By continuing to use our site, you agree to our use of cookies, our Terms & Conditions and Privacy Policy.
Risk of radiographic knee osteoarthritis in 12 years:
This clinical prediction model calculates the risk of developing radiographic knee osteoarthritis in 12 years using 6 clinical variables (c-index: 0.60-0.74).
Research authors: Zhang W, McWilliams DF, Ingham SL, Doherty SA, Muthuri S, Muir KR, Doherty M.
Details Formula Study characteristics Files & References
★★★
Model author
Evidencio Community
Model ID
498
Version
1.12
Revision date
2016-05-09
Specialty
Orthopaedics
MeSH terms
  • Osteoarthritis Of Knee
  • Radiography
  • Clinical Prediction Rule
    • Model type
    Logistic regression (Calculation)
    Status
    public
    Rating
    ★ ★ ★
    Share
    Formula
    No Formula defined yet
    Condition Formula

    Additional information

    Design:
    A 12-year retrospective cohort study was undertaken involving four general practices in North Nottinghamshire.1

    Exclusion criteria:
    People with terminal illness, psychiatric illness and severe dementia were excluded.1

    Study population:
    In total, 424 people were eligible for the study.1 Individuals were recruited from two baseline community postal questionnaire studies for knee pain.2,3 Baseline data were collected between 1996 and 1999 from 9429 adults aged 40-79 years on the general practice registers. A follow-up survey was undertaken during 2007–2008 in 5479 individuals who are still registered with the general practices and eligible for the study. Radiographs of both knees at baseline and follow-up were obtained from 424 participants according to availability and willingness to participate.

    Source:
    1 Zhang W, McWilliams DF, Ingham SL, et al. Nottingham knee osteoarthritis risk prediction models. Ann Rheum Dis. 2011;70(9):1599-604.
    2 O’Reilly SC, Muir KR, Doherty M. Screening for pain in knee osteoarthritis: which question? Ann Rheum Dis. 1996:55:931-3.
    3 Thomas KS, Muir KR, Doherty M, et al. Home based exercise programme for knee pain and knee osteoarthritis: randomised controlled trial. BMJ. 2002;325:752.
     

    Study Population

    Total population size: 424
    Males: {{ model.numberOfMales }}
    Females: {{ model.numberOfFemales }}

    Continuous characteristics

    Name Mean SD Unit
    Age 56.8 7.9 years
    Body mass index 25.5 3.5 kg/m2
    Name LL Q1 Median Q3 UL Unit
    Occupational risk 0 2 3 4 4 risk score
    Duration of follow-up 7 12 12 years

    Categorical characteristics

    Name Subset / Group Nr. of patients
    Previous serious knee injury Yes 25
    No 399
    Familial osteoarthritis Yes 136
    No 288

    Related files

    Supporting Publications

    Risk of radiographic knee osteoarthritis in 12 years:
    ...

    {{ resultSubheader }}

    {{ model.survival.PITTitle }}

    {{ model.survival.YNETitle }}

    Result
    Note
    Notes are only visible in the result download and will not be saved by Evidencio

    Risk of radiographic knee osteoarthritis in 12 years:

    {{ resultSubheader }}
    {{ chart.title }}

    Outcome stratification

    Result interval {{ additionalResult.min }} to {{ additionalResult.max }}

    Conditional information

    Result interpretation

    How this model should be used:
    This model may be applied at the individual level to predict the risk, and to encourage risk reduction. It may also be used at the population level, with reference to other relative risks from published studies, to estimate the potential population risk reduction that may be gained by primary prevention of the major risk factors of knee OA.

    Model performance:
    Upon internal validation (N=424), the area under the ROC curve showed a moderate discriminative ability of the model (c-index: 0.69, 95% CI 0.62 to 0.76).1 External validation was performed on two different cohorts: the (2) Nottingham Genetics of Osteoarthritis and Lifestyle (GOAL) case-control study population (N=3174)2 and (3) the Osteoarthritis Initiative (OAI) cohort study population (N=4796).3  External validation resulted in c-indices of 0.60 (0.55 to 0.64) and 0.74 (0.72 to 0.76), respectively. The Hosmer–Lemeshow χ2 statistic for goodness-of-fit showed good calibration in all three patient cohorts.

    Source:
    1 Zhang W, McWilliams DF, Ingham SL, et al. Nottingham knee osteoarthritis risk prediction models. Ann Rheum Dis. 2011;70(9):1599-604.
    2 Zhang W, Robertson J, Doherty S, et al. Index to ring finger length ratio and the risk of osteoarthritis. Arthritis Rheum. 2008;58:137-44.
    3 Fawaz-Estrup F. The osteoarthritis initiative: an overview. Med Health R I. 2004;87:169-71.

    {{ file.classification }}

    Calculations alone should never dictate patient care, and are no substitute for professional judgement. See our full disclaimer.

    Comments
    Rating
    Comment
    Please enter a comment of rating
    Comments are visible to anyone

    Model feedback

    No feedback yet 1 Comment {{ model.comments.length }} Comments
    Not rated | On {{ comment.created_at }} {{ comment.user.username }} a no longer registered author wrote:
    Recent Orthopaedics Models
    Ottawa knee rule
    Benefit form manual physiotherapy and exercise in knee osteoarthritis
    Risk of symptomatic knee osteoarthritis in 12 years
    Kans op aanwezigheid van een polsfractuur
    Clinical prediction rule to identify low back pain patients most likely to benefit from spinal manipulation
    All Orthopaedics Models
    logo

    Please sign in to enable Evidencio print features

    In order to use the Evidencio print features, you need to be logged in.
    If you don't have an Evidencio Community Account you can create your free personal account at:

    https://www.evidencio.com/registration

    Printed results - Examples {{ new Date().toLocaleString() }}

    Evidencio Community Account Benefits

    With an Evidencio Community account you can:

    • Create and publish your own prediction models.
    • Share your prediction models with your colleagues, research group, organization or the world.
    • Review and provide feedback on models that have been shared with you.
    • Validate your models and validate models from other users.
    • Find models based on Title, Keyword, Author, Institute, or MeSH classification.
    • Use and save prediction models and their data.
    • Use patient specific protocols and guidelines based on sequential models and decision trees.
    • Stay up-to-date with new models in your field as they are published.
    • Create your own lists of favorite models and topics.
    A personal Evidencio account is free, with no strings attached! Join us and help create clarity, transparency, and efficiency in the creation, validation, and use of medical prediction models.
    Disclaimer: Calculations alone should never dictate patient care, and are no substitute for professional judgement.
    Evidencio v2.16 © 2015 - 2020 Evidencio. All Rights Reserved | Terms & Conditions | Privacy Policy | Disclaimer | Contact