Are Pain Beliefs, Cognitions, and Behaviors
Influenced by Race, Ethnicity, and Culture in
Patients with Chronic Musculoskeletal Pain: A
Systematic Review

Orhan, Ceren; Looveren, Eveline Van; Cagnie, Barbara; Mukhtar, Naziru Bashir; Lenoir, Dorine; Meeus, Mira

doi:10.36076/ppj.2018.6.541

Cited by 67 publications

(17 citation statements)

References 53 publications

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“…This has important consequences for the clinical use of this study as the results may not accurately represent the general population. In a recent review [ 52 ] investigating various dimensions of pain between ethnic groups, it was found that African-Americans tend to use more emotion-focused coping strategies compared to Caucasians. The same study also revealed some interesting differences between the US and other countries.…”

Section: Discussionmentioning

confidence: 99%

Defining pain and interference recovery trajectories after acute non-catastrophic musculoskeletal trauma through growth mixture modeling

Lee

Walton

Tremblay

et al. 2020

BMC Musculoskelet Disord

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Background Recovery trajectories support early identification of delayed recovery and can inform personalized management or phenotyping of risk profiles in patients. The objective of this study was to investigate the trajectories in pain severity and functional interference following non-catastrophic musculoskeletal (MSK) trauma in an international, mixed injury sample. Methods A prospective longitudinal cohort (n = 241) was formed from patients identified within four weeks of trauma, from attendance at emergency or urgent care centres located in London, ON, Canada, or Chicago, IL, USA. Pain interference was measured via the Brief Pain Inventory (London cohort) or the Neck Disability Index (Chicago cohort). Pain severity was captured in both cohorts using the numeric pain rating scale. Growth mixture modeling and RM repeated measures ANOVA approaches identified distinct trajectories of recovery within pain interference and pain severity data. Results For pain interference, the three trajectories were labeled accordingly: Class 1 = Rapid recovery (lowest intercept, full or near full recovery by 3 months, 32.0% of the sample); Class 2 = Delayed recovery (higher intercept, recovery by 12 months, 26.7% of the sample); Class 3 = Minimal or no recovery (higher intercept, persistently high interference scores at 12 months, 41.3% of the sample). For pain severity, the two trajectories were labeled: Class 1 = Rapid recovery (lower intercept, recovery by 3 months, 81.3% of the sample); and Class 2 = Minimal or no recovery (higher intercept, flat curve, 18.7% of the sample). The “Minimal or No Recovery” trajectory could be predicted by female sex and axial (vs. peripheral) region of trauma with 74.3% accuracy across the 3 classes for the % Interference outcome. For the Pain Severity outcome, only region (axial trauma, 81.3% accuracy) predicted the “Minimal or No Recovery” trajectory. Conclusions These results suggest that three meaningful recovery trajectories can be identified in an international, mixed-injury sample when pain interference is the outcome, and two recovery trajectories emerge when pain severity is the outcome. Females in the sample or people who suffered axial injuries (head, neck, or low back) were more likely to be classed in poor outcome trajectories. Trial registration National Institutes of Health - clinicaltrials.gov (NCT02711085; Retrospectively registered Mar 17, 2016).

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Section: Discussionmentioning

confidence: 99%

Defining pain and interference recovery trajectories after acute non-catastrophic musculoskeletal trauma through growth mixture modeling

Lee

Walton

Tremblay

et al. 2020

BMC Musculoskelet Disord

View full text Add to dashboard Cite

show abstract

“…Nevertheless, this study has some limitations. The sample of patients was restricted to a small part of the Spanish population, making necessary the validation of the method for other populations where the psychological response to chronic pain may differ [ 96 , 97 ], affecting the score of the questionnaires used in this study. Moreover, as shown by Botvinik-Nezer and colleagues, different data analysis pipelines could generate different results using the same data [ 98 ].…”

Section: Discussionmentioning

confidence: 99%

Chronic Pain Diagnosis Using Machine Learning, Questionnaires, and QST: A Sensitivity Experiment

Santana

Montoya

2020

Diagnostics

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In the last decade, machine learning has been widely used in different fields, especially because of its capacity to work with complex data. With the support of machine learning techniques, different studies have been using data-driven approaches to better understand some syndromes like mild cognitive impairment, Alzheimer’s disease, schizophrenia, and chronic pain. Chronic pain is a complex disease that can recurrently be misdiagnosed due to its comorbidities with other syndromes with which it shares symptoms. Within that context, several studies have been suggesting different machine learning algorithms to classify or predict chronic pain conditions. Those algorithms were fed with a diversity of data types, from self-report data based on questionnaires to the most advanced brain imaging techniques. In this study, we assessed the sensitivity of different algorithms and datasets classifying chronic pain syndromes. Together with this assessment, we highlighted important methodological steps that should be taken into account when an experiment using machine learning is conducted. The best results were obtained by ensemble-based algorithms and the dataset containing the greatest diversity of information, resulting in area under the receiver operating curve (AUC) values of around 0.85. In addition, the performance of the algorithms is strongly related to the hyper-parameters. Thus, a good strategy for hyper-parameter optimization should be used to extract the most from the algorithm. These findings support the notion that machine learning can be a powerful tool to better understand chronic pain conditions.

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“…This represents an important step for future studies, because analyzing biomarker concentrations in isolation may be an oversimplification of their role in persistent pain. We also recognize that race and ethnicity have been shown to be important clinical dimensions in pain 66 ; however, many of the participants chose not to report their race or ethnicity (only 19% of respondents did so). This made it impossible to stratify the data or even comment on the possible ramifications within this study, but it represents an important area of exploration for future research.…”

Section: Limitationsmentioning

confidence: 99%

Latent profile analysis of blood marker phenotypes and their relationships with clinical pain and interference reports in people with acute musculoskeletal trauma

Lee

Walton

2021

Canadian Journal of Pain

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Background : The prevalence of inadequate treatments for chronic pain has necessitated the search for biological factors that influence the transition to chronicity. Methods : Antecubital blood was drawn from those who experienced acute, noncatastrophic musculoskeletal trauma. Follow-up occurred at 1, 3, 6, and 12 months with the primary outcome being Brief Pain Inventory (BPI) Functional Interference scores. Eight markers were chosen for latent profile analysis: brain-derived neurotrophic factor (BDNF); transforming growth factor-beta 1 (TGF-β1); C-reactive protein (CRP); tumor necrosis factor-alpha (TNF-α); interleukins (ILs) 1-beta, 6, and 10; and the stress hormone cortisol. Results : Mean age of the 106 participants was 44.6 years and 58.5% were female. The final model indicated a three-class solution that could be adequately described by three of the eight markers: class 1 = low concentration of all markers (33.9% of the sample), class 2 = average concentration of all markers (47.7%), and class 3 = high concentration of BDNF and TGF-β1 (18.3%). BPI Pain Interference scores captured at both inception and 6-month follow-up were compared across the three groups. Mean scores were significantly higher in class 3 for the BPI Interference subscale at inception (27.0 [SD 16.4] vs. 35.8 [SD 17.3], P = 0.05) and at 6-month follow-up (2.2 [SD 4.8] vs. 7.3 [SD 10.7], P = 0.03) compared to those of the other two classes. Conclusions : Although recovered populations are not significantly different in BDNF and TGF-β1 levels, those who experience persisting disability are more likely to have moderate to high levels in serum.

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Are Pain Beliefs, Cognitions, and Behaviors Influenced by Race, Ethnicity, and Culture in Patients with Chronic Musculoskeletal Pain: A Systematic Review

Cited by 67 publications

References 53 publications

Defining pain and interference recovery trajectories after acute non-catastrophic musculoskeletal trauma through growth mixture modeling

Defining pain and interference recovery trajectories after acute non-catastrophic musculoskeletal trauma through growth mixture modeling

Chronic Pain Diagnosis Using Machine Learning, Questionnaires, and QST: A Sensitivity Experiment

Latent profile analysis of blood marker phenotypes and their relationships with clinical pain and interference reports in people with acute musculoskeletal trauma

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