Predictive model for the risk of knee osteoarthritis: a systematic review_Chinese Journal of Evidence-Based Medicine

Authors：

LIU Ling ¹ , LI Huijing ² , ZHANG Zhi ¹ , HU Xiaoshen ³ ,  MA Yun ¹

1. Chengdu Bayi Orthopaedic Hospital, China RongTong Medical Healthcare Group Co.Ltd, Chendu 610012, P. R. China;
2. College of Clinical Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu 610075, P. R. China;
3. School of Health Preservation and Rehabilitation, Chengdu University of Traditional Chinese Medicine, Chengdu 610075, P. R. China;

Corresponding?author：

MA Yun, Email: 2622644@qq.com

Keywords：

Knee osteoarthritis; Prediction models; Systematic review; Risk

DOI：

10.7507/1672-2531.202405081

Video：

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Objective To systematically evaluate the risk prediction model of knee osteoarthritis (KOA). Methods The CNKI, WanFang Data, VIP, PubMed, Embase, Web of Science and Cochrane Library databases were electronically searched to collect relevant studies on KOA’s risk prediction model from inception to April, 2024. After study screening and data extraction by two independent researchers, the PROBAST bias risk assessment tool was used to evaluate the bias risk and applicability of the risk prediction model. Results A total of 12 studies involving 21 risk prediction models for KOA were included. The number of predictors ranged from 3 to 12, and the most common predictors were age, sex, and BMI. The range of modeling AUC included in the model was 0.554-0.948, and the range of testing AUC was 0.6-0.94. The overall predictive performance of the models was mediocre and the risk of overall bias was high, and more than half of the models were not externally verified. Conclusion At present, the overall quality and applicability of the KOA morbidity risk prediction model still have great room for improvement. Future modeling should follow the CHARMS and PROBAST to reduce the risk of bias, explore the combination of multiple modeling methods, and strengthen the external verification of the model.

Citation： LIU Ling, LI Huijing, ZHANG Zhi, HU Xiaoshen, MA Yun. Predictive model for the risk of knee osteoarthritis: a systematic review. Chinese Journal of Evidence-Based Medicine, 2024, 24(9): 1038-1043. doi: 10.7507/1672-2531.202405081 Copy

1.	Zhang ZY, Huang L, Tian L, et al. Home-based vs center-based exercise on patient-reported and performance-based outcomes for knee osteoarthritis: a systematic review with meta-analysis. Front Public Health, 2024, 12: 1360824.
2.	Cui A, Li H, Wang D, et al. Global, regional prevalence, incidence and risk factors of knee osteoarthritis in population-based studies. EClinicalMedicine, 2020, 29-30: 100587.
3.	王歡. 中西醫結合治療膝骨關節炎(膝痹)專家共識. 世界中醫藥, 2023, 18(17): 2407-2412.
4.	Huang Y, Huang H, Chen Q, et al. Efficacy and immune-inflammatory mechanism of acupuncture-related therapy in animal models of knee osteoarthritis: a preclinical systematic review and network meta-analysis. J Orthop Surg Res, 2024, 19(1): 177.
5.	王麗敏, 陳泓伯, 魯寒, 等. 膝關節骨性關節炎疾病負擔與疾病風險模型研究進展. 護理研究, 2020, 34(20): 3642-3646.
6.	Moons KG, de Groot JA, Bouwmeester W, et al. Critical appraisal and data extraction for systematic reviews of prediction modelling studies: the CHARMS checklist. PLoS Med, 2014, 11(10): e1001744.
7.	Moons KGM, Wolff RF, Riley RD, et al. PROBAST: a tool to assess risk of bias and applicability of prediction model studies: explanation and elaboration. Ann Intern Med, 2019, 170(1): W1-W33.
8.	陳香萍, 張奕, 莊一渝, 等. PROBAST: 診斷或預后多因素預測模型研究偏倚風險的評估工具. 中國循證醫學雜志, 2020, 20(6): 737-744.
9.	王浩. 膝骨關節炎前瞻性隊列構建及發病風險預測模型研究. 銀川: 寧夏醫科大學, 2023.
10.	邵文娟, 賈瀟, 蔡可書, 等. 基于貝葉斯網絡的膝骨關節炎患病風險定量預測研究. 北京體育大學學報, 2024, 47(3): 124-132.
11.	McCabe PG, Lisboa P, Baltzopoulos B, et al. Externally validated models for first diagnosis and risk of progression of knee osteoarthritis. PLoS One, 2022, 17(7): e0270652.
12.	Landsmeer MLA, Runhaar J, van Middelkoop M, et al. Predicting knee pain and knee osteoarthritis among overweight women. J Am Board Fam Med, 2019, 32(4): 575-584.
13.	Magnusson K, Turkiewicz A, Timpka S, et al. A prediction model for the 40-year risk of knee osteoarthritis in adolescent men. Arthritis Care Res (Hoboken), 2019, 71(4): 558-562.
14.	Yoo TK, Kim DW, Choi SB, et al. Simple scoring system and artificial neural network for knee osteoarthritis risk prediction: a cross-sectional study. PLoS One, 2016, 11(2): e0148724.
15.	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): 2116-2121.
16.	Zhang W, McWilliams DF, Ingham SL, et al. Nottingham knee osteoarthritis risk prediction models. Ann Rheum Dis, 2011, 70(9): 1599-1604.
17.	Takahashi H, Nakajima M, Ozaki K, et al. Prediction model for knee osteoarthritis based on genetic and clinical information. Arthritis Res Ther, 2010, 12(5): R187.
18.	Wang L, Lu H, Chen H, et al. Development of a model for predicting the 4-year risk of symptomatic knee osteoarthritis in China: a longitudinal cohort study. Arthritis Res Ther, 2021, 23(1): 65.
19.	Garriga C, Sánchez-Santos MT, Judge A, 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), 2020, 72(1): 88-97.
20.	Zhang Q, Yao Y, Chen Y, et al. A retrospective study of biological risk factors associated with primary knee osteoarthritis and the development of a nomogram model. Int J Gen Med, 2024, 17: 1405-1417.
21.	陳鑒冰, 葉倩云, 朱曉峰. 膝骨關節炎致病危險因素的Meta分析. 暨南大學學報(自然科學與醫學版), 2023, 44(4): 406-415.
22.	Chen X, Gong W, Shao X, et al. METTL3-mediated m6A modification of ATG7 regulates autophagy-GATA4 axis to promote cellular senescence and osteoarthritis progression. Ann Rheum Dis, 2022, 81(1): 87-99.
23.	Xie J, Wang Y, Lu L, et al. Cellular senescence in knee osteoarthritis: molecular mechanisms and therapeutic implications. Ageing Res Rev, 2021, 70: 101413.
24.	Ratzlaff CR, Liang MH. New developments in osteoarthritis. Prevention of injury-related knee osteoarthritis: opportunities for the primary and secondary prevention of knee osteoarthritis. Arthritis Res Ther, 2010, 12(4): 215.
25.	Novin S, Alvarez C, Renner JB, et al. Features of knee and multijoint osteoarthritis by sex and race and ethnicity: a preliminary analysis in the Johnston county health study. J Rheumatol, 2023.
26.	Nishino K, Koga H, Koga Y, et al. Association of isometric quadriceps strength with stride and knee kinematics during gait in community dwelling adults with normal knee or early radiographic knee osteoarthritis. Clin Biomech (Bristol, Avon), 2021, 84: 105325.
27.	Culvenor AG, Ruhdorfer A, Juhl C, et al. Knee extensor strength and risk of structural, symptomatic, and functional decline in knee osteoarthritis: a systematic review and meta-analysis. Arthritis Care Res (Hoboken), 2017, 69(5): 649-658.
28.	Herrero-Beaumont G, Roman-Blas JA, Mediero A, et al. Treating osteoporotic osteoarthritis, or the art of cutting a balding man's hair. Osteoarthritis Cartilage, 2020, 28(3): 239-241.
29.	Huang G, Hong W, Wang K, et al. Causal analysis of body composition measurements in osteoarthritis knee: a two-sample Mendelian randomization study. BMC Musculoskelet Disord, 2024, 25(1): 341.
30.	Zheng H, Chen C. Body mass index and risk of knee osteoarthritis: systematic review and meta-analysis of prospective studies. BMJ Open, 2015, 5(12): e007568.
31.	Zhou ZY, Liu YK, Chen HL, et al. Body mass index and knee osteoarthritis risk: a dose-response meta-analysis. Obesity (Silver Spring), 2014, 22(10): 2180-2185.
32.	Goto K, Imaoka M, Goto M, et al. Effect of body-weight loading onto the articular cartilage on the occurrence of quinolone-induced chondrotoxicity in juvenile rats. Toxicol Lett, 2013, 216(2-3): 124-129.
33.	劉敏, 李玉茹, 王健, 等. 不同步速條件下超重肥胖老年人步態運動學特征. 中國老年學雜志, 2022, 42(13): 3216-3220.
34.	Muthuri SG, McWilliams DF, Doherty M, et al. History of knee injuries and knee osteoarthritis: a meta-analysis of observational studies. Osteoarthritis Cartilage, 2011, 19(11): 1286-1293.
35.	吳煌, 李永忠, 陳亮, 等. 關節鏡半月板治療膝骨關節炎對治療有效率、VAS評分、HSS評分及TNF-α指標水平的應用研究. 現代醫院, 2024, 24(1): 153-155, 161.
36.	黃志, 劉瀚霖, 魏壘, 等. 原發性膝骨關節炎合并高血壓的臨床研究. 實用骨科雜志, 2017, 23(4): 319-322.
37.	魯小丹, 衛建華, 沈建通, 等. 預測模型系統評價的制作方法與步驟. 中國循證醫學雜志, 2023, 23(5): 602-609.
38.	莫航灃, 陳亞萍, 韓慧, 等. 臨床預測模型研究方法與步驟. 中國循證醫學雜志, 2024, 24(2): 228-236.
39.	曹燚, 曾茜, 趙信飛, 等. 卒中相關性肺炎風險預測模型的系統評價. 中國循證醫學雜志, 2023, 23(11): 1259-1268.
40.	Kokkotis C, Moustakidis S, Papageorgiou E, et al. Machine learning in knee osteoarthritis: a review. Osteoarthr Cartil Open, 2020, 2(3): 100069.

1. Zhang ZY, Huang L, Tian L, et al. Home-based vs center-based exercise on patient-reported and performance-based outcomes for knee osteoarthritis: a systematic review with meta-analysis. Front Public Health, 2024, 12: 1360824.
2. Cui A, Li H, Wang D, et al. Global, regional prevalence, incidence and risk factors of knee osteoarthritis in population-based studies. EClinicalMedicine, 2020, 29-30: 100587.
3. 王歡. 中西醫結合治療膝骨關節炎(膝痹)專家共識. 世界中醫藥, 2023, 18(17): 2407-2412.
4. Huang Y, Huang H, Chen Q, et al. Efficacy and immune-inflammatory mechanism of acupuncture-related therapy in animal models of knee osteoarthritis: a preclinical systematic review and network meta-analysis. J Orthop Surg Res, 2024, 19(1): 177.
5. 王麗敏, 陳泓伯, 魯寒, 等. 膝關節骨性關節炎疾病負擔與疾病風險模型研究進展. 護理研究, 2020, 34(20): 3642-3646.
6. Moons KG, de Groot JA, Bouwmeester W, et al. Critical appraisal and data extraction for systematic reviews of prediction modelling studies: the CHARMS checklist. PLoS Med, 2014, 11(10): e1001744.
7. Moons KGM, Wolff RF, Riley RD, et al. PROBAST: a tool to assess risk of bias and applicability of prediction model studies: explanation and elaboration. Ann Intern Med, 2019, 170(1): W1-W33.
8. 陳香萍, 張奕, 莊一渝, 等. PROBAST: 診斷或預后多因素預測模型研究偏倚風險的評估工具. 中國循證醫學雜志, 2020, 20(6): 737-744.
9. 王浩. 膝骨關節炎前瞻性隊列構建及發病風險預測模型研究. 銀川: 寧夏醫科大學, 2023.
10. 邵文娟, 賈瀟, 蔡可書, 等. 基于貝葉斯網絡的膝骨關節炎患病風險定量預測研究. 北京體育大學學報, 2024, 47(3): 124-132.
11. McCabe PG, Lisboa P, Baltzopoulos B, et al. Externally validated models for first diagnosis and risk of progression of knee osteoarthritis. PLoS One, 2022, 17(7): e0270652.
12. Landsmeer MLA, Runhaar J, van Middelkoop M, et al. Predicting knee pain and knee osteoarthritis among overweight women. J Am Board Fam Med, 2019, 32(4): 575-584.
13. Magnusson K, Turkiewicz A, Timpka S, et al. A prediction model for the 40-year risk of knee osteoarthritis in adolescent men. Arthritis Care Res (Hoboken), 2019, 71(4): 558-562.
14. Yoo TK, Kim DW, Choi SB, et al. Simple scoring system and artificial neural network for knee osteoarthritis risk prediction: a cross-sectional study. PLoS One, 2016, 11(2): e0148724.
15. 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): 2116-2121.
16. Zhang W, McWilliams DF, Ingham SL, et al. Nottingham knee osteoarthritis risk prediction models. Ann Rheum Dis, 2011, 70(9): 1599-1604.
17. Takahashi H, Nakajima M, Ozaki K, et al. Prediction model for knee osteoarthritis based on genetic and clinical information. Arthritis Res Ther, 2010, 12(5): R187.
18. Wang L, Lu H, Chen H, et al. Development of a model for predicting the 4-year risk of symptomatic knee osteoarthritis in China: a longitudinal cohort study. Arthritis Res Ther, 2021, 23(1): 65.
19. Garriga C, Sánchez-Santos MT, Judge A, 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), 2020, 72(1): 88-97.
20. Zhang Q, Yao Y, Chen Y, et al. A retrospective study of biological risk factors associated with primary knee osteoarthritis and the development of a nomogram model. Int J Gen Med, 2024, 17: 1405-1417.
21. 陳鑒冰, 葉倩云, 朱曉峰. 膝骨關節炎致病危險因素的Meta分析. 暨南大學學報(自然科學與醫學版), 2023, 44(4): 406-415.
22. Chen X, Gong W, Shao X, et al. METTL3-mediated m6A modification of ATG7 regulates autophagy-GATA4 axis to promote cellular senescence and osteoarthritis progression. Ann Rheum Dis, 2022, 81(1): 87-99.
23. Xie J, Wang Y, Lu L, et al. Cellular senescence in knee osteoarthritis: molecular mechanisms and therapeutic implications. Ageing Res Rev, 2021, 70: 101413.
24. Ratzlaff CR, Liang MH. New developments in osteoarthritis. Prevention of injury-related knee osteoarthritis: opportunities for the primary and secondary prevention of knee osteoarthritis. Arthritis Res Ther, 2010, 12(4): 215.
25. Novin S, Alvarez C, Renner JB, et al. Features of knee and multijoint osteoarthritis by sex and race and ethnicity: a preliminary analysis in the Johnston county health study. J Rheumatol, 2023.
26. Nishino K, Koga H, Koga Y, et al. Association of isometric quadriceps strength with stride and knee kinematics during gait in community dwelling adults with normal knee or early radiographic knee osteoarthritis. Clin Biomech (Bristol, Avon), 2021, 84: 105325.
27. Culvenor AG, Ruhdorfer A, Juhl C, et al. Knee extensor strength and risk of structural, symptomatic, and functional decline in knee osteoarthritis: a systematic review and meta-analysis. Arthritis Care Res (Hoboken), 2017, 69(5): 649-658.
28. Herrero-Beaumont G, Roman-Blas JA, Mediero A, et al. Treating osteoporotic osteoarthritis, or the art of cutting a balding man's hair. Osteoarthritis Cartilage, 2020, 28(3): 239-241.
29. Huang G, Hong W, Wang K, et al. Causal analysis of body composition measurements in osteoarthritis knee: a two-sample Mendelian randomization study. BMC Musculoskelet Disord, 2024, 25(1): 341.
30. Zheng H, Chen C. Body mass index and risk of knee osteoarthritis: systematic review and meta-analysis of prospective studies. BMJ Open, 2015, 5(12): e007568.
31. Zhou ZY, Liu YK, Chen HL, et al. Body mass index and knee osteoarthritis risk: a dose-response meta-analysis. Obesity (Silver Spring), 2014, 22(10): 2180-2185.
32. Goto K, Imaoka M, Goto M, et al. Effect of body-weight loading onto the articular cartilage on the occurrence of quinolone-induced chondrotoxicity in juvenile rats. Toxicol Lett, 2013, 216(2-3): 124-129.
33. 劉敏, 李玉茹, 王健, 等. 不同步速條件下超重肥胖老年人步態運動學特征. 中國老年學雜志, 2022, 42(13): 3216-3220.
34. Muthuri SG, McWilliams DF, Doherty M, et al. History of knee injuries and knee osteoarthritis: a meta-analysis of observational studies. Osteoarthritis Cartilage, 2011, 19(11): 1286-1293.
35. 吳煌, 李永忠, 陳亮, 等. 關節鏡半月板治療膝骨關節炎對治療有效率、VAS評分、HSS評分及TNF-α指標水平的應用研究. 現代醫院, 2024, 24(1): 153-155, 161.
36. 黃志, 劉瀚霖, 魏壘, 等. 原發性膝骨關節炎合并高血壓的臨床研究. 實用骨科雜志, 2017, 23(4): 319-322.
37. 魯小丹, 衛建華, 沈建通, 等. 預測模型系統評價的制作方法與步驟. 中國循證醫學雜志, 2023, 23(5): 602-609.
38. 莫航灃, 陳亞萍, 韓慧, 等. 臨床預測模型研究方法與步驟. 中國循證醫學雜志, 2024, 24(2): 228-236.
39. 曹燚, 曾茜, 趙信飛, 等. 卒中相關性肺炎風險預測模型的系統評價. 中國循證醫學雜志, 2023, 23(11): 1259-1268.
40. Kokkotis C, Moustakidis S, Papageorgiou E, et al. Machine learning in knee osteoarthritis: a review. Osteoarthr Cartil Open, 2020, 2(3): 100069.

Chinese Journal of Evidence-Based Medicine

Predictive model for the risk of knee osteoarthritis: a systematic review

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