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A clinical model for predicting knee replacement in early-stage knee osteoarthritis: data from osteoarthritis initiative

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Abstract

Background

Knee osteoarthritis (OA) progresses in a heterogeneous way, as a majority of the patients gradually worsen over decades while some undergo rapid progression and require knee replacement. The aim of this study was to develop a predictive model that enables quantified risk prediction of future knee replacement in patients with early-stage knee OA.

Methods

Patients with early-stage knee OA, intact MRI measurements, and a follow-up time larger than 108 months were retrieved from the Osteoarthritis Initiative database. Twenty-five candidate predictors including demographic data, clinical outcomes, and radiographic parameters were selected. The presence or absence of knee replacement during the first 108 months of the follow-up was regarded as the primary outcome. Patients were randomly divided into derivation and validation groups in the ratio of three to one. Nomograms were developed based on multivariable logistic regressions of derivation group via R language. Those models were further tested in the validation group for external validation.

Results

A total of 839 knees were enrolled, with 98 knees received knee replacement during the first 108 months. Glucocorticoid injection history, knee OA in the contralateral side, extensor muscle strength, area of cartilage deficiency, bone marrow lesion, and meniscus extrusion were selected to develop the nomogram after multivariable logistic regression analysis. The bias-corrected C-index and AUC of our nomogram in the validation group were 0.804 and 0.822, respectively.

Conclusion

Our predicting model provided simplified identification of patients with high risk of rapid progression in knee OA, which showed adequate predictive discrimination and calibration.

Key Points

• Knee OA progresses in a heterogeneous way and rises to a challenge when making treatment strategies.

• Our predicting model provided simplified identification of patients with high risk of rapid progression in knee OA.

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available at https://futurekr.shinyapps.io/DynNomapp/

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Data availability

The data used for analyses in this paper are publicly available at https://nda.nih.gov/oai.

Abbreviations

OA:

Osteoarthritis

KR:

Knee replacement

ME:

Meniscus extrusion

CD:

Cartilage defect

OAI:

Osteoarthritis initiative

BMI:

Body mass index

PASE:

Physical activity scale for the elderly

CCI:

Charlson comorbidity index

ACL:

Anterior cruciate ligament

MOAKS:

MRI Osteoarthritis knee Score

ROC:

Receive-operating characteristic

AUC:

The area under the curve

DCA:

Decision curve analysis

References

  1. Deshpande BR, Katz JN, Solomon DH, Yelin EH, Hunter DJ, Messier SP, Suter LG, Losina E (2016) Number of persons with symptomatic knee osteoarthritis in the US: impact of race and ethnicity, age, sex, and obesity. Arthritis Care Res (Hoboken) 68(12):1743–1750

    Article  Google Scholar 

  2. Hunter DJ, Bierma-Zeinstra S (2019) Osteoarthritis. Lancet 393(10182):1745–1759

    Article  PubMed  CAS  Google Scholar 

  3. Madry H, Kon E, Condello V, Peretti GM, Steinwachs M, Seil R, Berruto M, Engebretsen L, Filardo G, Angele P (2016) Early osteoarthritis of the knee. Knee Surg Sports Traumatol Arthrosc 24(6):1753–1762

    Article  PubMed  Google Scholar 

  4. Riddle DL, Stratford PW, Perera RA (2016) The incident tibiofemoral osteoarthritis with rapid progression phenotype: development and validation of a prognostic prediction rule. Osteoarthritis Cartilage 24(12):2100–2107

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  5. Lansdown DA (2019) Does MRI of knee cartilage help to characterize osteoarthritis severity?: Commentary on an article by Joshua S. Everhart, MD, MPH, et al. “Full-Thickness Cartilage Defects Are Important Independent Predictive Factors for Progression to Total Knee Arthroplasty in Older Adults with Minimal to Moderate Osteoarthritis. Data from the Osteoarthritis Initiative”. J Bone Joint Surg Am 101(1):e4.

  6. Lourido L, Balboa-Barreiro V, Ruiz-Romero C, Rego-Perez I, Camacho-Encina M, Paz-Gonzalez R, Calamia V, Oreiro N, Nilsson P, Blanco FJ (2021) A clinical model including protein biomarkers predicts radiographic knee osteoarthritis: a prospective study using data from the Osteoarthritis Initiative. Osteoarthritis Cartilage 29(8):1147–54.

  7. Conaghan PG, Kloppenburg M, Schett G, Bijlsma JW (2014) Osteoarthritis research priorities: a report from a EULAR ad hoc expert committee. Ann Rheum Dis 73(8):1442–1445

    Article  PubMed  Google Scholar 

  8. Wijn SRW, Rovers MM, van Tienen TG, Hannink G (2020) Intra-articular corticosteroid injections increase the risk of requiring knee arthroplasty. Bone Joint J 102-B(5):586–592

    Article  PubMed  Google Scholar 

  9. Tanamas SK, Wluka AE, Pelletier JP, Pelletier JM, Abram F, Berry PA, Wang Y, Jones G, Cicuttini FM (2010) Bone marrow lesions in people with knee osteoarthritis predict progression of disease and joint replacement: a longitudinal study. Rheumatology (Oxford) 49(12):2413–2419

    Article  Google Scholar 

  10. Roth M, Emmanuel K, Wirth W, Kwoh CK, Hunter DJ, Hannon MJ, Eckstein F (2020) Changes in medial meniscal 3D position and morphology predict knee replacement in rapidly progressing knee osteoarthritis - data from the Osteoarthritis Initiative (OAI). Arthritis Care Res (Hoboken)

  11. Iasonos A, Schrag D, Raj GV, Panageas KS (2008) How to build and interpret a nomogram for cancer prognosis. J Clin Oncol 26(8):1364–1370

    Article  PubMed  Google Scholar 

  12. Yuan K, Chen J, Xu P, Zhang X, Gong X, Wu M, Xie Y, Wang H, Xu G, Liu X (2020) A nomogram for predicting stroke recurrence among young adults. Stroke 51(6):1865–1867

    Article  PubMed  CAS  Google Scholar 

  13. Fu G, Li M, Xue Y, Li Q, Deng Z, Ma Y, Zheng Q (2020) Perioperative patient-specific factors-based nomograms predict short-term periprosthetic bone loss after total hip arthroplasty. J Orthop Surg Res 15(1):503

    Article  PubMed  PubMed Central  Google Scholar 

  14. Emmanuel K, Quinn E, Niu J, Guermazi A, Roemer F, Wirth W, Eckstein F, Felson D (2016) Quantitative measures of meniscus extrusion predict incident radiographic knee osteoarthritis–data from the Osteoarthritis Initiative. Osteoarthritis Cartilage 24(2):262–269

    Article  PubMed  CAS  Google Scholar 

  15. Kohn MD, Sassoon AA, Fernando ND (2016) Classifications in brief: Kellgren-Lawrence classification of osteoarthritis. Clin Orthop Relat Res 474(8):1886–1893

    Article  PubMed  PubMed Central  Google Scholar 

  16. Hunter DJ, Guermazi A, Lo GH, Grainger AJ, Conaghan PG, Boudreau RM, Roemer FW (2011) Evolution of semi-quantitative whole joint assessment of knee OA: MOAKS (MRI Osteoarthritis Knee Score). Osteoarthritis Cartilage 19(8):990–1002

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  17. Moons KG, Altman DG, Reitsma JB, Ioannidis JP, Macaskill P, Steyerberg EW, Vickers AJ, Ransohoff DF, Collins GS (2015) Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): explanation and elaboration. Ann Intern Med 162(1):W1-73

    Article  PubMed  Google Scholar 

  18. Washburn RA, McAuley E, Katula J, Mihalko SL, Boileau RA (1999) The physical activity scale for the elderly (PASE): evidence for validity. J Clin Epidemiol 52(7):643–651

    Article  PubMed  CAS  Google Scholar 

  19. Bellamy N, Buchanan WW, Goldsmith CH, Campbell J, Stitt LW (1988) Validation study of WOMAC: a health status instrument for measuring clinically important patient relevant outcomes to antirheumatic drug therapy in patients with osteoarthritis of the hip or knee. J Rheumatol 15(12):1833–1840

    PubMed  CAS  Google Scholar 

  20. Charlson ME, Pompei P, Ales KL, MacKenzie CR (1987) A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis 40(5):373–383

    Article  PubMed  CAS  Google Scholar 

  21. Kellgren JH, Lawrence JS (1957) Radiological assessment of osteo-arthrosis. Ann Rheum Dis 16(4):494–502

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  22. Hou X, Wang D, Zuo J, Li J, Wang T, Guo C, Peng F, Su D, Zhao L, Ye Z et al (2019) Development and validation of a prognostic nomogram for HIV/AIDS patients who underwent antiretroviral therapy: data from a China population-based cohort. EBioMedicine 48:414–424

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  23. Goltz DE, Ryan SP, Hopkins TJ, Howell CB, Attarian DE, Bolognesi MP, Seyler TM (2019) A novel risk calculator predicts 90-day readmission following total joint arthroplasty. J Bone Joint Surg Am 101(6):547–556

    Article  PubMed  Google Scholar 

  24. Everhart JS, Abouljoud MM, Kirven JC, Flanigan DC (2019) Full-thickness cartilage defects are important independent predictive factors for progression to total knee arthroplasty in older adults with minimal to moderate osteoarthritis: data from the Osteoarthritis Initiative. J Bone Joint Surg Am 101(1):56–63

    Article  PubMed  Google Scholar 

  25. Mahmoudian A, Van Assche D, Herzog W, Luyten FP (2018) Towards secondary prevention of early knee osteoarthritis. RMD Open 4(2):e000468.

  26. Martel-Pelletier J, Barr AJ, Cicuttini FM, Conaghan PG, Cooper C, Goldring MB, Goldring SR, Jones G, Teichtahl AJ, Pelletier JP (2016) Osteoarthritis Nat Rev Dis Primers 2:16072

    Article  PubMed  Google Scholar 

  27. Tammachote N, Kanitnate S, Yakumpor T, Panichkul P (2016) Intra-articular, single-shot hylan G-F 20 hyaluronic acid injection compared with corticosteroid in knee osteoarthritis: a double-blind, randomized controlled trial. J Bone Joint Surg Am 98(11):885–892

    Article  PubMed  Google Scholar 

  28. Bijlsma JW, Berenbaum F, Lafeber FP (2011) Osteoarthritis: an update with relevance for clinical practice. Lancet 377(9783):2115–2126

    Article  PubMed  Google Scholar 

  29. Deveza LA, Downie A, Tamez-Pena JG, Eckstein F, Van Spil WE, Hunter DJ (2019) Trajectories of femorotibial cartilage thickness among persons with or at risk of knee osteoarthritis: development of a prediction model to identify progressors. Osteoarthritis Cartilage 27(2):257–265

    Article  PubMed  CAS  Google Scholar 

  30. Zhang W, McWilliams DF, Ingham SL, Doherty SA, Muthuri S, Muir KR, Doherty M (2011) Nottingham knee osteoarthritis risk prediction models. Ann Rheum Dis 70(9):1599–1604

    Article  PubMed  Google Scholar 

  31. Hafezi-Nejad N, Zikria B, Eng J, Carrino JA, Demehri S (2015) Predictive value of semi-quantitative MRI-based scoring systems for future knee replacement: data from the osteoarthritis initiative. Skeletal Radiol 44(11):1655–1662

    Article  PubMed  Google Scholar 

  32. Eckstein F, Boudreau RM, Wang Z, Hannon MJ, Wirth W, Cotofana S, Guermazi A, Roemer F, Nevitt M, John MR et al (2014) Trajectory of cartilage loss within 4 years of knee replacement–a nested case-control study from the osteoarthritis initiative. Osteoarthritis Cartilage 22(10):1542–1549

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  33. Balachandran VP, Gonen M, Smith JJ, DeMatteo RP (2015) Nomograms in oncology: more than meets the eye. Lancet Oncol 16(4):e173-180

    Article  PubMed  PubMed Central  Google Scholar 

  34. Fu G, Li M, Xue Y, Wang H, Zhang R, Ma Y, Zheng Q (2021) Rapid preoperative predicting tools for 1-year mortality and walking ability of Asian elderly femoral neck fracture patients who planned for hip arthroplasty. J Orthop Surg Res 16(1):455

    Article  PubMed  PubMed Central  Google Scholar 

  35. Hunter DJ (2009) Risk stratification for knee osteoarthritis progression: a narrative review. Osteoarthritis Cartilage 17(11):1402–1407

    Article  PubMed  CAS  Google Scholar 

  36. Bastick AN, Belo JN, Runhaar J, Bierma-Zeinstra SM (2015) What are the prognostic factors for radiographic progression of knee osteoarthritis? A meta-analysis Clin Orthop Relat Res 473(9):2969–2989

    Article  PubMed  Google Scholar 

  37. Chapple CM, Nicholson H, Baxter GD, Abbott JH (2011) Patient characteristics that predict progression of knee osteoarthritis: a systematic review of prognostic studies. Arthritis Care Res (Hoboken) 63(8):1115–1125

    Article  Google Scholar 

  38. Roemer FW, Kwoh CK, Hannon MJ, Hunter DJ, Eckstein F, Wang Z, Boudreau RM, John MR, Nevitt MC, Guermazi A (2015) Can structural joint damage measured with MR imaging be used to predict knee replacement in the following year? Radiology 274(3):810–820

    Article  PubMed  Google Scholar 

  39. Oiestad BE, Juhl CB, Eitzen I, Thorlund JB (2015) Knee extensor muscle weakness is a risk factor for development of knee osteoarthritis. A systematic review and meta-analysis. Osteoarthritis Cartilage 23(2):171–177.

  40. Roemer FW, Nevitt MC, Felson DT, Niu J, Lynch JA, Crema MD, Lewis CE, Torner J, Guermazi A (2012) Predictive validity of within-grade scoring of longitudinal changes of MRI-based cartilage morphology and bone marrow lesion assessment in the tibio-femoral joint–the MOST study. Osteoarthritis Cartilage 20(11):1391–1398

    Article  PubMed  CAS  Google Scholar 

  41. Anis HK, Strnad GJ, Klika AK, Zajichek A, Spindler KP, Barsoum WK, Higuera CA, Piuzzi NS, Cleveland Clinic OMEAG (2020) Developing a personalized outcome prediction tool for knee arthroplasty. Bone Joint J 102-B(9):1183–1193.

  42. Wuerz TH, Kent DM, Malchau H, Rubash HE (2014) A nomogram to predict major complications after hip and knee arthroplasty. J Arthroplasty 29(7):1457–1462

    Article  PubMed  Google Scholar 

  43. Dowsey MM, Spelman T, Choong PF (2016) Development of a prognostic nomogram for predicting the probability of nonresponse to total knee arthroplasty 1 year after surgery. J Arthroplasty 31(8):1654–1660

    Article  PubMed  Google Scholar 

  44. Gronbeck C, Cote MP, Halawi MJ (2019) Predicting inpatient status after primary total knee arthroplasty in Medicare-aged patients. J Arthroplasty 34(7):1322–1327

    Article  PubMed  Google Scholar 

  45. Zhang Z, Rousson V, Lee WC, Ferdynus C, Chen M, Qian X, Guo Y (2018) written on behalf of AMEB-DCTCG: Decision curve analysis: a technical note. Ann Transl Med 6(15):308

    Article  PubMed  PubMed Central  Google Scholar 

  46. van der Voet JA, Runhaar J, van der Plas P, Vroegindeweij D, Oei EH, Bierma-Zeinstra SMA (2017) Baseline meniscal extrusion associated with incident knee osteoarthritis after 30 months in overweight and obese women. Osteoarthritis Cartilage 25(8):1299–1303

    Article  PubMed  Google Scholar 

  47. McAlindon TE, LaValley MP, Harvey WF, Price LL, Driban JB, Zhang M, Ward RJ (2017) Effect of intra-articular triamcinolone vs saline on knee cartilage volume and pain in patients with knee osteoarthritis: a randomized clinical trial. JAMA 317(19):1967–1975

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  48. Roemer FW, Kwoh CK, Fujii T, Hannon MJ, Boudreau RM, Hunter DJ, Eckstein F, John MR, Guermazi A (2018) From early radiographic knee osteoarthritis to joint arthroplasty: determinants of structural progression and symptoms. Arthritis Care Res (Hoboken) 70(12):1778–1786

    Article  Google Scholar 

  49. Fontanella CG, Belluzzi E, Rossato M, Olivotto E, Trisolino G, Ruggieri P, Rubini A, Porzionato A, Natali A, De Caro R et al (2019) Quantitative MRI analysis of infrapatellar and suprapatellar fat pads in normal controls, moderate and end-stage osteoarthritis. Ann Anat 221:108–114

    Article  PubMed  Google Scholar 

  50. Vina ER, Ran D, Ashbeck EL, Ratzlaff C, Kwoh CK (2018) Race, sex, and risk factors in radiographic worsening of knee osteoarthritis. Semin Arthritis Rheum 47(4):464–471

    Article  PubMed  Google Scholar 

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Acknowledgements

The OAI is a public-private partnership comprised of five contracts (N01-AR-2-2258; N01-AR-2-2259; N01-AR-2-2260; N01-AR-2-2261; N01-AR-2-2262) funded by the National Institutes of Health, a branch of the Department of Health and Human Services, and conducted by the OAI Study Investigators. Private funding partners include Merck Research Laboratories; Novartis Pharmaceuticals Corporation, GlaxoSmithKline; and Pfizer, Inc. Private sector funding for the OAI is managed by the Foundation for the National Institutes of Health. This manuscript was prepared using an OAI public use data set and does not necessarily reflect the opinions or views of the OAI investigators, the NIH or the private funding partners.

Funding

This work was supported by Medical Scientific Research Foundation of Guangdong Province of China (A2021310), Project of Administration of Traditional Chinese Medicine of Guangdong Province of China (20211007), Natural Science Foundation of Guangdong Province (2021A1515011008), the Program of Science and Technology of Guangzhou (201904010424), and NSFC Incubation Program of GDPH (KY012021163).

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Author notes

  1. Rongjie Wu, Yuanchen Ma and Yuhui Yang contributed equally to this work.

Authors and Affiliations

  1. Department of Orthopedics, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Yuexiu District, 106, Zhongshan Road, Guangzhou, Guangdong Province, People’s Republic of China

    Rongjie Wu, Yuanchen Ma, Yuhui Yang, Mengyuan Li, Qiujian Zheng & Guangtao Fu

  2. Shantou University Medical College, Shantou, Guangdong Province, People’s Republic of China

    Rongjie Wu

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Contributions

Rongjie Wu: conception, design and drafting of article. Yuanchen Ma and Yuhui Yang: performing analysis. Mengyuan Li: data extraction. Guangtao Fu: revising manuscript content. Qiujian Zheng: approving final version of manuscript. Qiujian Zheng takes responsibility for the integrity of the data analysis.

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Correspondence to Qiujian Zheng or Guangtao Fu.

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Wu, R., Ma, Y., Yang, Y. et al. A clinical model for predicting knee replacement in early-stage knee osteoarthritis: data from osteoarthritis initiative. Clin Rheumatol 41, 1199–1210 (2022). https://doi.org/10.1007/s10067-021-05986-z

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