Knee osteoarthritis (KOA) is one of the common degenerative joint diseases, which is more common in the middle-aged and elderly population. It shows significant gender differences, with a significantly higher incidence rate in women than in men, seriously affecting the quality of life of patients. However, there are few research reports on the correlation between gender differences and the incidence of KOA both domestically and internationally. Therefore, this article will summarize and analyze the potential causes of gender differences related to the incidence of KOA from five aspects: hormone levels, anatomical biomechanical characteristics, genes, obesity, and exercise-muscle factors. Through a comprehensive review of research progress, the aim is to provide a theoretical basis for gender based personalized treatment of KOA in clinical practice.
ObjectiveTo systematically evaluate the risk prediction model of knee osteoarthritis (KOA). MethodsThe 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. ResultsA 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. ConclusionAt 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.