| Literature DB >> 35250304 |
Don Daniel Ocay1,2, Cynthia L Larche2, Natalie Betinjane2, Alexandre Jolicoeur2, Marie Josee Beaulieu2, Neil Saran3, Jean A Ouellet3, Pablo M Ingelmo4,5,6,7, Catherine E Ferland1,2,5,6,7.
Abstract
PURPOSE: A major limitation in treatment outcomes for chronic pain is the heterogeneity of the population. Therefore, a personalized approach to the assessment and treatment of children and adolescents with chronic pain conditions is needed. The objective of the study was to subgroup pediatric patients with chronic MSK pain that will be phenotypically different from each other based on their psychosocial profile, somatosensory function, and pain modulation. PATIENTS AND METHODS: This observational cohort study recruited 302 adolescents (10-18 years) with chronic musculoskeletal pain and 80 age-matched controls. After validated self-report questionnaires on psychosocial factors were completed, quantitative sensory tests (QST) and conditioned pain modulation (CPM) were performed.Entities:
Keywords: adolescents; chronic pain; conditioned pain modulation; musculoskeletal pain; quantitative sensory testing; temporal summation of pain
Year: 2022 PMID: 35250304 PMCID: PMC8892739 DOI: 10.2147/JPR.S352607
Source DB: PubMed Journal: J Pain Res ISSN: 1178-7090 Impact factor: 3.133
Figure 1Flow chart of patient recruitment and evaluations.
Characteristics of the Patient and Control Cohorts
| Variable | Chronic MSK Pain Patients (n = 302) | Age-Matched Controls (n = 80) | Test Statistic | p-value | Cohen’s |
|---|---|---|---|---|---|
| 14.93 ± 1.95 | 14.99 ± 1.96 | 0.25† | 0.805 | ||
| Younger adolescent (10–13 years), n (%) | 87 (28.81) | 20 (25.00) | 0.29* | 0.593 | |
| Older adolescent (14–18 years), n (%) | 215 (71.19) | 60 (75.00) | 0.576 | ||
| 53.98* | |||||
| Female | 247 (81.79) | 32 (40.00) | |||
| Male | 55 (18.21) | 48 (60.00) | |||
| 0.41* | 0.521 | ||||
| Caucasian (White) | 231 (76.49) | 58 (72.50) | |||
| Person of color | 70 (23.18) | 22 (27.50) | |||
| 4.23* | |||||
| No | 212 (70.20) | 66 (82.50) | |||
| Yes | 90 (29.80) | 14 (17.50) | |||
| 1.59* | 0.207 | ||||
| No | 182 (60.26) | 55 (68.75) | |||
| Yes | 120 (39.74) | 25 (21.25) | |||
| Pain locations, x/67 | 8.78 ± 8.40 | - | |||
| Sensory descriptors, x/37% | 23.26 ± 15.36 | - | |||
| Affective descriptors, x/11% | 15.57 ± 17.41 | - | |||
| Evaluative descriptors, x/8% | 43.73 ± 24.52 | - | |||
| Temporal descriptors, x/24% | 29.01 ± 15.96 | - | |||
| Total score, mean ± SD | 2.96 ± 2.03 | - | |||
| Likely neuropathic, n (%) | 133 (44.04) | - | |||
| Total score | 15.79 ± 9.76 | - | |||
| No/minimal disability, n (%) | 119 (39.40) | - | |||
| Mild disability, n (%) | 80 (26.49) | - | |||
| Moderate disability, n (%) | 71 (23.51) | - | |||
| Severe disability, n (%) | 27 (8.94) | - | |||
| Total score, mean ± SD | 28.40 ± 9.98 | 18.55 ± 8.77 | 8.66† | 1.01 | |
| Low catastrophizers, n (%) | 30 (9.93) | 29 (36.25) | 56.145* | ||
| Moderate catastrophizers, n (%) | 84 (27.81) | 36 (45.00) | |||
| High catastrophizers, n (%) | 186 (61.59) | 15 (18.75) | |||
| Total | 52.27 ± 14.08 | 46.35 ± 11.59 | 3.87† | 0.44 | |
| Below clinical threshold, n (%) | 244 (80.79) | 74 (92.50) | 5.55* | 0.062 | |
| Borderline clinical threshold, n (%) | 10 (3.31) | 1 (1.25) | |||
| Above clinical threshold, n (%) | 45 (14.90) | 5 (6.25) | |||
| Total score, mean ± SD | 7.81 ± 3.77 | 4.88 ± 2.61 | 7.93† | 0.82 | |
| Good sleep quality, n (%) | 62 (20.53) | 35 (43.75) | 16.43* | ||
| Poor sleep quality, n (%) | 229 (75.83) | 43 (53.75) | |||
| Control area | 0.69 ± 1.22 | 0.42 ± 1.05 | 1.93† | 0.055 | |
| Tested area | 0.67 ± 1.77 | - | 0.60‡ | 0.552 | |
| Control area | −4.27 ± 1.18 | −4.53 ± 0.48 | 3.01† | 0.24 | |
| Tested area | −3.40 ± 2.07 | - | 7.74‡ | 0.52 | |
| Control area | 6.72 ± 0.98 | 7.04 ± 0.85 | 2.92† | 0.34 | |
| Tested area | 5.96 ± 1.35 | - | 17.18‡ | 0.64 | |
| Control area | 0.75 ± 0.98 | 0.60 ± 0.54 | 1.81† | 0.072 | |
| Presence of painful after sensations after 10 stimuli in the control area, n (%) | 113 (37.42) | 17 (21.25) | 7.05* | ||
| Tested area | 0.66 ± 1.03 | - | 0.62‡ | 0.534 | |
| Presence of painful after sensations after 10 stimuli in the tested area, n (%) | 126 (41.72) | 17 (21.25) | 13.19* | ||
| Control area | 5.11 ± 0.47 | 5.38 ± 0.54 | 4.01† | 0.55 | |
| Tested area | 5.11 ± 0.65 | - | 2.26‡ | <0.01 | |
| Control area | 0.43 ± 0.69 | 0.48 ± 0.66 | 0.64† | 0.523 | |
| Control area | 39.35 ± 2.73 | 39.02 ± 2.60 | 0.99† | 0.324 | |
| −22.16 ± 44.28 | −33.37 ± 33.28 | 2.48† | 0.27 | ||
| Inefficient, n (%) | 104 (34.44) | 18 (22.50) | 5.56* | 0.062 | |
| Suboptimal, n (%) | 60 (19.87) | 16 (20.00) | |||
| Optimal, n (%) | 130 (43.05) | 46 (57.50) | |||
| 0.02 ± 2.27 | 0.33 ± 2.05 | 1.16† | 0.25 | ||
| Absence, n (%) | 249 (82.45) | 66 (82.50) | 0.09* | 0.761 | |
| Presence, n (%) | 45 (14.90) | 14 (17.50) |
Notes: Percentages do not always add up to 100% due to missing data for some demographic variables. aDue to low frequency of some racial groups, races typically identified by Statistics Canada as a visible minority group (American Indian or Alaska Native, Asian, Black or African American, Latin American, Arab, and Mixed Race) were collapsed into a single category. *Test statistic for chi-square test. †Test statistic for Student’s t-test between patients and controls. ‡Test statistic for Student’s t-test between control area and tested pain area. Significant p-values < 0.05 are bolded. Cohen’s d values are displayed for significant p-values for the Student’s t-test (0.2 – small; 0.5 – medium, 0.8 – large).
Abbreviations: MSK, musculoskeletal; log, log-transformed data; MDT, mechanical detection threshold; DMA, dynamic mechanical allodynia; VDT, vibration detection threshold; WUR, wind-up ratio; PPT, pressure pain threshold; WDT, warm detection threshold; HPT, heat pain threshold; CPM, conditioned pain modulation; TSP, temporal summation of pain.
Figure 2Inhibitory and facilitatory pain modulations responses in adolescents with chronic musculoskeletal pain and age-matched controls. The distribution of conditioned pain modulation in (A) patients and (a) age-matched controls show a spectrum of individual responses. Bar = individual participants. A CPM efficiency between −100% and −30% was considered as optimal, between −30% and −10% suboptimal and between −10% and +100% inefficient. The distribution of temporal summation of pain during the test stimulus before the conditioning stimulus in (B) patients and (b) age-matched controls also show a spectrum of individual responses. Bar = individual participants. An increase in pain intensity was determined minimum clinically significant if the change was equal or larger than 20/100 during the last 60 seconds of the first test stimulus (ie, presence of temporal summation of pain).
Figure 3Psychosocial profiles in adolescents with chronic musculoskeletal pain. (A) Individual patient questionnaire scores were transformed and presented as z-scores. Higher z-scores represent higher scores for the questionnaire completed. Differences are significant if p<0.05 Significant difference between #the adaptive pain and high pain dysfunctional cluster, ‡the adaptive pain and high somatic symptoms cluster or †the high pain dysfunctional and high somatic symptoms cluster. Data points = mean. (B) The pain catastrophizing score is represented by psychosocial cluster and compared with age-matched controls. Bars = mean ± SEM. (C) The Revised Child Anxiety and Depression Scale total T-score is represented by psychosocial cluster and compared with age-matched controls. Bars = mean ± SEM. (D) The Pittsburgh Sleep Quality Index global score is represented by psychosocial cluster and compared with age-matched controls. Bars = mean ± SEM. *p<0.05, **p<0.01, ****p<0.0001. PCS-C, pain Catastrophizing Scale – Child version; DN4, Douleur Neuropathique 4 questionnaire; FDI, Functional disability inventory; Sensory, sensory descriptors; Affective, affective descriptors; Evaluative, evaluative descriptors; Temporal, temporal descriptors; RCADS, Revised Child Anxiety and Depression Scale.
Figure 4Quantitative sensory testing profiles in adolescents with chronic musculoskeletal pain. Individual patient pain area thresholds were converted into z-scores calculated with reference to within-cohort control measures at the control area. Individual patient control area thresholds were converted into z-scores calculated with reference to between-cohort control measures at the control area. z-Scores for dynamic mechanical allodynia to brush and for the presence of painful after-sensations at the end of the 60-second period after 10 pinprick stimuli were were calculated with reference to the pain intensity reported by the patients using the numerical rating scale (NRS 0–10). An average z-score for all QST parameters for the control and affected area was then calculated for each patient. The z-score plot for each individual patient was grouped according to the closest matching adult mechanism-related profile: mechanical hyperalgesia, sensory loss, thermal hyperalgesia or normative QST. Gain of function (hyperalgesia) is indicated as a positive z-score and a loss of function (sensory loss) as a negative score. Data points = mean.
Figure 5Pain modulation profiles in adolescents with chronic musculoskeletal pain and age-matched controls. Mean pain intensity during the tonic thermal heat stimulations of the conditioned pain modulation assessment. Each individual patient was grouped according to their inhibitory and facilitatory pain modulation responses: dysfunctional central processing (suboptimal or inefficient CPM and presence of temporal summation of pain), dysfunctional inhibition (suboptimal or inefficient CPM and absence of temporal summation of pain), facilitation (optimal CPM and presence of temporal summation of pain) and functional central processing (optimal CPM and absence of temporal summation of pain). A CPM efficiency between −100% and −30% was considered as optimal, between −30% and −10% suboptimal and between −10% and +100% inefficient. Presence of temporal summation of pain was defined as an increase in pain intensity equal or larger than 20/100 (using the CoVAS) during the last 60 seconds of the first test stimulus.
Figure 6Associations between psychosocial profiles and somatosensory profiles and pain modulatory profiles. (A) The proportion of distinct somatosensory profiles is shown divided by the identified psychosocial profiles. (B) The proportion of distinct pain modulatory profiles is shown divided by the identified psychosocial profiles. (C) The proportion of distinct somatosensory profiles is shown divided by the identified pain modulatory profiles.
Figure 7Comprehensive patient pain assessment and rational predicted treatment efficacy. Pain assessment through self-reported questionnaires, quantitative sensory testing and conditioned pain modulation identifies distinct psychosocial, somatosensory, and pain modulatory profiles. Predictions for differential efficacy of treatment approaches across profiles are depicted. + represents beneficial; ++ represents very beneficial.