Abstract
BackgroundTwenty-five percent of the British population over the age of 50 years experiences knee pain. Knee pain can limit physical ability and cause distress and bears significant socioeconomic costs. The objectives of this study were to develop and validate the first risk prediction model for incident knee pain in the Nottingham community and validate this internally within the Nottingham cohort and externally within the Osteoarthritis Initiative (OAI) cohort.MethodsA total of 1822 participants from the Nottingham community who were at risk for knee pain were followed for 12 years. Of this cohort, two-thirds (n = 1203) were used to develop the risk prediction model, and one-third (n = 619) were used to validate the model. Incident knee pain was defined as pain on most days for at least 1 month in the past 12 months. Predictors were age, sex, body mass index, pain elsewhere, prior knee injury and knee alignment. A Bayesian logistic regression model was used to determine the probability of an OR >1. The Hosmer-Lemeshow χ2 statistic (HLS) was used for calibration, and ROC curve analysis was used for discrimination. The OAI cohort from the United States was also used to examine the performance of the model.ResultsA risk prediction model for knee pain incidence was developed using a Bayesian approach. The model had good calibration, with an HLS of 7.17 (p = 0.52) and moderate discriminative ability (ROC 0.70) in the community. Individual scenarios are given using the model. However, the model had poor calibration (HLS 5866.28, p < 0.01) and poor discriminative ability (ROC 0.54) in the OAI cohort.ConclusionsTo our knowledge, this is the first risk prediction model for knee pain, regardless of underlying structural changes of knee osteoarthritis, in the community using a Bayesian modelling approach. The model appears to work well in a community-based population but not in individuals with a higher risk for knee osteoarthritis, and it may provide a convenient tool for use in primary care to predict the risk of knee pain in the general population.
Highlights
Twenty-five percent of the British population over the age of 50 years experiences knee pain
Population characteristics The Nottingham cohort had a mean age at baseline of 56 years (SD ±8.84) and a mean body mass index (BMI) of 25.13 kg/m2 (SD ±3.4), and 55.38% were women
Risk prediction model The development data were used to determine which parameters were of significance (p < 0.05), and these were identified as age, sex, BMI, knee injury, pain elsewhere and presence of a varus knee or valgus knee
Summary
Twenty-five percent of the British population over the age of 50 years experiences knee pain. Knee pain can limit lower limb function, induce disability and distress, and reduce quality of life, resulting in high societal and the association between knee pain and KOA continues to be debated. One reason for this is the common discordance between radiographic KOA and knee pain [5]. In 1992, Hadler remarked, ‘The epidemiology of osteoarthritis and the epidemiology of pain have little in common, not nothing in common, but surprisingly little’ ([9]; pg 598) This distinction is important because OA management guidelines, healthcare spending, and a healthcare practitioner’s diagnosis, treatment and management are targeted at reducing pain and associated symptoms as opposed to treating structural radiographic changes. An understanding of the risk factors that contribute to and predict incident knee pain and knee pain progression instead of focussing on structural KOA is arguably a more insightful and useful clinical tool
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