Novel approach towards musculoskeletal phenotypes

Meisingset, Ingebrigt; Vasseljen, Ottar; Vøllestad, Nina Køpke; Robinson, Hilde Stendal; Woodhouse, Astrid; Engebretsen, Kaia Beck; Glette, Mari; Øverås, Cecilie K; Nordstoga, Anne Lovise; Evensen, Kari Anne I.; Skarpsno, Eivind Schjelderup

doi:10.1002/ejp.1541

Cited by 42 publications

(57 citation statements)

References 36 publications

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“…In addition, patients with NP and LBP have several similarities in psychosocial prognostic factors and comorbidities, clinical guidelines for best practice, and lack of specific pathoanatomic causality [11][12][13][14]. While the models for clinical management of musculoskeletal complaints to date have mainly been condition specific, there have recently been calls for management based on characteristics within the biopsychosocial model regardless of pain condition [14][15][16][17]. Studies have also demonstrated that patients with trajectories of NP display similar on most health-related factors as for LBP [5,6].…”

Section: Introductionmentioning

confidence: 99%

Neck pain patterns and subgrouping based on weekly SMS-derived trajectories

Irgens

Kongsted

Myhrvold

et al. 2020

BMC Musculoskelet Disord

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Background Neck and low back pain represent dynamic conditions that change over time, often with an initial improvement after the onset of a new episode, followed by flare-ups or variations in intensity. Pain trajectories were previously defined based on longitudinal studies of temporal patterns and pain intensity of individuals with low back pain. In this study, we aimed to 1) investigate if the defined patterns and subgroups for low back pain were applicable to neck pain patients in chiropractic practice, 2) explore the robustness of the defined patterns, and 3) investigate if patients within the various patterns differ concerning characteristics and clinical findings. Methods Prospective cohort study including 1208 neck pain patients from chiropractic practice. Patients responded to weekly SMS-questions about pain intensity and frequency over 43 weeks. We categorized individual responses into four main patterns based on number of days with pain and variations in pain intensity, and subdivided each into four subgroups based on pain intensity, resulting in 16 trajectory subgroups. We compared baseline characteristics and clinical findings between patterns and between Persistent fluctuating and Episodic subgroups. Results All but two patients could be classified into one of the 16 subgroups, with 94% in the Persistent fluctuating or Episodic patterns. In the largest subgroup, “Mild Persistent fluctuating” (25%), mean (SD) pain intensity was 3.4 (0.6) and mean days with pain 130. Patients grouped as “Moderate Episodic” (24%) reported a mean pain intensity of 2.7 (0.6) and 39 days with pain. Eight of the 16 subgroups each contained less than 1% of the cohort. Patients in the Persistent fluctuating pattern scored higher than the other patterns in terms of reduced function and psychosocial factors. Conclusions The same subgroups seem to fit neck and low back pain patients, with pain that typically persists and varies in intensity or is episodic. Patients in a Persistent fluctuating pattern are more bothered by their pain than those in other patterns. The low back pain definitions can be used on patients with neck pain, but with the majority of patients classified into 8 subgroups, there seems to be a redundancy in the original model.

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Section: Introductionmentioning

confidence: 99%

Neck pain patterns and subgrouping based on weekly SMS-derived trajectories

Irgens

Kongsted

Myhrvold

et al. 2020

BMC Musculoskelet Disord

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“…We recently identified five MSK phenotypes across common non-specific MSK complaints based on generic prognostic factors in MSK pain conditions [12]. The phenotypes were identified by latent class analysis (LCA) and based on 11 prognostic factors covering all aspects of the biopsychosocial domain.…”

Section: Introductionmentioning

confidence: 99%

Recovery trajectories in common musculoskeletal complaints by diagnosis contra prognostic phenotypes

Aasdahl

Granviken

Meisingset

et al. 2021

BMC Musculoskelet Disord

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Background There are large variations in symptoms and prognostic factors among patients sharing the same musculoskeletal (MSK) diagnosis, making traditional diagnostic labelling not very helpful in informing treatment or prognosis. Recently, we identified five MSK phenotypes across common MSK pain locations through latent class analysis (LCA). The aim of this study was to explore the one-year recovery trajectories for pain and functional limitations in the phenotypes and describe these in relation to the course of traditional diagnostic MSK groups. Methods We conducted a longitudinal observational study of 147 patients with neck, back, shoulder or complex pain in primary health care physiotherapy. Data on pain intensity and function were collected at baseline (week 0) and 1, 2, 3, 4, 6, 8, 12, 26 and 52 weeks of follow up using web-based questionnaires and mobile text messages. Recovery trajectories were described separately for the traditional diagnostic MSK groups based on pain location and the same patients categorized in phenotype groups based on prognostic factors shared among the MSK diagnostic groups. Results There was a general improvement in function throughout the year of follow-up for the MSK groups, while there was a more modest decrease for pain intensity. The MSK diagnoses were dispersed across all five phenotypes, where the phenotypes showed clearly different trajectories for recovery and course of symptoms over 12 months follow-up. This variation was not captured by the single trajectory for site specific MSK diagnoses. Conclusion Prognostic subgrouping revealed more diverse patterns in pain and function recovery over 1 year than observed in the same patients classified by traditional diagnostic groups and may better reflect the diversity in recovery of common MSK disorders.

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“…These endeavors have frequently been based on individual characteristics such as symptoms, pain sites, or other prognostic factors, and usually within defined patient groups. Previous studies have found subgroups that differ in severity [55][56][57], or subgroups that are qualitatively distinct, for example in symptoms or personal factors [22,[58][59][60]. For instance, studies using prognostic factors have found subgroups characterized by low risk, mental health issues, physical limitations and pain, and workplace related concerns in patients with musculoskeletal disorders [61,62].…”

Section: Discussionmentioning

confidence: 99%

“…Identifying subgroups that can be used to stratify care is challenging and has been a focus of research in some fields for many years [19], mainly in patients with musculoskeletal disorders [20][21][22][23]. Such stratification approaches have shown effective in treatment of patients with low back pain [24].…”

Section: Introductionmentioning

confidence: 99%

Subgroups of Long-Term Sick-Listed Based on Prognostic Return to Work Factors Across Diagnoses: A Cross-Sectional Latent Class Analysis

et al. 2020

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Comorbidity is common among long-term sick-listed and many prognostic factors for return to work (RTW) are shared across diagnoses. RTW interventions have small effects, possibly due to being averaged across heterogeneous samples. Identifying subgroups based on prognostic RTW factors independent of diagnoses might help stratify interventions. The aim of this study was to identify and describe subgroups of long-term sick-listed workers, independent of diagnoses, based on prognostic factors for RTW. Latent class analysis of 532 workers sick-listed for eight weeks was used to identify subgroups based on seven prognostic RTW factors (self-reported health, anxiety and depressive symptoms, pain, self-efficacy, work ability, RTW expectations) and four covariates (age, gender, education, physical work). Four classes were identified: Class 1 (45% of participants) was characterized by favorable scores on the prognostic factors; Class 2 (22%) by high anxiety and depressive symptoms, younger age and higher education; Class 3 (16%) by overall poor scores including high pain levels; Class 4 (17%) by physical work and lack of workplace adjustments. Class 2 included more individuals with a psychological diagnosis, while diagnoses were distributed more proportionate to the sample in the other classes. The identified classes illustrate common subgroups of RTW prognosis among long-term sick-listed individuals largely independent of diagnosis. These classes could in the future assist RTW services to provide appropriate type and extent of follow-up, however more research is needed to validate the class structure and examine how these classes predict outcomes and respond to interventions.

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Novel approach towards musculoskeletal phenotypes

Cited by 42 publications

References 36 publications

Neck pain patterns and subgrouping based on weekly SMS-derived trajectories

Neck pain patterns and subgrouping based on weekly SMS-derived trajectories

Recovery trajectories in common musculoskeletal complaints by diagnosis contra prognostic phenotypes

Subgroups of Long-Term Sick-Listed Based on Prognostic Return to Work Factors Across Diagnoses: A Cross-Sectional Latent Class Analysis

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