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...
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| Veröffentlicht in: | Arthritis research & therapy 2017-03, Vol.19 (1), p.59-59, Article 59 |
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| description | 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 χ
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.
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 |
| doi_str_mv | 10.1186/s13075-017-1272-6 |
| format | Article |
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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
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.
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.</description><identifier>ISSN: 1478-6362</identifier><identifier>ISSN: 1478-6354</identifier><identifier>EISSN: 1478-6362</identifier><identifier>DOI: 10.1186/s13075-017-1272-6</identifier><identifier>PMID: 28320477</identifier><language>eng</language><publisher>England: BioMed Central</publisher><subject>Age ; Aged ; Algorithms ; Arthralgia - epidemiology ; Arthritis ; Bayes Theorem ; Cohort Studies ; Epidemiology ; Female ; Humans ; Incidence ; Joint and ligament injuries ; Knee ; Knee Injuries - epidemiology ; Knee Joint - pathology ; Logistic Models ; Male ; Methods ; Middle Aged ; Older people ; Osteoarthritis, Knee - epidemiology ; Pain ; Pain - epidemiology ; Primary care ; Risk Factors ; United Kingdom - epidemiology</subject><ispartof>Arthritis research & therapy, 2017-03, Vol.19 (1), p.59-59, Article 59</ispartof><rights>Copyright BioMed Central 2017</rights><rights>The Author(s). 2017</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c427t-ad76a47d44b463d5774fea1f458524e11b72fb63f5305fbc27957f24367a08803</citedby><cites>FETCH-LOGICAL-c427t-ad76a47d44b463d5774fea1f458524e11b72fb63f5305fbc27957f24367a08803</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5359844/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5359844/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,27901,27902,53766,53768</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/28320477$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Fernandes, G S</creatorcontrib><creatorcontrib>Bhattacharya, A</creatorcontrib><creatorcontrib>McWilliams, D F</creatorcontrib><creatorcontrib>Ingham, S L</creatorcontrib><creatorcontrib>Doherty, M</creatorcontrib><creatorcontrib>Zhang, W</creatorcontrib><title>Risk prediction model for knee pain in the Nottingham community: a Bayesian modelling approach</title><title>Arthritis research & therapy</title><addtitle>Arthritis Res Ther</addtitle><description>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 χ
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.
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.</description><subject>Age</subject><subject>Aged</subject><subject>Algorithms</subject><subject>Arthralgia - epidemiology</subject><subject>Arthritis</subject><subject>Bayes Theorem</subject><subject>Cohort Studies</subject><subject>Epidemiology</subject><subject>Female</subject><subject>Humans</subject><subject>Incidence</subject><subject>Joint and ligament injuries</subject><subject>Knee</subject><subject>Knee Injuries - epidemiology</subject><subject>Knee Joint - pathology</subject><subject>Logistic Models</subject><subject>Male</subject><subject>Methods</subject><subject>Middle Aged</subject><subject>Older people</subject><subject>Osteoarthritis, Knee - epidemiology</subject><subject>Pain</subject><subject>Pain - epidemiology</subject><subject>Primary care</subject><subject>Risk Factors</subject><subject>United Kingdom - epidemiology</subject><issn>1478-6362</issn><issn>1478-6354</issn><issn>1478-6362</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>BENPR</sourceid><recordid>eNpdkVtrFTEUhYMo9mJ_QF9KwBdfpuaycxkfBC3VCkVB9NWQmUl60p5JxmSmcP59czinpQobsiFrLfbiQ-iUknNKtXxfKCdKNISqhjLFGvkCHVJQupFcspfP9gN0VMotIYy1DF6jA6Y5I6DUIfrzM5Q7PGU3hH4OKeIxDW6Nfcr4LjqHJxsirjOvHP6e5jnEm5UdcZ_GcYlh3nzAFn-2G1eC3XvXVYLtNOVk-9Ub9MrbdXEn-_cY_f5y-eviqrn-8fXbxafrpgem5sYOSlpQA0AHkg9CKfDOUg9CCwaO0k4x30nuBSfCdz1TrVCeAZfKEq0JP0Yfd7nT0o1u6F2cs12bKYfR5o1JNph_f2JYmZt0bwQXrQaoAe_2ATn9XVyZzRhKX9vY6NJSDNWqlRKIZFX69j_pbVpyrPWqSoNugfNtIN2p-pxKyc4_HUOJ2dIzO3qm0jNbekZWz9nzFk-OR1z8AWm_lbI</recordid><startdate>20170320</startdate><enddate>20170320</enddate><creator>Fernandes, G S</creator><creator>Bhattacharya, A</creator><creator>McWilliams, D F</creator><creator>Ingham, S L</creator><creator>Doherty, M</creator><creator>Zhang, W</creator><general>BioMed Central</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>K9.</scope><scope>M0S</scope><scope>M1P</scope><scope>PHGZM</scope><scope>PHGZT</scope><scope>PIMPY</scope><scope>PJZUB</scope><scope>PKEHL</scope><scope>PPXIY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>20170320</creationdate><title>Risk prediction model for knee pain in the Nottingham community: a Bayesian modelling approach</title><author>Fernandes, G S ; Bhattacharya, A ; McWilliams, D F ; Ingham, S L ; Doherty, M ; Zhang, W</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c427t-ad76a47d44b463d5774fea1f458524e11b72fb63f5305fbc27957f24367a08803</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Age</topic><topic>Aged</topic><topic>Algorithms</topic><topic>Arthralgia - epidemiology</topic><topic>Arthritis</topic><topic>Bayes Theorem</topic><topic>Cohort Studies</topic><topic>Epidemiology</topic><topic>Female</topic><topic>Humans</topic><topic>Incidence</topic><topic>Joint and ligament injuries</topic><topic>Knee</topic><topic>Knee Injuries - epidemiology</topic><topic>Knee Joint - pathology</topic><topic>Logistic Models</topic><topic>Male</topic><topic>Methods</topic><topic>Middle Aged</topic><topic>Older people</topic><topic>Osteoarthritis, Knee - epidemiology</topic><topic>Pain</topic><topic>Pain - epidemiology</topic><topic>Primary care</topic><topic>Risk Factors</topic><topic>United Kingdom - epidemiology</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Fernandes, G S</creatorcontrib><creatorcontrib>Bhattacharya, A</creatorcontrib><creatorcontrib>McWilliams, D F</creatorcontrib><creatorcontrib>Ingham, S L</creatorcontrib><creatorcontrib>Doherty, M</creatorcontrib><creatorcontrib>Zhang, W</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>ProQuest Central (New)</collection><collection>ProQuest One Academic (New)</collection><collection>Publicly Available Content Database</collection><collection>ProQuest Health & Medical Research Collection</collection><collection>ProQuest One Academic Middle East (New)</collection><collection>ProQuest One Health & Nursing</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Arthritis research & therapy</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Fernandes, G S</au><au>Bhattacharya, A</au><au>McWilliams, D F</au><au>Ingham, S L</au><au>Doherty, M</au><au>Zhang, W</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Risk prediction model for knee pain in the Nottingham community: a Bayesian modelling approach</atitle><jtitle>Arthritis research & therapy</jtitle><addtitle>Arthritis Res Ther</addtitle><date>2017-03-20</date><risdate>2017</risdate><volume>19</volume><issue>1</issue><spage>59</spage><epage>59</epage><pages>59-59</pages><artnum>59</artnum><issn>1478-6362</issn><issn>1478-6354</issn><eissn>1478-6362</eissn><abstract>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 χ
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.
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.</abstract><cop>England</cop><pub>BioMed Central</pub><pmid>28320477</pmid><doi>10.1186/s13075-017-1272-6</doi><tpages>1</tpages><oa>free_for_read</oa></addata></record> |
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| subjects | Age Aged Algorithms Arthralgia - epidemiology Arthritis Bayes Theorem Cohort Studies Epidemiology Female Humans Incidence Joint and ligament injuries Knee Knee Injuries - epidemiology Knee Joint - pathology Logistic Models Male Methods Middle Aged Older people Osteoarthritis, Knee - epidemiology Pain Pain - epidemiology Primary care Risk Factors United Kingdom - epidemiology |
| title | Risk prediction model for knee pain in the Nottingham community: a Bayesian modelling approach |
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