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

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:
10.1001/jama.2013.13805.
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-
6736(12)61729-2.
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/
pnas.1703856114.
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.
joca.2007.06.014.
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/
ard.2008.092015.
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/
s11999-009-1119-9.
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/
archinte.168.13.1430.
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:
10.1007/s11999-008-0399-9.
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.
2009.07.013.
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.
2009.08.010.
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/
ard.2011.149807.
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:
10.1136/annrheumdis-2013-203620.
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.
joca.2016.06.021.
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/
acr.23932.
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:
10.1136/annrheumdis-2016-209252.
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-
1291-3.
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:
10.1186/s12891-015-0771-3.
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/
acr.23685.
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.
joca.2012.02.009.
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/
journal.pone.0148724.
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/
TNB.2018.2840082.
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.
joca.2018.08.003.
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:
10.2196/13562.
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:
10.1038/s41598-019-56527-3.
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-
016-2360-4.
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.
joca.2017.09.001.
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/
jor.23519.
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:
10.1016/j.clinbiomech.2017.06.001.
43.Chen L. Overview of clinical prediction models.
Ann Transl Med. 2020;8(4):71. doi: 10.21037/
atm.2019.11.121.
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:
10.1016/j.neunet.2014.09.003.
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.
e5339.
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:
10.1148/rg.301095057.
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:
10.1016/j.jclinepi.2020.03.005.
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:
10.7326/0003-4819-158-4-201302190-00009.
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:
10.1007/s11999-015-4349-z.
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/
sim.8183.
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.
180302.