < 0.0001 in both paradigms). Within each group, no differences in the magnitudes of CPM were found between the two paradigms.

CONCLUSION

These findings show that regardless of the thermal CPM paradigm employed, healthy individuals exhibiting low sensitivity to pain have a low pain inhibition profile and vice-versa. It is suggested that in healthy subjects, pain sensitivity predisposes the magnitude of CPM and not the other way around.

Identification of clusters of individuals relevant to temporomandibular disorders and other chronic pain conditions: the OPPERA study

The classification of most chronic pain disorders gives emphasis to anatomical location of the pain to distinguish one disorder from the other (eg, back pain vs temporomandibular disorder [TMD]) or to define subtypes (eg, TMD myalgia vs arthralgia). However, anatomical criteria overlook etiology, potentially hampering treatment decisions. This study identified clusters of individuals using a comprehensive array of biopsychosocial measures. Data were collected from a case-control study of 1031 chronic TMD cases and 3247 TMD-free controls. Three subgroups were identified using supervised cluster analysis (referred to as the adaptive, pain-sensitive, and global symptoms clusters). Compared with the adaptive cluster, participants in the pain-sensitive cluster showed heightened sensitivity to experimental pain, and participants in the global symptoms cluster showed both greater pain sensitivity and greater psychological distress. Cluster membership was strongly associated with chronic TMD: 91.5% of TMD cases belonged to the pain-sensitive and global symptoms clusters, whereas 41.2% of controls belonged to the adaptive cluster. Temporomandibular disorder cases in the pain-sensitive and global symptoms clusters also showed greater pain intensity, jaw functional limitation, and more comorbid pain conditions. Similar results were obtained when the same methodology was applied to a smaller case-control study consisting of 199 chronic TMD cases and 201 TMD-free controls. During a median 3-year follow-up period of TMD-free individuals, participants in the global symptoms cluster had greater risk of developing first-onset TMD (hazard ratio = 2.8) compared with participants in the other 2 clusters. Cross-cohort predictive modeling was used to demonstrate the reliability of the clusters.