• 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;
MA Yun, Email: 2622644@qq.com
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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

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