The clinical utility of pain classification in non-specific arm pain

Inter- and intra-rater-reliability of a clinical framework for spine-related neck-arm pain

OBJECTIVE

A mechanism-based clinical framework for spine-related pain differentiates (i) somatic referred pain, ii) heightened nerve mechanosensitivity, iii) radicular pain, iv) radiculopathy and mixed-pain. This study aimed to determine the reliability of proposed framework.

METHOD

Fifty-one people with unilateral spine-related neck-arm pain were assessed and categorized by examiner-1. The classifications were compared to those made by two other examiners, based on written documentation of examiner-1. Cohens kappa was calculated between examiner-pairs; Fleiss Kappa among all examiners to assess agreement in classifying subgroups and entire framework.

RESULT

Inter-rater-reliability showed moderate to almost perfect reliability (somatic: no variation, mechanosensitivity: 0.96 (95% CI 0.87-1.0) to 1.0 (95% CI: 1.0-1.0), radicular pain: 0.46 (95% CI: 0.19-0.69) to 0.62 (95% CI: 0.42-0.81), radiculopathy: 0.65 (95% CI: 0.43-0.84) to 0.80 (95% CI: 0.63-0.96) mixed-pain: 0.54 (95% CI: 0.21-0.81) to 0.75 (95% CI: 0.48-0.94). There was almost perfect to moderate reliability among all examiners (somatic: no variation, mechanosensitivity: 0.97 (95% CI: 0.82-1.0), radicular pain: 0.56 (95% CI: 0.40-0.71), radiculopathy: 0.74 (95% CI: 0.58-0.90), mixed-pain: 0.63 (95% CI: 0.47-0.79), entire framework: 0.64 (95% CI: 0.57-0.71)). Intra-rater-reliability showed substantial to almost perfect reliability (somatic: no variation, mechanosensitivity: 0.96 (95% CI: 0.87-1.0), radicular pain: 0.76 (95% CI: 0.57-0.92), radiculopathy: 0.84 (95% CI: 0.67-0.96), mixed-pain: 0.83 (95% CI: 0.60-1.0), entire framework: 0.80 (95% CI: 0.61-0.92).

CONCLUSION

Moderate to almost perfect reliability in subgrouping people with spine-related neck-arm pain and substantial reliability for entire framework support this classification's reliability.

Classification algorithm for the International Classification of Diseases-11 chronic pain classification: development and results from a preliminary pilot evaluation

ABSTRACT

The International Classification of Diseases-11 (ICD-11) chronic pain classification includes about 100 chronic pain diagnoses on different diagnostic levels. Each of these diagnoses requires specific operationalized diagnostic criteria to be present. The classification comprises more than 200 diagnostic criteria. The aim of the Classification Algorithm for Chronic Pain in ICD-11 (CAL-CP) is to facilitate the use of the classification by guiding users through these diagnostic criteria. The diagnostic criteria were ordered hierarchically and visualized in accordance with the standards defined by the Society for Medical Decision Making Committee on Standardization of Clinical Algorithms. The resulting linear decision tree underwent several rounds of iterative checks and feedback by its developers, as well as other pain experts. A preliminary pilot evaluation was conducted in the context of an ecological implementation field study of the classification itself. The resulting algorithm consists of a linear decision tree, an introduction form, and an appendix. The initial decision trunk can be used as a standalone algorithm in primary care. Each diagnostic criterion is represented in a decision box. The user needs to decide for each criterion whether it is present or not, and then follow the respective yes or no arrows to arrive at the corresponding ICD-11 diagnosis. The results of the pilot evaluation showed good clinical utility of the algorithm. The CAL-CP can contribute to reliable diagnoses by structuring a way through the classification and by increasing adherence to the criteria. Future studies need to evaluate its utility further and analyze its impact on the accuracy of the assigned diagnoses.

Methods to discriminate between mechanism-based categories of pain experienced in the musculoskeletal system: a systematic review

ABSTRACT

Mechanism-based classification of pain has been advocated widely to aid tailoring of interventions for individuals experiencing persistent musculoskeletal pain. Three pain mechanism categories (PMCs) are defined by the International Association for the Study of Pain: nociceptive, neuropathic, and nociplastic pain. Discrimination between them remains challenging. This study aimed to build on a framework developed to converge the diverse literature of PMCs to systematically review methods purported to discriminate between them; synthesise and thematically analyse these methods to identify the convergence and divergence of opinion; and report validation, psychometric properties, and strengths/weaknesses of these methods. The search strategy identified articles discussing methods to discriminate between mechanism-based categories of pain experienced in the musculoskeletal system. Studies that assessed the validity of methods to discriminate between categories were assessed for quality. Extraction and thematic analysis were undertaken on 184 articles. Data synthesis identified 200 methods in 5 themes: clinical examination, quantitative sensory testing, imaging, diagnostic and laboratory testing, and pain-type questionnaires. Few methods have been validated for discrimination between PMCs. There was general convergence but some disagreement regarding findings that discriminate between PMCs. A combination of features and methods, rather than a single method, was generally recommended to discriminate between PMCs. Two major limitations were identified: an overlap of findings of methods between categories due to mixed presentations and many methods considered discrimination between 2 PMCs but not others. The results of this review provide a foundation to refine methods to differentiate mechanisms for musculoskeletal pain.

Application and utility of a clinical framework for spinally referred neck-arm pain: A cross-sectional and longitudinal study protocol

BACKGROUND

The clinical presentation of neck-arm pain is heterogeneous with varying underlying pain types (nociceptive/neuropathic/mixed) and pain mechanisms (peripheral/central sensitization). A mechanism-based clinical framework for spinally referred pain has been proposed, which classifies into (1) somatic pain, (2) neural mechanosensitivity, (3) radicular pain, (4) radiculopathy and mixed pain presentations. This study aims to (i) investigate the application of the clinical framework in patients with neck-arm pain, (ii) determine their somatosensory, clinical and psychosocial profile and (iii) observe their clinical course over time.

METHOD

We describe a study protocol. Patients with unilateral neck-arm pain (n = 180) will undergo a clinical examination, after which they will be classified into subgroups according to the proposed clinical framework. Standardized quantitative sensory testing (QST) measurements will be taken in their main pain area and contralateral side. Participants will have to complete questionnaires to assess function (Neck Disability Index), psychosocial factors (Tampa Scale of Kinesiophobia, Pain Catastrophizing Scale, Depression, anxiety and stress scale), neuropathic pain (Douleur Neuropathique 4 Questions, PainDETECT Questionnaire) and central sensitization features (Central Sensitization Inventory). Follow-ups at three, six and 12 months include the baseline questionnaires. The differences of QST data and questionnaire outcomes between and within groups will be analyzed using (M)AN(C)OVA and/or regression models. Repeated measurement analysis of variance or a linear mixed model will be used to calculate the differences between three, six, and 12 months outcomes. Multiple regression models will be used to analyze potential predictors for the clinical course.

CONCLUSION

The rationale for this study is to assess the usability and utility of the proposed clinical framework as well as to identify possible differing somatosensory and psychosocial phenotypes between the subgroups. This could increase our knowledge of the underlying pain mechanisms. The longitudinal analysis may help to assess possible predictors for pain persistency.