Improving the prognosis of knee osteoarthritis

Based on a comprehensive clinical and instrumental examination of 355 patients with osteoarthritis, a scoring system for the course of knee osteoarthritis was improved, which includes the following signs: instability of the knee joint in the sagittal and frontal planes, which is caused by deformation of the tibial condyles or a decrease in the height of the articular cartilage in osteoarthritis, the presence of a rupture of the anterior cruciate ligament or posterior cruciate ligament, the presence of damage to the medial or lateral meniscus. Separately distinguished damage to the root of the internal meniscus with the presence of meniscus extrusion, the volume of movements in the joint, the magnitude of deformation in the frontal plane, the magnitude of the flexion contracture, the presence of a bone defect in the area of the femoral or tibial condyles, the height of the articular cartilage in the area of the medial and lateral condyles of the femur, the time since the onset of the disease, the patient’s age, the results of biochemical research (osteocalcin content, decreased parathyroid hormone levels). Taking into account the scoring of risk factors, a method for predicting the course of gonarthrosis was developed. The prediction was verified by comparing it with the X-ray morphometric data of patients in dynamics. The prediction efficiency for patients with stage 2 gonarthrosis was 88.7%, for patients with stage 3 gonarthrosis — 84.3%. The overall efficiency of the developed prediction method was 86.5%.

References

1. GBD 2021 Osteoarthritis Collaborators (2023) Global, regional, and national burden of osteoarthritis, 1990–2020 and projections to 2050: a systematic analysis for the Global Burden of Disease Study 2021. Lancet Rheumatol., 5(9): e508–e522. doi: 10.1016/S2665-9913(23)00163-7.
2. Li H.Z., Liang X.Z., Sun Y.Q. et al. (2024) Global, regional, and national burdens of osteoarthritis from 1990 to 2021: findings from the 2021 global burden of disease study. Front Med (Lausanne), 11: 1476853. doi: 10.3389/fmed.2024.1476853.
3. Wang Z., Xiao Z., Sun C. et al. (2024) Global, regional and national burden of osteoarthritis in 1990–2021: a systematic analysis of the global burden of disease study 2021. BMC Musculoskelet. Disord., 25(1): 1021. doi: 10.1186/s12891-024-08122-5.
4. Wu R., Guo Y., Chen Y. et al. (2025) Osteoarthritis burden and inequality from 1990 to 2021: a systematic analysis for the global burden of disease Study 2021. Sci. Rep., 15(1): 8305. doi: 10.1038/s41598-025-93124-z.
5. Xie X., Zhang K., Li Y. et al. (2021) Global, regional, and national burden of osteoarthritis from 1990 to 2021 and projections to 2035: A cross-sectional study for the Global Burden of Disease Study. PLoS One, 20(5): e0324296. doi: 10.1371/journal.pone.0324296.
6. Perruccio A.V., Young J.J., Wilfong J.M. et al. (2024) Osteoarthritis year in review 2023: Epidemiology & therapy. Osteoarthritis Cartilage, 32(2): 159–165. doi: 10.1016/j.joca.2023.11.012.
7. Courties A., Kouki I., Soliman N. et al. (2024) Osteoarthritis year in review 2024: Epidemiology and therapy. Osteoarthritis Cartilage, 32(11): 1397–1404. doi: 10.1016/j.joca.2024.07.014.
8. Minnig M.C.C., Golightly Y.M., Nelson A.E. (2024) Epidemiology of osteoarthritis: literature update 2022–2023. Curr. Opin. Rheumatol., 36(2): 108–112. doi: 10.1097/BOR.0000000000000985.
9. Martel-Pelletier J., Paiement P., Pelletier J.P. (2023) Magnetic resonance imaging assessments for knee segmentation and their use in combination with machine/deep learning as predictors of early osteoarthritis diagnosis and prognosis. Ther. Adv. Musculoskelet. Dis., 15: 1759720X231165560. doi: 10.1177/1759720X231165560.
10. Castagno S., Birch M., van der Schaar M. et al. (2025) Predicting rapid progression in knee osteoarthritis: a novel and interpretable automated machine learning approach, with specific focus on young patients and early disease. Ann. Rheum. Dis., 84(1): 124–135. doi: 10.1136/ard-2024-225872.
11. Atasoy-Zeybek A., Showel K.K., Nagelli C.V. et al. (2025) The intersection of aging and estrogen in osteoarthritis. NPJ Womens Health, 3(1): 15. doi: 10.1038/s44294-025-00063-1.
12. Nguyen U.D.T., Saunders F.R., Martin K.R. (2023) Sex Difference in OA: Should We Blame Estrogen? Eur. J. Rheumatol., 11 (Suppl. 1): S7–S14. doi: 10.5152/eurjrheum.2023.20193.
13. Wright V.J., Schwartzman J.D., Itinoche R. et al. (2024) The musculoskeletal syndrome of menopause. Climacteric, 27(5): 466–472. doi: 10.1080/13697137.2024.2380363.
14. Hernandez P.A., Bradford J.C., Brahmachary P. et al. (2024) Unraveling sex-specific risks of knee osteoarthritis before menopause: Do sex differences start early in life? Osteoarthritis Cartilage, 32(9): 1032–1044. doi: 10.1016/j.joca.2024.04.015.
15. Segal N.A., Nilges J.M., Oo W.M. (2024) Sex differences in osteoarthritis prevalence, pain perception, physical function and therapeutics. Osteoarthritis Cartilage, 32(9): 1045–1053. doi: 10.1016/j.joca.2024.04.002.
16. Kreutzinger V., Ziegeler K., Joseph G.B. et al. (2025) Gender-differences in imaging phenotypes of osteoarthritis in the osteoarthritis initiative. Sci. Rep., 15(1): 6219. doi: 10.1038/s41598-025-90782-x.