Risk prediction model for knee pain in the Nottingham community: a Bayesian modelling approach.
Twenty-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.



A 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 χ2statistic (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.



A 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.



To 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.
Division of Rheumatology, Orthopaedics and Dermatology, School of Medicine, University of Nottingham, Nottingham, UK.|Arthritis Research UK Centre for Sport, Exercise and Osteoarthritis, Nottingham, UK.|Arthritis Research UK Pain Centre, University of Nottingham, Nottingham, UK.|Academic Rheumatology, School of Medicine, Nottingham University, Nottingham City Hospital, Nottingham, NG5 1PB, UK.
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Fernandes
Division of Rheumatology, Orthopaedics and Dermatology, School of Medicine, University of Nottingham, Nottingham, UK.|Arthritis Research UK Centre for Sport, Exercise and Osteoarthritis, Nottingham, UK.|Academic Rheumatology, School of Medicine, Nottingham University, Nottingham City Hospital, Nottingham, NG5 1PB, UK.
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Bhattacharya
Division of Rheumatology, Orthopaedics and Dermatology, School of Medicine, University of Nottingham, Nottingham, UK.|Arthritis Research UK Pain Centre, University of Nottingham, Nottingham, UK.|Academic Rheumatology, School of Medicine, Nottingham University, Nottingham City Hospital, Nottingham, NG5 1PB, UK.
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McWilliams
Faculty of Life Sciences, University of Manchester, Carys Bannister Building, Dover Street, Manchester, M13 9PL, UK.
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Ingham
Division of Rheumatology, Orthopaedics and Dermatology, School of Medicine, University of Nottingham, Nottingham, UK.|Arthritis Research UK Centre for Sport, Exercise and Osteoarthritis, Nottingham, UK.|Arthritis Research UK Pain Centre, University of Nottingham, Nottingham, UK.|Academic Rheumatology, School of Medicine, Nottingham University, Nottingham City Hospital, Nottingham, NG5 1PB, UK.
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Doherty
Division of Rheumatology, Orthopaedics and Dermatology, School of Medicine, University of Nottingham, Nottingham, UK. weiya.zhang@nottingham.ac.uk.|Arthritis Research UK Centre for Sport, Exercise and Osteoarthritis, Nottingham, UK. weiya.zhang@nottingham.ac.uk.|Arthritis Research UK Pain Centre, University of Nottingham, Nottingham, UK. weiya.zhang@nottingham.ac.uk.|Academic Rheumatology, School of Medicine, Nottingham University, Nottingham City Hospital, Nottingham, NG5 1PB, UK. weiya.zhang@nottingham.ac.uk.
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Zhang
D000368:Aged; D000465:Algorithms; D018771:Arthralgia / Q000453:epidemiology*; D001499:Bayes Theorem; D015331:Cohort Studies; D005260:Female; D006801:Humans; D015994:Incidence; D007718:Knee Injuries / Q000453:epidemiology; D007719:Knee Joint / Q000473:pathology*; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D020370:Osteoarthritis, Knee / Q000453:epidemiology; D010146:Pain / Q000453:epidemiology; D012307:Risk Factors; D006113:United Kingdom / Q000453:epidemiology
Bayesian statistics; Knee pain; Musculoskeletal epidemiology; Prediction modelling