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Risk prediction model for knee pain in the Nottingham community: a Bayesian modelling approach
BACKGROUND: 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 i...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5359844/ https://www.ncbi.nlm.nih.gov/pubmed/28320477 http://dx.doi.org/10.1186/s13075-017-1272-6 |
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author | Fernandes, G. S. Bhattacharya, A. McWilliams, D. F. Ingham, S. L. Doherty, M. Zhang, W. |
author_facet | Fernandes, G. S. Bhattacharya, A. McWilliams, D. F. Ingham, S. L. Doherty, M. Zhang, W. |
author_sort | Fernandes, G. S. |
collection | PubMed |
description | BACKGROUND: 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. METHODS: 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 χ(2) 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. RESULTS: 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. CONCLUSIONS: 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13075-017-1272-6) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-5359844 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-53598442017-03-22 Risk prediction model for knee pain in the Nottingham community: a Bayesian modelling approach Fernandes, G. S. Bhattacharya, A. McWilliams, D. F. Ingham, S. L. Doherty, M. Zhang, W. Arthritis Res Ther Research Article BACKGROUND: 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. METHODS: 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 χ(2) 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. RESULTS: 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. CONCLUSIONS: 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13075-017-1272-6) contains supplementary material, which is available to authorized users. BioMed Central 2017-03-20 2017 /pmc/articles/PMC5359844/ /pubmed/28320477 http://dx.doi.org/10.1186/s13075-017-1272-6 Text en © The Author(s). 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Fernandes, G. S. Bhattacharya, A. McWilliams, D. F. Ingham, S. L. Doherty, M. Zhang, W. Risk prediction model for knee pain in the Nottingham community: a Bayesian modelling approach |
title | Risk prediction model for knee pain in the Nottingham community: a Bayesian modelling approach |
title_full | Risk prediction model for knee pain in the Nottingham community: a Bayesian modelling approach |
title_fullStr | Risk prediction model for knee pain in the Nottingham community: a Bayesian modelling approach |
title_full_unstemmed | Risk prediction model for knee pain in the Nottingham community: a Bayesian modelling approach |
title_short | Risk prediction model for knee pain in the Nottingham community: a Bayesian modelling approach |
title_sort | risk prediction model for knee pain in the nottingham community: a bayesian modelling approach |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5359844/ https://www.ncbi.nlm.nih.gov/pubmed/28320477 http://dx.doi.org/10.1186/s13075-017-1272-6 |
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