2014
DOI: 10.1016/j.joca.2014.09.026
|View full text |Cite
|
Sign up to set email alerts
|

Pain trajectory groups in persons with, or at high risk of, knee osteoarthritis: findings from the Knee Clinical Assessment Study and the Osteoarthritis Initiative

Abstract: SummaryObjectiveThe authors aimed to characterize distinct trajectories of knee pain in adults who had, or were at high risk of, knee osteoarthritis using data from two population-based cohorts.MethodLatent class growth analysis was applied to measures of knee pain severity on activity obtained at 18-month intervals for up to 6 years between 2002 and 2009 from symptomatic participants aged over 50 years in the Knee Clinical Assessment Study (CAS-K) in the United Kingdom. The optimum latent class growth model f… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
2
1

Citation Types

20
85
2

Year Published

2015
2015
2020
2020

Publication Types

Select...
8

Relationship

0
8

Authors

Journals

citations
Cited by 104 publications
(107 citation statements)
references
References 48 publications
20
85
2
Order By: Relevance
“…[18][19][20] Using LCGA to define pain trajectories in knee OA is a relatively new technique and has only been applied by a few authors to date. [21][22][23] Holla and colleagues applied this technique on the same study population (CHECK), but used WOMAC physical function as the outcome variable; 22 they identified a three-group model and found similar associations to the study findings presented here. Collins and colleagues applied LCGA on a study population from the Osteoarthritis Initiative (OAI) and used WOMAC pain as the outcome measure; they identified five trajectories 21 and suggest knee OA is characterised by persistent, rather than severe, inevitable progression -this is in contrast with the findings presented here.…”
Section: Comparison With Existing Literaturesupporting
confidence: 60%
See 1 more Smart Citation
“…[18][19][20] Using LCGA to define pain trajectories in knee OA is a relatively new technique and has only been applied by a few authors to date. [21][22][23] Holla and colleagues applied this technique on the same study population (CHECK), but used WOMAC physical function as the outcome variable; 22 they identified a three-group model and found similar associations to the study findings presented here. Collins and colleagues applied LCGA on a study population from the Osteoarthritis Initiative (OAI) and used WOMAC pain as the outcome measure; they identified five trajectories 21 and suggest knee OA is characterised by persistent, rather than severe, inevitable progression -this is in contrast with the findings presented here.…”
Section: Comparison With Existing Literaturesupporting
confidence: 60%
“…11 Nicholls and colleagues applied LCGA on a study population from the Knee Clinical Assessment Study and matched their model with a population drawn from the OAI. 23 They also used WOMAC pain as an outcome variable and identified five trajectories. They concluded that different types of symptom progression in knee OA exist, varying from severe progression to regression, which is in accordance with the findings presented here.…”
Section: Comparison With Existing Literaturementioning
confidence: 99%
“…The natural history of OA involves fluctuation of symptoms, and these people may well have lower pain values on reassessment in the absence of an intervention, introducing regression to the mean (9). In addition, people entering a study in a painful phase of their disease have been shown to have more missing visits in longitudinal cohort studies (10). The second issue with published longitudinal functional values in people with knee OA is that people lost to followup have been shown to be older and have poorer function than those without missing visits (11,12).…”
Section: Introductionmentioning
confidence: 99%
“…Model fit was based on statistical criteria (Bayesian information criterion (BIC)) and on a judgement of model interpretability [12]. In our study, the latent class mixed-effects model was specified with linear, quadratic and cubic time terms to model non-linear change over time [13-15]. Coefficients were allowed to vary between latent groups.…”
Section: Methodsmentioning
confidence: 99%