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 studies 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 were included, involving 21 risk prediction models for KOA. 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.

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.	王欢. 中西医结合治疗膝骨关节炎(膝痹)专家共识. 世界中医药, 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.	王丽敏, 陈泓伯, 鲁寒, 等. 膝关节骨性关节炎疾病负担与疾病风险模型研究进展. 护理研究, 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.	陈香萍, 张奕, 庄一渝, 等. PROBAST: 诊断或预后多因素预测模型研究偏倚风险的评估工具. 中国循证医学杂志, 2020, 20(6): 737-744.
9.	王浩. 膝骨关节炎前瞻性队列构建及发病风险预测模型研究. 银川: 宁夏医科大学, 2023.
10.	邵文娟, 贾潇, 蔡可书, 等. 基于贝叶斯网络的膝骨关节炎患病风险定量预测研究. 北京体育大学学报, 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.	陈鉴冰, 叶倩云, 朱晓峰. 膝骨关节炎致病危险因素的Meta分析. 暨南大学学报(自然科学与医学版), 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, 2023-0479.
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.	刘敏, 李玉茹, 王健, 等. 不同步速条件下超重肥胖老年人步态运动学特征. 中国老年学杂志, 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.	吴煌, 李永忠, 陈亮, 等. 关节镜半月板治疗膝骨关节炎对治疗有效率、VAS评分、HSS评分及TNF-α指标水平的应用研究. 现代医院, 2024, 24(1): 153-155,161.
36.	黄志, 刘瀚霖, 魏垒, 等. 原发性膝骨关节炎合并高血压的临床研究. 实用骨科杂志, 2017, 23(4): 319-322.
37.	鲁小丹, 卫建华, 沈建通, 等. 预测模型系统评价的制作方法与步骤. 中国循证医学杂志, 2023, 23(5): 602-609.
38.	莫航沣, 陈亚萍, 韩慧, 等. 临床预测模型研究方法与步骤. 中国循证医学杂志, 2024, 24(2): 228-236.
39.	曹燚, 曾茜, 赵信飞, 等. 卒中相关性肺炎风险预测模型的系统评价. 中国循证医学杂志, 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. 王欢. 中西医结合治疗膝骨关节炎(膝痹)专家共识. 世界中医药, 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. 王丽敏, 陈泓伯, 鲁寒, 等. 膝关节骨性关节炎疾病负担与疾病风险模型研究进展. 护理研究, 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. 陈香萍, 张奕, 庄一渝, 等. PROBAST: 诊断或预后多因素预测模型研究偏倚风险的评估工具. 中国循证医学杂志, 2020, 20(6): 737-744.
9. 王浩. 膝骨关节炎前瞻性队列构建及发病风险预测模型研究. 银川: 宁夏医科大学, 2023.
10. 邵文娟, 贾潇, 蔡可书, 等. 基于贝叶斯网络的膝骨关节炎患病风险定量预测研究. 北京体育大学学报, 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. 陈鉴冰, 叶倩云, 朱晓峰. 膝骨关节炎致病危险因素的Meta分析. 暨南大学学报(自然科学与医学版), 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, 2023-0479.
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. 刘敏, 李玉茹, 王健, 等. 不同步速条件下超重肥胖老年人步态运动学特征. 中国老年学杂志, 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. 吴煌, 李永忠, 陈亮, 等. 关节镜半月板治疗膝骨关节炎对治疗有效率、VAS评分、HSS评分及TNF-α指标水平的应用研究. 现代医院, 2024, 24(1): 153-155,161.
36. 黄志, 刘瀚霖, 魏垒, 等. 原发性膝骨关节炎合并高血压的临床研究. 实用骨科杂志, 2017, 23(4): 319-322.
37. 鲁小丹, 卫建华, 沈建通, 等. 预测模型系统评价的制作方法与步骤. 中国循证医学杂志, 2023, 23(5): 602-609.
38. 莫航沣, 陈亚萍, 韩慧, 等. 临床预测模型研究方法与步骤. 中国循证医学杂志, 2024, 24(2): 228-236.
39. 曹燚, 曾茜, 赵信飞, 等. 卒中相关性肺炎风险预测模型的系统评价. 中国循证医学杂志, 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.

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