Background
People of all ages can experience persistent knee pain, and one-fourth of the population over the age of 50 years in the United Kingdom is affected [
1,
2]. Knee pain can limit lower limb function, induce disability and distress, and reduce quality of life, resulting in high societal and health-economic costs [
3]. Knee pain commonly associates with knee osteoarthritis (KOA) in middle-aged and older people and is the main reason why 20% of people with KOA give up work or retire earlier by 8 years [
4]. This burden is increasing as a result of ageing populations, increasing prevalence of obesity and lack of effective preventive strategies.
However, 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]. Self-reported knee pain can occur both with and without any radiographic osteoarthritis (OA) change, and such discrepancies could be due to x-ray views used, definition of pain, OA grading scores and population characteristics studied. Regardless of the debate, what is clear is knee pain is a common malady [
6], KOA is one of many risk factors associated with this malady, and it is the knee pain that causes a patient to consult.
In radiographic OA, the Kellgren and Lawrence (KL) composite score is often used to classify the disease which comprises the presence of osteophytes predominantly and, to an extent, joint space narrowing. The prevalence of radiographic KOA using the KL score in adults over the age of 45 years varies from 19% to 37% [
5]. The prevalence of self-reported knee pain was 35% in men and 62% of women over the age of 40 years [
7]. In the National Health and Nutrition Examination Survey I study, of 6880 participants, 14.6% reported knee pain, and only 15% of these had KL scores demonstrating structural OA changes [
8]. 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. It is knee pain and associated symptoms in KOA that lead to consultations as well as social and economic burdens [
10‐
12]. Importantly, from a patient’s perspective, it is the knee pain that limits everyday activities such as getting out of bed in the morning or climbing stairs. 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.
The first risk prediction model for incidence and progression of KOA was developed by Zhang and colleagues [
6] on the basis of a 12-year retrospective community cohort (Nottingham) using conventional risk factors such as age, sex, body mass index (BMI), family history of OA, occupational risk and joint injury. The study reported that reducing obesity would have an effect on patient outcomes and radiographic KOA development. Another prognostic prediction model for incident KOA was developed in a larger cohort (Rotterdam Study II and Chingford) [
13] using clinical, genetic and biochemical risk factors which showed a moderate predictive value for incident KOA based on genetics.
There have been no risk prediction models developed for incident or progressive knee pain, and because knee pain and KOA present distinctly in a clinical setting, further investigation into whether known and unknown risk factors affect knee pain outcomes is the purpose of the present study. We sought to develop the first knee pain risk prediction model, regardless of any underlying structural changes of KOA, to provide a convenient tool for use in primary care to predict the risk of this common malady. As a result, conventional risk factors that can be measured easily in a primary care setting were included, such as age, sex, BMI, self-reported varus and valgus alignment, and joint injury [
14]. The objectives of this study were (a) to develop a risk prediction model for incident knee pain in community participants in Nottingham, UK; and (b) to validate this internally within the Nottingham community and externally with the Osteoarthritis Initiative (OAI) cohort from the United States.
Discussion
To our knowledge, this is the first risk prediction model for knee pain in a general population sample. Conventional risk factors that can be measured easily in primary care were included in this model to increase its utility, including age, sex, BMI, history of knee injury, pain elsewhere and knee alignment. The following are the main findings:
1.
Knee pain can be predicted by conventional risk factors.
2.
The likelihood of this prediction (calibration) is better in the general population than in individuals with high risk of KOA.
3.
The discrimination is also better in the general population than in the high-risk population (OAI).
4.
The model has high sensitivity (95%) but lower specificity (32%), so it is more useful for screening possible knee pain cases rather than for confirming the diagnosis.
This is also the first prediction model to use Bayesian inference technique. This has at least two advantages: (1) It usually gives more precise estimates (i.e., narrower CIs) of the risk prediction [
19], and (2) it provides a posterior probability of having OR >1 rather than a
p value. The latter gives a degree of likelihood that a person would have the disease, given an exposure to the risk factor(s), not just a false-positive error from a statistical test. This is an advantage of the Bayesian over the frequentist statistics, where uncertainty is measured by the probability of having a disease, not the probability of making a false-positive error [
20].
Knee injury, presence of pain elsewhere and varus knee alignment were the strongest clinical predictors of knee pain using our model. Not surprisingly, the strongest predictor was knee injury, which is a well-known local biomechanical risk factor for subsequent development of KOA, of which knee pain is a major symptom [
2]. The precise relationships between joint injury and development of post-traumatic OA and pain are poorly understood. However, any major insults to the articular cartilage, menisci and ligaments can increase the risk of subsequent OA [
2,
21]. Our findings align with those in another U.K.-based cohort in which the onset of knee pain was significantly associated with baseline knee injury (OR 1.59, 95% CI 1.17–2.17) over a 3-year period [
22]. Knee malalignment is another recognised biomechanical risk factor for the development and progression of KOA, and we previously reported that self-reported constitutional varus malalignment associates with increased incident knee pain (OR 2.82, 95% CI 1.57–5.06) over a 10-year period [
17]. A varus alignment creates a knee adduction moment which increases joint loading, particularly on the medial tibiofemoral compartment [
23]. In the present study, self-reported varus or valgus alignment had an OR of 3.93 (95% CI 2.14–6.57) for predicting knee pain at 12-year follow-up. Whilst Sharma and colleagues [
23] relied on x-ray images for analysis of alignment and load bearing axes, our method uses a simple and cost-effective self-reported measure which has been validated previously [
17] and which can be included as part of routine clinical assessment.
Pain elsewhere was a significant risk factor for development of knee pain in our cohort, with an OR of 2.49 (95% CI 1.83–3.30). This is in keeping with longitudinal studies [
22] and prevalence literature [
24,
25] which have particularly focused on regional body pain at the hip and back. The same definition of pain elsewhere (presence of hip pain and back pain) was used in both the Nottingham and OAI cohorts. It is possible that a proportion of self-reported knee pain could be referred pain from the hips or spine rather than pain originating at the knee. However, simple enquiry concerning other features of the pain (e.g., localised or diffuse, associated with sensory disturbance, improved by rubbing, exacerbated by use or straining) together with a basic musculoskeletal examination should permit ready distinction in primary care without the need for any investigations.
There are several caveats to this study. Firstly, the model performed poorly in the OAI population in the United States. This may be because OAI selected individuals with a higher risk for KOA [
26]. The OAI consists of participants with either established KOA or significant risk factors for the development of KOA to help identify and characterise the disease from onset to joint replacement. Incidentally, 853 OAI participants included in this study had available KL grading, and their data (see Additional file
1: Appendix S1) demonstrated that 317 participants (37.15%) showed definite signs of osteoarthritis (joint space narrowing and osteophyte formation) with KL ≥2, whereas 512 participants (60%) had some signs of osteoarthritis (KL ≥1). By contrast, the Nottingham participants were derived randomly from the community and were at much lower risk of knee pain. There were statistically significant differences in key risk factors at baseline between the two populations, such as age, BMI and injury (Table
1). As a result, the model lost its power to differentiate the cases in hospitals, because those are more likely to be severe cases within a narrower band of the disease spectrum. It suggests that the developed model may be more useful for a community setting, such as in primary care. An alternative to this approach would be to develop the model for OAI and verify that it could not predict the Nottingham population, which would strengthen the obvious discrepancy between these two population sources. Secondly, although we successfully validated the model in the community, this is only an internal validation. We still do not know whether this community-based knee pain prediction model is useful for other community populations, such as a European or U.S. population sample. Therefore, further validation is required. Thirdly, the Nottingham knee pain cohort is a retrospective cohort with only two time points for dichotomous outcomes (knee pain-positive and knee pain-negative). Therefore, it was not possible to apply a time-to-event or survival analysis to maximise information on knee pain incidence. There is an inherent bias to retrospective study designs, such as the inability to accurately recall exposures prior to the study owing to selective preconceptions about the association between risk factors and the knee pain (outcome). Furthermore, this paper is based purely on knee pain outcomes as opposed to structural change from KOA (i.e., evidence of radiographic OA), owing to the lack of knee x-rays available for all 1822 participants in the Nottingham cohort. The prediction can be limited only to knee pain, not to KOA.
Acknowledgements
This study was supported financially by the Arthritis Research UK Centre for Sport, Exercise and Osteoarthritis (grant reference 20194). The OAI is a public-private partnership comprised of five contracts (N01-AR-2-2258, N01-AR-2-2259, N01-AR-2-2260, N01-AR-2-2261 and N01-AR-2-2262) funded by the National Institutes of Health (NIH), a branch of the Department of Health and Human Services, and conducted by the OAI Study Investigators. Private funding partners include Merck Research Laboratories, Novartis Pharmaceuticals Corporation, GlaxoSmithKline and Pfizer, Inc. Private sector funding for the OAI is managed by the Foundation for the National Institutes of Health. The manuscript was prepared using an OAI public use dataset and does not necessarily reflect the opinions or views of the OAI investigators, the NIH or the private funding partners.