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Distinct patterns of variation in the distribution of knee pain.

ABSTRACT: The patient's expression of pain using digital-body maps expands analytic opportunities for exploring the spatial variation of bodily pain. A common knee pain condition in adolescents and adults is patellofemoral pain (PFP) and recently PFP was shown to be characterized by a heterogeneous distribution of pain. Whether there are important patterns in these distributions remains unclear. This pioneering study assesses the spatial variation of pain using principal component analysis and a clustering approach. Detailed digital-body maps of knee pain were drawn by 299 PFP patients of mixed sex, age, and pain severity. Three pain distribution patterns emerged resembling an Anchor, Hook, and an Ovate shape on and around the patella. The variations in pain distribution were independent of sex, age, and pain intensity. Bilateral pain associated with a longer duration of pain and the majority characterized by the Hook and Ovate pain distributions. Bilateral and/or symmetrical pain between the left and right knees may represent symptoms associated with longstanding PFP. The distinct patterns of pain location and area suggest specific underlying structures cannot be ruled out as important drivers, although central neuronal mechanisms possibly exemplified by the symmetrical representation of pain may play a role in individuals with longstanding symptoms.

SUBMITTER: Boudreau SA

PROVIDER: S-EPMC6224396 | biostudies-other | 2018 Nov

REPOSITORIES: biostudies-other

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Distinct patterns of variation in the distribution of knee pain.

Boudreau Shellie A SA Royo Albert Cid AC Matthews Mark M Graven-Nielsen Thomas T Kamavuako Ernest N EN Slabaugh Greg G Thorborg Kristian K Vicenzino Bill B Rathleff Michael Skovdal MS

Scientific reports 20181108 1

The patient's expression of pain using digital-body maps expands analytic opportunities for exploring the spatial variation of bodily pain. A common knee pain condition in adolescents and adults is patellofemoral pain (PFP) and recently PFP was shown to be characterized by a heterogeneous distribution of pain. Whether there are important patterns in these distributions remains unclear. This pioneering study assesses the spatial variation of pain using principal component analysis and a clusterin ...[more]

PMID: 30410031

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Project description:BackgroundIn clinical practice, the bodily distribution of chronic pain is often used in conjunction with other signs and symptoms to support a diagnosis or treatment plan. For example, the diagnosis of fibromyalgia involves tallying the areas of pain that a patient reports using a drawn body map. It remains unclear whether patterns of pain distribution independently inform aspects of the pain experience and influence patient outcomes. The objective of the current study was to evaluate the clinical relevance of patterns of pain distribution using an algorithmic approach agnostic to diagnosis or patient-reported facets of the pain experience.Methods and findingsA large cohort of patients (N = 21,658) completed pain body maps and a multi-dimensional pain assessment. Using hierarchical clustering of patients by body map selection alone, nine distinct subgroups emerged with different patterns of body region selection. Clinician review of cluster body maps recapitulated some clinically-relevant patterns of pain distribution, such as low back pain with radiation below the knee and widespread pain, as well as some unique patterns. Demographic and medical characteristics, pain intensity, pain impact, and neuropathic pain quality all varied significantly across cluster subgroups. Multivariate modeling demonstrated that cluster membership independently predicted pain intensity and neuropathic pain quality. In a subset of patients who completed 3-month follow-up questionnaires (N = 7,138), cluster membership independently predicted the likelihood of improvement in pain, physical function, and a positive overall impression of change related to multidisciplinary pain care.ConclusionsThis study reports a novel method of grouping patients by pain distribution using an algorithmic approach. Pain distribution subgroup was significantly associated with differences in pain intensity, impact, and clinically relevant outcomes. In the future, algorithmic clustering by pain distribution may be an important facet in chronic pain biosignatures developed for the personalization of pain management.

Project description:PurposeThe diagnostic process in patients after painful total knee arthroplasty (TKA) is challenging. The more clinical and radiological information about a patient with pain after TKA is included in the assessment, the more reliable and sustainable the advice regarding TKA revision can be. The primary aim was to investigate the position of TKA components and evaluate bone tracer uptake (BTU) using pre-revision SPECT/CT and correlate these findings with previously published pain patterns in painful patients after TKA.MethodsA prospectively collected cohort of 83 painful primary TKA patients was retrospectively evaluated. All patients followed a standardized diagnostic algorithm including 99m-Tc-HDP-SPECT/CT, which led to a diagnosis indicating revision surgery. Pain character, location, dynamics and radiation were systematically assessed as well as TKA component position in 3D-CT. BTU was anatomically localized and quantified using a validated localization scheme. Component positioning and BTU were correlated with pain characteristics using non-parametric Spearman correlations (p < 0.05).ResultsBased on Spearman's rho, significant correlations were found between pain and patients characteristics and SPECT/CT findings resulting in nine specific patterns. The most outstanding ones include: Pattern 1: More flexion in the femoral component correlated with tender/splitting pain and patella-related pathologies. Pattern 3: More varus in the femoral component correlated with dull/heavy and tingling/stinging pain during descending stairs, unloading and long sitting in patients with high BMI and unresurfaced patella. Pattern 6: More posterior slope in the tibial component correlated with constant pain.ConclusionThe results of this study help to place component positioning in the overall context of the "painful knee arthroplasty" including specific pain patterns. The findings further differentiate the clinical picture of a painful TKA. Knowing these patterns enables a prediction of the cause of the pain to be made as early as possible in the diagnostic process before the state of pain becomes chronic.Level of evidenceLevel III.

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Distinct patterns of variation in the distribution of knee pain.

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Distinct patterns of variation in the distribution of knee pain.

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