ABSTRACT
OBJECTIVES: Knee osteoarthritis (KOA) progression is frequently monitored by calculating the change in knee joint space width (JSW) measurements. Such differences are small and sensitive to measurement error. We aimed to assess the utility of two alternative statistical modelling methods for monitoring KOA. MATERIAL AND METHODS: We used JSW on radiographs from both the control arm of the Strontium Ranelate Efficacy in Knee Osteoarthritis trial (SEKOIA), a 3-year multicentre, double-blind, placebo-controlled phase three trial, and the Osteoarthritis Initiative (OAI), an open-access longitudinal dataset from the USA comprising participants followed over 8 years. Individual estimates of annualised change obtained from frequentist linear mixed effect (LME) and Bayesian hierarchical modelling, were compared with annualised crude change, and the association of these parameters with change in WOMAC pain was examined. RESULTS: Mean annualised JSW changes were comparable for all estimates, a reduction of around 0.14 mm/y in SEKOIA and 0.08 mm/y in OAI. The standard deviation (SD) of change estimates was lower with LME and Bayesian modelling than crude change (SEKOIA SD = 0.12, 0.12 and 0.21 respectively; OAI SD = 0.08, 0.08 and 0.11 respectively). Estimates from LME and Bayesian modelling were statistically significant predictors of change in pain in SEKOIA (LME P-value = 0.04, Bayes P-value = 0.04), while crude change did not predict change in pain (P-value = 0.10). CONCLUSIONS: Implementation of LME or Bayesian modelling in clinical trials and epidemiological studies, would reduce sample sizes by enabling all study participants to be included in analysis regardless of incomplete follow up, and precision of change estimates would improve. They provide increased power to detect associations with other measures.
Subject(s)
Knee Joint/diagnostic imaging , Osteoarthritis, Knee/diagnostic imaging , Aged , Bayes Theorem , Disease Progression , Female , Humans , Knee Joint/pathology , Linear Models , Male , Middle Aged , Organ Size , Osteoarthritis, Knee/physiopathology , Outcome Assessment, Health CareABSTRACT
PURPOSE: Recent studies have suggested that metabolic factors (obesity, diabetes, hypertension and dyslipidemia) and their clustering in metabolic syndrome (MetS) might be involved in the pathophysiology of knee osteoarthritis (OA). We investigated their impact on radiographic progression by an annualised measure of the joint space narrowing (JSN) of the medial tibiofemoral compartment. METHODS: 559 patients older than 50 years with symptomatic knee OA were recruited for the placebo arm of the SEKOIA trial. The presence of diabetes, hypertension and dyslipidemia was determined at baseline interview. Body mass index (BMI) was calculated, obesity was considered >30 kg/m(2). MetS was defined by the sum of metabolic factors ≥ 3. Minimal medial tibiofemoral joint space on plain radiographs was measured by an automated method at baseline and then annually for up to 3 years. RESULTS: The mean age of patients was 62.8 [62.2-63.4] years; 392 were women. A total of 43.8% was obese, 6.6% had type 2 diabetes, 45.1% hypertension, 27.6% dyslipidemia and 13.6% MetS. Mean annualised JSN was greater for patients with type 2 diabetes than without diabetes (0.26 [-0.35 to -0.17] vs 0.14 [-0.16 to -0.12] mm; P = 0.001). This association remained significant after adjustment for sex, age, BMI, hypertension and dyslipidemia (P = 0.018). In subgroup analysis, type 2 diabetes was a significant predictor of JSN in males but not females. The other metabolic factors and MetS were not associated with annualised JSN. CONCLUSION: Type 2 diabetes was a predictor of joint space reduction in men with established knee OA. No relationships were found between MetS or other metabolic factors and radiographic progression.
