Do Knee Pain Phenotypes Have Different Risks of Total Knee Replacement?
Abstract
:1. Introduction
2. Materials and Methods
2.1. Participants
2.2. Measurements for Factors to Identify Pain Phenotypes
2.2.1. Knee Structural Abnormalities on MRI
2.2.2. Emotional Problems
2.2.3. Number of Painful Sites
2.2.4. Other Pain-Related Factors
2.3. Measurement for Outcome
Total Knee Replacement
2.4. Measurements for Other Related Factors
2.5. Statistical Analysis
3. Results
3.1. Participants Included for Identifying Pain Phenotypes
3.2. Participants’ Characteristics across Pain Phenotypes
3.3. First-Time TKR Due to OA
4. Discussion
Supplementary Materials
Author Contributions
Funding
Conflicts of Interest
References
- Hunter, D.J.; Bierma-Zeinstra, S. Osteoarthritis. Lancet 2019, 393, 1745–1759. [Google Scholar] [CrossRef]
- Cross, M.; Smith, E.; Hoy, D.G.; Nolte, S.; Ackerman, I.N.; Fransen, M.; Bridgett, L.; Williams, S.; Guillemin, F.; Hill, C.L.; et al. The global burden of hip and knee osteoarthritis: Estimates from the Global Burden of Disease 2010 study. Ann. Rheum. Dis. 2014, 73, 1323–1330. [Google Scholar] [CrossRef]
- Ferket, B.; Feldman, Z.; Zhou, J.; Oei, E.H.; Bierma-Zeinstra, S.M.; Mazumdar, M. Impact of total knee replacement practice: Cost effectiveness analysis of data from the Osteoarthritis Initiative. BMJ 2017, 356, 1131. [Google Scholar] [CrossRef] [Green Version]
- Beswick, A.; Wylde, V.; Gooberman-Hill, R.; Blom, A.; Dieppe, P. What proportion of patients report long-term pain after total hip or knee replacement for osteoarthritis? A systematic review of prospective studies in unselected patients. BMJ Open 2012, 2, e000435. [Google Scholar] [CrossRef]
- Thomas, E.; Peat, G.; Croft, P. Defining and mapping the person with osteoarthritis for population studies and public health. Rheumatology 2013, 53, 338–345. [Google Scholar] [CrossRef] [Green Version]
- Brandt, K.D.; Heilman, D.K.; Slemenda, C.; Katz, B.P.; Mazzuca, S.A.; Braunstein, E.M.; Byrd, D. Quadriceps strength in women with radiographically progressive osteoarthritis of the knee and those with stable radiographic changes. J. Rheumatol. 1999, 26, 2431–2437. [Google Scholar]
- Dell’Isola, A.; Allan, R.; Smith, S.L.; Marreiros, S.S.; Steultjens, M. Identification of clinical phenotypes in knee osteoarthritis: A systematic review of the literature. BMC Musculoskelet. Disord. 2016, 17, 425. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Deveza, L.A.; Loeser, R.F. Is osteoarthritis one disease or a collection of many? Rheumatology 2017, 57, iv34–iv42. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Deveza, L.A.; Melo, L.; Yamato, T.P.; Mills, K.; Ravi, V.; Hunter, D.J. Knee osteoarthritis phenotypes and their relevance for outcomes: A systematic review. Osteoarthr. Cartil. 2017, 25, 1926–1941. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Kittelson, A.J.; George, S.Z.; Maluf, K.S.; Stevens-Lapsley, J.E. Future Directions in Painful Knee Osteoarthritis: Harnessing Complexity in a Heterogeneous Population. Phys. Ther. 2014, 94, 422–432. [Google Scholar] [CrossRef] [Green Version]
- Knoop, J.; Van Der Leeden, M.; Thorstensson, C.A.; Roorda, L.D.; Lems, W.F.; Knol, D.L.; Steultjens, M.P.M.; Dekker, J. Identification of phenotypes with different clinical outcomes in knee osteoarthritis: Data from the osteoarthritis initiative. Arthritis Rheum. 2011, 63, 1535–1542. [Google Scholar] [CrossRef] [PubMed]
- Murphy, S.L.; Lyden, A.K.; Phillips, K.; Clauw, D.J.; Williams, D.A. Subgroups of older adults with osteoarthritis based upon differing comorbid symptom presentations and potential underlying pain mechanisms. Arthritis Res. Ther. 2011, 13, R135. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Cruz-Almeida, Y.; King, C.D.; Goodin, B.R.; Sibille, K.T.; Glover, T.L.; Riley, J.L.; Sotolongo, A.; Herbert, M.S.; Schmidt, J.; Fessler, B.J.; et al. Psychological profiles and pain characteristics of older adults with knee osteoarthritis. Arthritis Rheum. 2013, 65, 1786–1794. [Google Scholar] [CrossRef] [PubMed]
- van der Esch, M.; Knoop, J.; van der Leeden, M.; Roorda, L.D.; Lems, W.F.; Knol, D.L.; Dekker, J. Clinical phenotypes in patients with knee osteoarthritis: A study in the Amsterdam osteoarthritis cohort. Osteoarthr. Cartil. 2015, 23, 544–549. [Google Scholar] [CrossRef] [Green Version]
- Kittelson, A.J.; Stevens-Lapsley, J.; Schmiege, S.J. Determination of Pain Phenotypes in Knee Osteoarthritis: A Latent Class Analysis Using Data From the Osteoarthritis Initiative. Arthritis Rheum. 2016, 68, 612–620. [Google Scholar] [CrossRef] [Green Version]
- Pan, F.; Tian, J.; Cicuttini, F.; Jones, G.; Aitken, D. Differentiating knee pain phenotypes in older adults: A prospective cohort study. Rheumatology 2018, 58, 274–283. [Google Scholar] [CrossRef]
- Adie, S.; Harris, I.; Chuan, A.; Lewis, P.; Naylor, J.M. Selecting and optimising patients for total knee arthroplasty. Med. J. Aust. 2019, 210, 135–141. [Google Scholar] [CrossRef] [PubMed]
- Doré, D.A.; Winzenberg, T.; Ding, C.; Otahal, P.; Pelletier, J.-P.; Martel-Pelletier, J.; Cicuttini, F.M.; Jones, G. The association between objectively measured physical activity and knee structural change using MRI. Ann. Rheum. Dis. 2012, 72, 1170–1175. [Google Scholar] [CrossRef]
- Ding, C.; Garnero, P.; Cicuttini, F.; Scott, F.; Cooley, H.; Jones, G. Knee cartilage defects: Association with early radiographic osteoarthritis, decreased cartilage volume, increased joint surface area and type II collagen breakdown. Osteoarthr. Cartil. 2005, 13, 198–205. [Google Scholar] [CrossRef] [Green Version]
- Drapé, J.-L.; Pessis, E.; Auleley, G.R.; Chevrot, A.; Dougados, M.; Ayral, X. Quantitative MR imaging evaluation of chondropathy in osteoarthritic knees. Radiology 1998, 208, 49–55. [Google Scholar] [CrossRef]
- Ding, C.; Cicuttini, F.; Scott, F.; Cooley, H.; Boon, C.; Jones, G. Natural History of Knee Cartilage Defects and Factors Affecting Change. Arch. Intern. Med. 2006, 166, 651–658. [Google Scholar] [CrossRef]
- Doré, D.A.; Quinn, S.; Ding, C.; Winzenberg, T.; Zhai, G.; Cicuttini, F.; Jones, G. Natural history and clinical significance of MRI-detected bone marrow lesions at the knee: A prospective study in community dwelling older adults. Arthritis Res. Ther. 2010, 12, R223. [Google Scholar] [CrossRef] [Green Version]
- Zhai, G.; Blizzard, L.; Srikanth, V.; Ding, C.; Cooley, H.; Cicuttini, F.; Jones, G. Correlates of knee pain in older adults: Tasmanian older adult cohort study. Arthritis Rheum. 2006, 55, 264–271. [Google Scholar] [CrossRef] [PubMed]
- Wang, X.; Blizzard, L.; Halliday, A.; Han, W.; Jin, X.; Cicuttini, F.; Jones, G.; Ding, C. Association between MRI-detected knee joint regional effusion-synovitis and structural changes in older adults: A cohort study. Ann. Rheum. Dis. 2014, 75, 519–525. [Google Scholar] [CrossRef] [PubMed]
- Roemer, F.; Guermazi, A.; Felson, D.; Niu, J.; Nevitt, M.C.; Crema, M.D.; Lynch, J.A.; Lewis, C.E.; Torner, J.; Zhang, Y. Presence of MRI-detected joint effusion and synovitis increases the risk of cartilage loss in knees without osteoarthritis at 30-month follow-up: The MOST study. Ann. Rheum. Dis. 2011, 70, 1804–1809. [Google Scholar] [CrossRef] [PubMed]
- Altman, R.D.; Hochberg, M.; Murphy, W.A.; Wolfe, F.; LeQuesne, M. Atlas of individual radiographic features in osteoarthritis. Osteoarthr. Cartil. 1995, 3, 3–70. [Google Scholar] [CrossRef] [Green Version]
- Altman, R.; Gold, G. Atlas of individual radiographic features in osteoarthritis, revised. Osteoarthr. Cartil. 2007, 15, A1–A56. [Google Scholar] [CrossRef] [Green Version]
- Nylund, K.L.; Asparouhov, T.; Muthén, B. Deciding on the Number of Classes in Latent Class Analysis and Growth Mixture Modeling: A Monte Carlo Simulation Study. Struct. Equ. Model. Multidiscip. J. 2007, 14, 535–569. [Google Scholar] [CrossRef]
- Lanza, S.T.; Dziak, J.J.; Huang, L.; Wagner, A.T.; Collins, L.M.; Lanza, S. LCA Stata Plugin Users’ Guide; Version 1.2; The Methodology Center: University Park, PA, USA, 2015. [Google Scholar]
- Wylde, V.; Trela-Larsen, L.; Whitehouse, M.R.; Blom, A.W. Preoperative psychosocial risk factors for poor outcomes at 1 and 5 years after total knee replacement. Acta Orthop. 2017, 88, 530–536. [Google Scholar] [CrossRef] [Green Version]
- Bletterman, A.N.; De Geest-Vrolijk, M.E.; Vriezekolk, J.E.; Der Sanden, M.W.N.-V.; Van Meeteren, N.L.; Hoogeboom, T. Preoperative psychosocial factors predicting patient’s functional recovery after total knee or total hip arthroplasty: A systematic review. Clin. Rehabil. 2017, 32, 512–525. [Google Scholar] [CrossRef]
- Khatib, Y.; Madan, A.; Naylor, J.M.; Harris, I.A. Do Psychological Factors Predict Poor Outcome in Patients Undergoing TKA? A Systematic Review. Clin. Orthop. Relat. Res. 2015, 473, 2630–2638. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Huynh, C.; Puyraimond-Zemmour, D.; Maillefert, J.F.; Conaghan, P.G.; Davis, A.M.; Gunther, K.P.; Hawker, G.; Hochberg, M.C.; Kloppenburg, M.; Lim, K.; et al. Factors associated with the orthopaedic surgeon’s decision to recommend total joint replacement in hip and knee osteoarthritis: An international cross-sectional study of 1905 patients. Osteoarthr. Cartil. 2018, 26, 1311–1318. [Google Scholar] [CrossRef] [PubMed] [Green Version]
Characteristics | Class 1 (n = 236) | Class 2 (n = 193) | Class 3 (n = 534) | p† C1 vs. C2 | p† C1 vs. C3 | p† C2 vs. C3 |
---|---|---|---|---|---|---|
Age (years) | 63.1 ± 7.7 | 64.4 ± 7.4 | 62.1 ± 7.2 | 0.217 | 0.242 | 0.001 |
Female sex, n (%) | 152 (64) | 66 (34) | 267 (50) | <0.001 | <0.001 | <0.001 |
BMI (kg/m2) | 29.0 ± 5.4 | 29.4 ± 4.5 | 26.5 ± 3.9 | 0.869 | <0.001 | <0.001 |
Presence of emotional problems, n (%) | 218 (92) | 97 (50) | 298 (56) | <0.001 | <0.001 | 0.040 |
Emotional problems, n (%) | <0.001 | <0.001 | 0.003 | |||
Not at all | 18 (8) | 96 (50) | 236 (44) | |||
Very little | 94 (40) | 69 (36) | 185 (35) | |||
Moderately | 86 (36) | 22 (11) | 84 (16) | |||
Quite a lot | 35 (14) | 5 (3) | 27 (5) | |||
Extremely | 4 (2) | 1 (1) | 2 (0.4) | |||
Education level, n (%) | <0.001 | <0.001 | <0.001 | |||
School only | 160 (68) | 77 (40) | 296 (55) | |||
Vocation training | 74 (31) | 77 (40) | 164 (31) | |||
University or higher | 2 (1) | 39 (20) | 74 (14) | |||
Presence of any comorbidity, n (%) | 162 (69) | 98 (51) | 198 (37) | <0.001 | <0.001 | <0.001 |
WOMAC pain score (0–45) | 8.6 ± 8.6 | 3.2 ± 5.6 | 1.5 ± 3.0 | <0.001 | <0.001 | 0.001 |
Number of painful sites (0–7) | 5.8 ± 1.1 | 2.4 ± 1.5 | 2.2 ± 1.7 | <0.001 | <0.001 | 0.265 |
Presence of radiographic knee OA, n (%) | 156 (66) | 131 (67) | 290 (54) | 0.791 | 0.004 | 0.004 |
Knee structural pathology, n (%) | ||||||
Cartilage defects | 88 (37) | 172 (89) | 60 (11) | <0.001 | <0.001 | <0.001 |
BMLs | 82 (35) | 161 (83) | 101 (19) | <0.001 | <0.001 | <0.001 |
Effusion-synovitis | 102 (43) | 125 (65) | 184 (34) | <0.001 | 0.026 | <0.001 |
Class 1 (n = 236) | Class 2 (n = 193) | Class 3 (n = 534) | p * C1 vs. C2 | p * C1 vs. C3 | p * C2 vs. C3 | |
---|---|---|---|---|---|---|
Mean time to first-time TKR, years | ||||||
Right knee | 8.2 ± 3.1 | 6.4 ± 3.8 | 8.9 ± 0.9 | 0.385 | 1.000 | 0.722 |
Left knee | 7.5 ± 3.8 | 7.4 ± 3.8 | 10.0 ± 3.2 | 1.000 | 0.328 | 0.248 |
Any knee | 7.7 ± 3.5 | 7.1 ± 3.9 | 9.7 ± 2.9 | 1.000 | 0.419 | 0.133 |
Number of TKR, n (%) | ||||||
Right knee | 15 (6) | 23 (12) | 3 (1) | 0.047 | <0.001 | <0.001 |
Left knee | 14 (6) | 21 (11) | 9 (2) | 0.066 | 0.003 | <0.001 |
Any knee | 22 (9) | 34 (18) | 11 (2) | 0.012 | <0.001 | <0.001 |
Class 1 vs. Class 3 | Class 2 vs. Class 3 | Class 2 vs. Class 1 | ||||
---|---|---|---|---|---|---|
Univariable HR(95%CI) | Multivariable * HR(95%CI) | Univariable HR(95%CI) | Multivariable * HR(95%CI) | Univariable HR(95%CI) | Multivariable * HR(95%CI) | |
Any | 4.87 (2.36, 10.05) | 4.81 (2.33, 9.93) | 9.59 (4.86, 18.93) | 9.23 (4.66, 18.30) | 1.97 (1.15, 3.37) | 1.92 (1.12, 3.29) |
Right | 11.93 (3.45, 41.21) | 11.96 (3.46, 41.34) | 23.07 (6.93, 76.88) | 23.26 (6.95, 77.77) | 1.93 (1.01, 3.71) | 1.94 (1.01, 3.74) |
Left | 3.72 (1.61, 8.62) | 3.63 (1.57, 8.40) | 6.90 (3.16, 15.08) | 6.42 (2.93, 14.09) | 1.85 (0.94, 3.64) | 1.77 (0.90, 3.48) |
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Pan, F.; Tian, J.; Munugoda, I.P.; Graves, S.; Lorimer, M.; Cicuttini, F.; Jones, G. Do Knee Pain Phenotypes Have Different Risks of Total Knee Replacement? J. Clin. Med. 2020, 9, 632. https://doi.org/10.3390/jcm9030632
Pan F, Tian J, Munugoda IP, Graves S, Lorimer M, Cicuttini F, Jones G. Do Knee Pain Phenotypes Have Different Risks of Total Knee Replacement? Journal of Clinical Medicine. 2020; 9(3):632. https://doi.org/10.3390/jcm9030632
Chicago/Turabian StylePan, Feng, Jing Tian, Ishanka P. Munugoda, Stephen Graves, Michelle Lorimer, Flavia Cicuttini, and Graeme Jones. 2020. "Do Knee Pain Phenotypes Have Different Risks of Total Knee Replacement?" Journal of Clinical Medicine 9, no. 3: 632. https://doi.org/10.3390/jcm9030632