Determining One‐Year Trajectories of Low‐Back–Related Leg Pain in Primary Care Patients: Growth Mixture Modeling of a Prospective Cohort Study

Ogollah, Reuben; Konstantinou, Kika; Stynes, Siobhán; Dunn, Kate M.

doi:10.1002/acr.23556

Cited by 7 publications

(4 citation statements)

References 36 publications

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“…Many studies have identified clusters of pain trajectories among individuals living with chronic pain. Some have focused on participants with 1 chronic pain condition, such as osteoarthritis [ 15 , 36 - 39 , 52 - 58 ], low back pain [ 13 , 14 , 27 , 59 - 65 ], other back pain [ 25 , 32 , 49 , 66 ], neck or shoulder pain [ 33 , 61 , 67 , 68 ], leg pain [ 29 ], knee pain [ 69 ], or foot pain [ 70 ], whereas others have identified clusters among a broader population, such as those with musculoskeletal pain [ 26 , 31 , 47 , 71 , 72 ] or general pain [ 48 , 73 - 75 ]. Clusters in these studies were described by the severity of pain (eg, no pain , very low pain , mild pain , moderate pain , high pain , and severe pain ), the level of change in pain severity (eg, persistent , ongoing , episodic , worsening , recovering , and fluctuating ), or a combination of these features.…”

Section: Discussionmentioning

confidence: 99%

Identifying Weekly Trajectories of Pain Severity Using Daily Data From an mHealth Study: Cluster Analysis

Little,

Schultz,

House

et al. 2024

JMIR Mhealth Uhealth

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Background People with chronic pain experience variability in their trajectories of pain severity. Previous studies have explored pain trajectories by clustering sparse data; however, to understand daily pain variability, there is a need to identify clusters of weekly trajectories using daily pain data. Between-week variability can be explored by quantifying the week-to-week movement between these clusters. We propose that future work can use clusters of pain severity in a forecasting model for short-term (eg, daily fluctuations) and longer-term (eg, weekly patterns) variability. Specifically, future work can use clusters of weekly trajectories to predict between-cluster movement and within-cluster variability in pain severity. Objective This study aims to understand clusters of common weekly patterns as a first stage in developing a pain-forecasting model. Methods Data from a population-based mobile health study were used to compile weekly pain trajectories (n=21,919) that were then clustered using a k-medoids algorithm. Sensitivity analyses tested the impact of assumptions related to the ordinal and longitudinal structure of the data. The characteristics of people within clusters were examined, and a transition analysis was conducted to understand the movement of people between consecutive weekly clusters. Results Four clusters were identified representing trajectories of no or low pain (1714/21,919, 7.82%), mild pain (8246/21,919, 37.62%), moderate pain (8376/21,919, 38.21%), and severe pain (3583/21,919, 16.35%). Sensitivity analyses confirmed the 4-cluster solution, and the resulting clusters were similar to those in the main analysis, with at least 85% of the trajectories belonging to the same cluster as in the main analysis. Male participants spent longer (participant mean 7.9, 95% bootstrap CI 6%-9.9%) in the no or low pain cluster than female participants (participant mean 6.5, 95% bootstrap CI 5.7%-7.3%). Younger people (aged 17-24 y) spent longer (participant mean 28.3, 95% bootstrap CI 19.3%-38.5%) in the severe pain cluster than older people (aged 65-86 y; participant mean 9.8, 95% bootstrap CI 7.7%-12.3%). People with fibromyalgia (participant mean 31.5, 95% bootstrap CI 28.5%-34.4%) and neuropathic pain (participant mean 31.1, 95% bootstrap CI 27.3%-34.9%) spent longer in the severe pain cluster than those with other conditions, and people with rheumatoid arthritis spent longer (participant mean 7.8, 95% bootstrap CI 6.1%-9.6%) in the no or low pain cluster than those with other conditions. There were 12,267 pairs of consecutive weeks that contributed to the transition analysis. The empirical percentage remaining in the same cluster across consecutive weeks was 65.96% (8091/12,267). When movement between clusters occurred, the highest percentage of movement was to an adjacent cluster. Conclusions The clusters of pain severity identified in this study provide a parsimonious description of the weekly experiences of people with chronic pain. These clusters could be used for future study of between-cluster movement and within-cluster variability to develop accurate and stakeholder-informed pain-forecasting tools.

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Section: Discussionmentioning

confidence: 99%

Identifying Weekly Trajectories of Pain Severity Using Daily Data From an mHealth Study: Cluster Analysis

Little,

Schultz,

House

et al. 2024

JMIR Mhealth Uhealth

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show abstract

“…Maintaining or treating this functionality is important for spine physicians to preserve or improve life quality and eventually get patients back to work. In recent years new research has identified clinically relevant clusters of patients presenting with low back pain (35)(36)(37) and backpain related leg pain (25,26,28,(38)(39)(40). Although several classification systems have been published, very few have focused and include patient reported outcomes.…”

Section: Discussionmentioning

confidence: 99%

Real world data collection and cluster analysis in patients with sciatica due to lumbar disc herniation

Raymaekers

Bamps

Duyvendak

et al. 2022

Clinical Neurology and Neurosurgery

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“…Neuropathic pain is a challenge to manage, and many patients with persistent LBP might have neuropathic pain that does not respond to existing treatments (Baron et al, ). Previous longitudinal modelling on a cohort of patients with low back‐related leg pain identified a “persistent severe” and “improving severe” group (Ogollah, Konstantinou, Stynes, S., Dunn, ). The characteristics that distinguished the persistent severe group were the patients who were more likely to report leg pain of potential neuropathic nature (measured using the self‐report Leeds Assessment Neuropathic Symptoms and Signs (S‐LANSS) questionnaire) than those in the “improving severe” group.…”

Section: Discussionmentioning

confidence: 99%

New insight to the characteristics and clinical course of clusters of patients with imaging confirmed disc‐related sciatica

Stynes

Grøvle

Haugen

et al. 2019

European Journal of Pain

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Background Referral to secondary care is common for a considerable proportion of patients with persistent sciatica symptoms. It is unclear if information from clinical assessment can further identify distinct subgroups of disc‐related sciatica, with perhaps different clinical courses. Aims This study aims to identify and describe clusters of imaging confirmed disc‐related sciatica patients using latent class analysis, and compare their clinical course. Methods The study population were 466 patients with disc‐related sciatica. Variables from clinical assessment were included in the analysis. Characteristics of the identified clusters were described and their clinical course over 2 years was compared. Results A four‐cluster solution was optimal. Cluster 1 (n = 110) had mild back and leg pain; cluster 2 (n = 59) had moderate back and leg pain; cluster 3 (n = 158) had mild back pain and severe leg pain; cluster 4 (n = 139) had severe back and leg pain. Patients in cluster 4 had the most severe profile in terms of disability, distress and comorbidity and the lowest reported global change and the smallest proportion of patients with a successful outcome at 2 years. Of the 135 patients who underwent surgery, 42% and 41% were in clusters 3 and 4, respectively. Conclusions Using a strict diagnosis of sciatica, this work identified four clusters of patients primarily differentiated by back and leg pain severity. Patients with severe back and leg pain had the most severe profile at baseline and follow‐up irrespective of intervention. This simple classification system may be useful when considering prognosis and management with sciatica patients. Significance Using data from a large observational prospective study, this work identifies four distinct clusters of patients with imaging confirmed disc‐related sciatica. This classification could be used when considering prognosis and management with sciatica patients at their initial consultation.

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Determining One‐Year Trajectories of Low‐Back–Related Leg Pain in Primary Care Patients: Growth Mixture Modeling of a Prospective Cohort Study

Cited by 7 publications

References 36 publications

Identifying Weekly Trajectories of Pain Severity Using Daily Data From an mHealth Study: Cluster Analysis

Identifying Weekly Trajectories of Pain Severity Using Daily Data From an mHealth Study: Cluster Analysis

Real world data collection and cluster analysis in patients with sciatica due to lumbar disc herniation

New insight to the characteristics and clinical course of clusters of patients with imaging confirmed disc‐related sciatica

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