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.
ObjectiveTo analyze the changing trends in disease burden of femoral fractures in China from 1990 to 2021, evaluate the impacts of age, period, and cohort effects, and project the age-standardized prevalence rate and age-standardized incidence rates of femoral fractures from 2022 to 2036. MethodsUtilizing open data from the 2021 Global Burden of Disease (GBD) study, this research characterized the disease burden of femoral fractures in China between 1990 and 2021, including trends in incidence, prevalence, and years lived with disability (YLDs). Age-standardized rates were calculated, and Joinpoint regression models were employed to estimate annual percentage changes (APC) and average annual percentage changes (AAPC). An age-period-cohort (APC) model was applied to quantify the effects of age, period, and birth cohort on disease burden. A Bayesian age-period-cohort (BAPC) model was further utilized to project age-standardized prevalence rates and age-standardized incidence rates from 2022 to 2036, with stratified analyses by age, sex, and time period. ResultsFrom 1990 to 2021, age-standardized prevalence (AAPC=0.138 5%), incidence (AAPC=0.294 2%), and YLD rates (AAPC=0.128 3%) exhibited sustained upward trends. Unintentional injuries constituted the predominant etiology of femoral fractures, followed by transport accidents and interpersonal violence/self-harm. In 2021, disease burden escalated with advancing age, with females over 60 years demonstrating significantly higher burdens than males. Age effect coefficients showed a monotonic increase, period effects displayed a U-shaped trajectory (decline followed by rebound), and cohort effects exhibited an inverted U-shaped pattern (rise then decline). Projections indicated continued growth in age-standardized prevalence rates and age-standardized incidence rates through 2036. ConclusionAs the population aging intensifies in China, the disease burden of femoral fractures in our country remains extremely severe. Among them, the elderly female group has become the key focus for prevention and control due to the high prevalence of osteoporosis.
ObjectiveTo systematically review the research status of risk prediction models for cognitive impairment in patients with T2DM. MethodsThe CNKI, WanFang Data, VIP, CBM, PubMed, Embase, Web of Science, Cochrane Library databases and Clinical trial registration platform were electronically searched to collect relevant literature on risk prediction models for cognitive impairment in patients with T2DM from inception to February 13th 2025. Two researchers independently screened the literature, extracted data, and assessed the risk of bias of the included studies, and then qualitative description and meta-analysis was performed. ResultsA total of 20 studies were included, involving 25 risk prediction models. In terms of the risk of bias, 20 studies were considered as high risk. With regards to applicability, 20 studies were high applicability. The pooled area under the curve (AUC) for modeling set was 0.83 (95%CI 0.79 to 0.88) and for the validation set was 0.83 (95% CI 0.79 to 0.87). It suggested that the model had good discrimination ability. The most common predictors included age, education level, duration of diabetes and depression. ConclusionThe overall performance of the risk prediction model for cognitive impairment in patients with T2DM is good, but the quality of the model needs to be improved.