Prediction Models for Knee Osteoarthritis: Review of Current Models and Future Directions



1 1 Department of Health Sciences Research, Mayo Clinic, 200 First St SW Rochester, Rochester, Minnesota, USA 2 Department of Orthopedics, Mayo Clinic, 200 First St SW Rochester, Rochester, Minnesota, USA

2 Department of Health Sciences Research, Mayo Clinic, 200 First St SW Rochester, Rochester, Minnesota, USA

3 Department of Orthopedics, Mayo Clinic, 200 First St SW Rochester, Rochester, Minnesota, USA


Background: Knee osteoarthritis (OA) is a prevalent joint disease. Clinical prediction models consider a wide range 
of risk factors for knee OA. This review aimed to evaluate published prediction models for knee OA and identify 
opportunities for future model development.
Methods: We searched Scopus, PubMed, and Google Scholar using the terms knee osteoarthritis, prediction model, 
deep learning, and machine learning. All the identified articles were reviewed by one of the researchers and we recorded 
information on methodological characteristics and findings. We only included articles that were published after 2000 
and reported a knee OA incidence or progression prediction model.
Results: We identified 26 models of which 16 employed traditional regression-based models and 10 machine learning 
(ML) models. Four traditional and five ML models relied on data from the Osteoarthritis Initiative. There was significant 
variation in the number and type of risk factors. The median sample size for traditional and ML models was 780 
and 295, respectively. The reported Area Under the Curve (AUC) ranged between 0.6 and 1.0. Regarding external 
validation, 6 of the 16 traditional models and only 1 of the 10 ML models validated their results in an external data set. 
Conclusion: Diverse use of knee OA risk factors, small, non-representative cohorts, and use of magnetic resonance 
imaging which is not a routine evaluation tool of knee OA in daily clinical practice are some of the main limitations of 
current knee OA prediction models. 
Level of evidence: III


Main Subjects

1. Murray CJ, Atkinson C, Bhalla K, et al. The state of
US health, 1990-2010: burden of diseases, injuries,
and risk factors. Jama. 2013;310(6):591-608. doi:
2. Vos T, Flaxman AD, Naghavi M, et al. Years lived with
disability (YLDs) for 1160 sequelae of 289 diseases
and injuries 1990-2010: a systematic analysis for
the Global Burden of Disease Study 2010. Lancet.
2012;380(9859):2163-96. doi: 10.1016/S0140-
3. Lawrence RC, Felson DT, Helmick CG, et al. Estimates
of the prevalence of arthritis and other rheumatic
conditions in the United States. Part II. Arthritis
Rheum. 2008;58(1):26-35. doi: 10.1002/art.23176.
4. Wallace IJ, Worthington S, Felson DT, et al.
Knee osteoarthritis has doubled in prevalence
since the mid-20th century. Proc Natl Acad Sci
U S A. 2017;114(35):9332-6. doi: 10.1073/
5. Zhang W, Moskowitz R, Nuki G, et al. OARSI
recommendations for the management of hip
and knee osteoarthritis, part I: critical appraisal
of existing treatment guidelines and systematic
review of current research evidence. Osteoarthritis
cartilage. 2007;15(9):981-1000. doi: 10.1016/j.
6. Zhang W, Nuki G, Moskowitz R, et al. OARSI
recommendations for the management of hip and knee
osteoarthritis: part III: Changes in evidence following
systematic cumulative update of research published
through January 2009. Osteoarthritis Cartilage.
2010;18(4):476-99. doi: 10.1016/j.joca.2010.01.013.
7. Zhang W, Robertson J, Jones A, Dieppe P, Doherty M. The
placebo effect and its determinants in osteoarthritis:
meta-analysis of randomised controlled trials. Ann
Rheum Dis. 2008;67(12):1716-23. doi: 10.1136/
8. Losina E, Daigle ME, Suter L, et al. Disease-modifying
drugs for knee osteoarthritis: can they be costeffective? Osteoarthritis Cartilage. 2013;21(5):655-
67. doi: 10.1016/j.joca.2013.01.016.
9. Bourne RB, Chesworth BM, Davis AM, Mahomed
NN, Charron KD. Patient satisfaction after total knee
arthroplasty: who is satisfied and who is not? Clin
Orthop Relat Res. 2010;468(1):57-63. doi: 10.1007/
10.Hamel MB, Toth M, Legedza A, Rosen MP. Joint
replacement surgery in elderly patients with severe
osteoarthritis of the hip or knee: decision making,
postoperative recovery, and clinical outcomes. Arch
Intern Med. 2008;168(13):1430-40. doi: 10.1001/
11.Singh JA, Gabriel S, Lewallen D. The impact of gender,
age, and preoperative pain severity on pain after TKA.
Clin Orthop Relat Res. 2008;466(11):2717-23. doi:
12.McAlindon TE, Bannuru RR, Sullivan M, et al.
OARSI guidelines for the non-surgical management
of knee osteoarthritis. Osteoarthritis Cartilage.
2014;22(3):363-88. doi: 10.1016/j.joca.2014.01.003.
13.McWilliams D, Leeb B, Muthuri S, Doherty M, Zhang
W. Occupational risk factors for osteoarthritis of the knee: a meta-analysis. Osteoarthritis Cartilage.
2011;19(7):829-39. doi: 10.1016/j.joca.2011.02.016.
14.Zhang W. Risk factors of knee osteoarthritis–excellent
evidence but little has been done. Osteoarthritis
Cartilage. 2010;18(1):1-2. doi: 10.1016/j.joca.
15.Blagojevic M, Jinks C, Jeffery A, Jordan K. Risk factors
for onset of osteoarthritis of the knee in older adults:
a systematic review and meta-analysis. Osteoarthritis
Cartilage. 2010;18(1):24-33. doi: 10.1016/j.joca.
16.Jamshidi A, Pelletier JP, Martel-Pelletier J. Machinelearning-based patient-specific prediction models for
knee osteoarthritis. Nat Rev Rheumatol. 2019;15(1):49-
60. doi: 10.1038/s41584-018-0130-5.
17.Zhang W, McWilliams DF, Ingham SL, et al. Nottingham
knee osteoarthritis risk prediction models. Ann
Rheum Dis. 2011;70(9):1599-604. doi: 10.1136/
18.Kerkhof HJ, Bierma-Zeinstra SM, Arden NK, et al.
Prediction model for knee osteoarthritis incidence,
including clinical, genetic and biochemical risk
factors. Ann Rheum Dis. 2014;73(12):2116-21. doi:
19.Riddle DL, Stratford PW, Perera RA. The incident
tibiofemoral osteoarthritis with rapid progression
phenotype: development and validation of
a prognostic prediction rule. Osteoarthritis
Cartilage. 2016;24(12):2100-7. doi: 10.1016/j.
20.Fernandes GS, Bhattacharya A, McWilliams DF,
Ingham SL, Doherty M, Zhang W. Risk prediction
model for knee pain in the Nottingham community:
a Bayesian modelling approach. Arthritis Res Ther.
2017;19(1):59. doi: 10.1186/s13075-017-1272-6.
21.Garriga-Fuentes C, Sanchez-Santos MT, Arden N, et al.
Predicting incident radiographic knee osteoarthritis
in middle-aged women within four years: the
importance of knee-level prognostic factors. Arthritis
Care Res (Hoboken) . 2019;72(1). doi: 10.1002/
22.Joseph GB, McCulloch CE, Nevitt MC, et al. Tool for
osteoarthritis risk prediction (TOARP) over 8 years
using baseline clinical data, X-ray, and MRI: Data from
the osteoarthritis initiative. J Magn Reson Imaging.
2018;47(6):1517-26. doi: 10.1002/jmri.25892.
23.Kraus VB, Collins JE, Hargrove D, et al. Predictive
validity of biochemical biomarkers in knee
osteoarthritis: data from the FNIH OA Biomarkers
Consortium. Ann Rheum Dis. 2017;76(1):186-95. doi:
24.LaValley MP, Lo GH, Price LL, Driban JB, Eaton CB,
McAlindon TE. Development of a clinical prediction
algorithm for knee osteoarthritis structural
progression in a cohort study: value of adding
measurement of subchondral bone density. Arthritis
Res Ther. 2017;19(1):1-9. doi: 10.1186/s13075-017-
25.Losina E, Klara K, Michl GL, Collins JE, Katz JN.
Development and feasibility of a personalized,
interactive risk calculator for knee osteoarthritis.
BMC Musculoskelet Disord. 2015;16(1):1-12. doi:
26.van Oudenaarde K, Jobke B, Oostveen AC, et al.
Predictive value of MRI features for development of
radiographic osteoarthritis in a cohort of participants
with pre-radiographic knee osteoarthritis—the
CHECK study. Rheumatology (Oxford). 2017;
56(1):113-120. doi: 10.1093/rheumatology/kew368.
27.Woloszynski T, Podsiadlo P, Stachowiak G,
Kurzynski M, Lohmander L, Englund M. Prediction
of progression of radiographic knee osteoarthritis
using tibial trabecular bone texture. Arthritis Rheum.
2012;64(3):688-95. doi: 10.1002/art.33410.
28.Magnusson K, Turkiewicz A, Timpka S, Englund M.
A Prediction Model for the 40-Year Risk of Knee
Osteoarthritis in Adolescent Men. Arthritis Care
Res (Hoboken). 2019;71(4):558-62. doi: 10.1002/
29.Watt EW, Bui AA. Evaluation of a dynamic bayesian
belief network to predict osteoarthritic knee pain
using data from the osteoarthritis initiative. AMIA
Annu Symp Proc. 2008:788-92.
30.Schett G, Kiechl S, Bonora E, et al. Vascular cell
adhesion molecule 1 as a predictor of severe
osteoarthritis of the hip and knee joints. Arthritis
Rheum. 2009;60(8):2381-9. doi: 10.1002/art.24757.
31.Takahashi H, Nakajima M, Ozaki K, Tanaka T, Kamatani
N, Ikegawa S. Prediction model for knee osteoarthritis
based on genetic and clinical information. Arthritis
Res Ther. 2010;12(5):R187. doi: 10.1186/ar3157.
32.Kinds MB, Marijnissen AC, Vincken KL, et al. Evaluation
of separate quantitative radiographic features adds to
the prediction of incident radiographic osteoarthritis
in individuals with recent onset of knee pain: 5-year
follow-up in the CHECK cohort. Osteoarthritis
Cartilage. 2012;20(6):548-56. doi: 10.1016/j.
33.Yoo TK, Kim DW, Choi SB, Oh E, Park JS. Simple Scoring
System and Artificial Neural Network for Knee
Osteoarthritis Risk Prediction: A Cross-Sectional
Study. PLoS One. 2016;11(2):e0148724. doi: 10.1371/
34.Du Y, Almajalid R, Shan J, Zhang M. A novel method
to predict knee osteoarthritis progression on
MRI using machine learning methods. IEEE Trans
Nanobioscience. 2018;17(3):228-36. doi: 10.1109/
35.Halilaj E, Le Y, Hicks JL, Hastie TJ, Delp SL. Modeling
and predicting osteoarthritis progression: data
from the osteoarthritis initiative. Osteoarthritis
Cartilage. 2018;26(12):1643-50. doi: 10.1016/j.
36.Lim J, Kim J, Cheon S. A deep neural network-based
method for early detection of osteoarthritis using
statistical data. Int J Environ Res Public Health.
2019;16(7):1281. doi: 10.3390/ijerph16071281.
37.Sheng B, Huang L, Wang X, et al. Identification of
Knee Osteoarthritis Based on Bayesian Network:
Pilot Study. JMIR Med Inform. 2019;7(3):e13562. doi:
38.Tiulpin A, Klein S, Bierma-Zeinstra SM, et al. Multimodal machine learning-based knee osteoarthritis progression prediction from plain radiographs
and clinical data. Scie Rep. 2019;9(1):1-11. doi:
39.Zhong H, Miller DJ, Urish KL. T2 map signal variation
predicts symptomatic osteoarthritis progression:
data from the Osteoarthritis Initiative. Skeletal
Radiol. 2016;45(7):909-13. doi: 10.1007/s00256-
40.Lazzarini N, Runhaar J, Bay-Jensen AC, et al. A
machine learning approach for the identification of
new biomarkers for knee osteoarthritis development
in overweight and obese women. Osteoarthritis
Cartilage. 2017;25(12):2014-21. doi: 10.1016/j.
41.Ashinsky BG, Bouhrara M, Coletta CE, et al. Predicting
early symptomatic osteoarthritis in the human knee
using machine learning classification of magnetic
resonance images from the osteoarthritis initiative.
J Orthop Res. 2017;35(10):2243-50. doi: 10.1002/
42.Long MJ, Papi E, Duffell LD, McGregor AH. Predicting
knee osteoarthritis risk in injured populations.
Clin Biomech (Bristol, Avon). 2017;47:87-95. doi:
43.Chen L. Overview of clinical prediction models.
Ann Transl Med. 2020;8(4):71. doi: 10.21037/
44.Collins GS, Reitsma JB, Altman DG, Moons KG.
Transparent Reporting of a Multivariable Prediction
Model for Individual Prognosis or Diagnosis (TRIPOD)
The TRIPOD Statement. Circulation. 2015;131(2):211-
9. doi: 10.1161/CIRCULATIONAHA.114.014508.
45.Zhang L, Lin J, Liu B, Zhang Z, Yan X, Wei M. A review
on deep learning applications in prognostics and
health management. IEEE Access. 2019;7:162415-38.
46.Schmidhuber J. Deep learning in neural networks:
An overview. Neural Netw. 2015;61:85-117. doi:
47.Menashe L, Hirko K, Losina E, et al. The diagnostic
performance of MRI in osteoarthritis: a systematic
review and meta-analysis. Osteoarthritis Cartilage.
2012;20(1):13-21. doi: 10.1016/j.joca.2011.10.003.
48.Guermazi A, Niu J, Hayashi D, et al. Prevalence of
abnormalities in knees detected by MRI in adults
without knee osteoarthritis: population based
observational study (Framingham Osteoarthritis
Study). Bmj. 2012;345:e5339. doi: 10.1136/bmj.
49.Nevitt M, Felson D, Lester G. The Osteoarthritis
Initiative: A knee health study. Protocol for the cohort
study. 2006 Jun:10-3.
50.Shah ND, Steyerberg EW, Kent DM. Big data and
predictive analytics: recalibrating expectations. Jama.
2018;320(1):27-8. doi: 10.1001/jama.2018.5602.
51.Hosner DW, Lemeshow S. Applied logistic regression.
New York: Jhon Wiley & Son. 1989;581.
52.Ayer T, Chhatwal J, Alagoz O, Kahn Jr CE, Woods RW,
Burnside ES. Comparison of logistic regression and
artificial neural network models in breast cancer risk
estimation. Radiographics. 2010;30(1):13-22. doi:
53.Gravesteijn BY, Nieboer D, Ercole A, et al. Machine
learning algorithms performed no better than
regression models for prognostication in traumatic
brain injury. J Clin Epidemiol. 2020;122:95-107. doi:
54.Hayden JA, van der Windt DA, Cartwright JL, Côté P,
Bombardier C. Assessing bias in studies of prognostic
factors. Ann Intern Med. 2013;158(4):280-6. doi:
55.Wolff RF, Moons KG, Riley RD, et al. PROBAST: a tool to
assess the risk of bias and applicability of prediction
model studies. Ann Intern Med. 2019;170(1):51-8.
doi: 10.7326/M18-1376.
56.Bastick AN, Belo JN, Runhaar J, Bierma-Zeinstra SM.
What are the prognostic factors for radiographic
progression of knee osteoarthritis? A meta-analysis.
Clin Orthop Relat Res.2015;473(9):2969-89. doi:
57.Chapple CM, Nicholson H, Baxter GD, Abbott JH.
Patient characteristics that predict progression
of knee osteoarthritis: a systematic review of
prognostic studies. Arthritis Care Res (Hoboken).
2011;63(8):1115-25. doi: 10.1002/acr.20492.
58.Luijken K, Groenwold RH, Van Calster B, Steyerberg
EW, van Smeden M. Impact of predictor measurement
heterogeneity across settings on the performance of
prediction models: A measurement error perspective.
Stat Med. 2019;38(18):3444-59. doi: 10.1002/
59.Landsmeer ML, Runhaar J, van Middelkoop M, et
al. Predicting knee pain and knee osteoarthritis
among overweight women. J Am Board Fam Med.
2019;32(4):575-84. doi: 10.3122/jabfm. 2019.04.