Subgroups in chronic low back pain patients – a step toward cluster-based, tailored treatment in inpatient standard care: On the need for precise targeting of treatment for chronic low back pain

Langenmaier, Anna-Maria; Amelung, Volker Eric; Karst, Matthias; Krauth, Christian; Püschner, Franziska; Urbanski, Dominika; Schießl, Christine; Thoma, R.; Klasen, Bernhard

doi:10.3205/000275

Cited by 2 publications

(4 citation statements)

References 75 publications

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“…When comparing our findings with the current literature on cluster analyses of populations with chronic pain, the majority of studies also report differences observed in the psychological domain. Some of these studies found similar 'extreme' groups regarding psychological characteristics and pain experience, similar to our clusters 1 and 2, along with one or more intermediate group(s) like our cluster 3 [5,[7][8][9][10][11][12]26]. The number of clusters differs between studies, varying from 2 to 9 clusters.…”

supporting

confidence: 76%

Section: Introductionmentioning

confidence: 98%

“…Identification of such patient subgroups should include biopsychosocial components, as chronic pain is a complex multi-faceted problem with important biological (e.g., genetics), psychological (e.g., anxiety, depression, pain catastrophizing), and social (e.g., low educational attainment, poor social support) factors all determining the experience of chronic pain. Several studies have supported the hypothesis that different subgroups of patients exist within different chronic pain populations, such as chronic low back pain [5], temporomandibular disorder [6] and fibromyalgia [7], whereas other studies have attempted to cluster patients in heterogenous chronic pain populations with a mix of painful conditions [8–12]. The available studies highly vary in their use of clustering variables, psychometric instruments and statistical methods.…”

Section: Introductionmentioning

confidence: 99%

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Divergent treatment responses in chronic pain: Identifying subgroups of patients through cluster analysis

Rijsdijk,

Smits,

Azizoglu

et al. 2024

Preprint

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BackgroundChronic pain is an ill-defined disease with complex biopsychosocial aspects, posing treatment challenges. We hypothesize that treatment failure results, at least partly, from limited understanding of diverse patient subgroups. We aim to identify subgroups through psychometric data, allowing for more tailored interventions.MethodsFor this retrospective cohort study, we extracted patient-reported data from two Dutch tertiary multidisciplinary outpatient pain clinics (2018-2023) for unsupervised hierarchical clustering. Clusters were defined by anxiety, depression, pain catastrophizing, and kinesiophobia. Sociodemographics, pain characteristics, diagnosis, lifestyle, health-related quality of life (HRQoL) and treatment efficacy were compared among clusters. A prediction model was built utilizing a minimum set of questions to reliably assess cluster allocation.ResultsAmong 5,454 patients with chronic pain, three clusters emerged. Cluster 1 (n=750) was characterized by high psychological burden, low HRQoL, lower educational levels and employment rates, and more smoking. Cluster 2 (n=1,795) showed low psychological burden, intermediate HRQoL, higher educational levels and employment rates, and more alcohol consumption. Cluster 3 (n=2,909) showed intermediate features. Pain reduction following treatment was least in cluster 1 (28.6% after capsaicin patch, 18.2% after multidisciplinary treatment), compared to >50% in clusters 2 and 3. A model incorporating 15 psychometric questions reliably predicted cluster allocation.In conclusion,our study identifies distinct chronic pain patient clusters through 15 psychometric questions, revealing one cluster with notably poorer response to conventional treatment. Our prediction model may help clinicians improve treatment by allowing patient-subgroup targeted therapy according to cluster allocation.In briefHierarchical clustering of chronic pain patients revealed three clusters based on pain experience and psychological welfare, with diverse sociodemographics and treatment effects suggesting potential for tailored interventions.

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supporting

confidence: 76%

Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

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Divergent treatment responses in chronic pain: Identifying subgroups of patients through cluster analysis

Rijsdijk,

Smits,

Azizoglu

et al. 2024

Preprint

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“…As the five components constituting illness perception ( Leventhal et al, 1997 ) may impact on both clinical measures and treatment choices in different ways, using cluster analysis to identify subgroups of patients with similar perception traits seems appropriate and has also been recommended ( Langenmaier et al, 2019 ; Viniol et al, 2013 ; Windgassen et al, 2018 ). Finding clusters of patients with common perceptions and treatment needs or preferences could potentially guide clinicians on how to advise patients concerning management of their LBP, eventually optimizing adherence to treatment.…”

Section: Introductionmentioning

confidence: 99%

The correlation between illness perception, pain intensity and quality of life in elderly with low back pain in Denmark: a cross-sectional study

Ginnerup-Nielsen

Harreby

Christensen

et al. 2022

PeerJ

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Background Illness perception is related to management patterns and pain intensity, but among elderly with low back pain, this relation is unclear. The aims of this study were to analyse the associations between illness perception, pain intensity and health related quality of life in a group of elderly with low back pain and explore how different illness perception profiles would cluster and differ in terms of pain, quality of life and choice of management. Method This was a cross-sectional survey based on a cohort of originally 640 Danish children. Of the 311 respondents in 2019, 69% reported low back pain within last year and were included. Associations between illness perceptions (Brief illness perception questionnaire), health related quality of life (EuroQol-5 Domain-3L) and low back pain intensity were assessed, and participants were clustered based on their perceptions using hierarchical and K-means cluster analysis. Cluster differences in pain, quality of life and use of pharmacological and non-pharmacological treatments were explored. Results Among the 213 individuals with low back pain, 33% reported severe or fluctuating pain intensity. Higher pain intensity was associated with perceiving low back pain as a greater threat. Participants reporting fluctuating pain perceived their low back pain almost as threatening as participants reporting severe pain. Two clusters were identified. Cluster 1 reported lower quality of life (difference in medians: −0.176 (95% CI [−0.233–−0.119 ])) and was more likely to report severe or fluctuating pain (37.7% vs. 4.5% [P < 0.0001]) and to use pharmacological treatments than Cluster 2 (37.7% vs. 14.9% [P < 0.001]). No association was found between clusters concerning use of non-pharmacological treatments (P = 0.134). Conclusion Based on illness perceptions, two clusters differing in pain intensity, quality of life and use of pharmacological treatments were identified. Targeting illness perceptions may be beneficial during rehabilitation or when guiding patients with low back pain in choice of management.

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Subgroups in chronic low back pain patients – a step toward cluster-based, tailored treatment in inpatient standard care: On the need for precise targeting of treatment for chronic low back pain

Cited by 2 publications

References 75 publications

Divergent treatment responses in chronic pain: Identifying subgroups of patients through cluster analysis

Divergent treatment responses in chronic pain: Identifying subgroups of patients through cluster analysis

The correlation between illness perception, pain intensity and quality of life in elderly with low back pain in Denmark: a cross-sectional study

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