To systematically assess the prevalence of frailty, including prefrailty, stratified prevalence according to frailty criteria, gender, age, and region, and the risk factors for frailty in China.

Aim: Pressure injuries are common adverse events in clinical practice, affecting the well‐being of patients and causing considerable financial burden to healthcare systems. It is therefore essential to use reliable assessment tools to identify pressure injuries for early prevention. The Braden Scale is a widely used tool to assess pressure injury risk, but the literature is currently lacking in determining its accuracy. This study aimed to evaluate the accuracy of the Braden Scale in assessing pressure injury risk.Design: Systematic review and meta‐analysis.Methods: Articles published between 1973–2020 from periodicals indexed in the PubMed, EMBASE, CINAHL, Web of Science and the Cochrane Library were selected. Two reviewers independently selected the relevant studies for inclusion. Data were analysed by the STATA 15.0 and the RevMan 5.3 software.Results: In total, 60 studies involving 49,326 individuals were eligible for this meta‐analysis. The pooled SEN, SPE, PLR, NLR, DOR and AUC were 0.78 (95% CI: 0.74 to 0.82), 0.72 (95% CI: 0.66 to 0.78), 2.80 (95% CI: 2.30 to 3.50), 0.30 (95% CI: 0.26 to 0.35), 9.00 (95% CI: 7.00 to 13.00) and 0.82 (95% CI: 0.79 to 0.85), respectively. Subgroup analyses indicated that the AUC was higher for prospective design (0.84, 95% CI: 0.81 to 0.87), mean age <60 years (0.87, 95% CI: 0.84 to 0.90), hospital (0.82, 95% CI: 0.79 to 0.86) and Caucasian population (0.86, 95% CI: 0.82 to 0.88). In addition, 18 was found to be the optimal cut‐off value.Conclusion: The evidence indicated that the Braden Scale had a moderate predictive validity. It was more suitable for mean age <60 years, hospitalized patients and the Caucasian population, and the cut‐off value of 18 might be used for the risk assessment of pressure injuries in clinical practice. However, due to the different cut‐off values used among included studies, the results had a significant heterogeneity. Future studies should explore the optimal cut‐off value in the same clinical environment.

Background and Purpose: Alzheimer’s disease (AD) is a devastating neurodegenerative disorder with no cure, and available treatments are only able to postpone the progression of the disease. Mild cognitive impairment (MCI) is considered to be a transitional stage preceding AD. Therefore, prediction models for conversion from MCI to AD are desperately required. These will allow early treatment of patients with MCI before they develop AD. This study performed a systematic review and meta-analysis to summarize the reported risk prediction models and identify the most prevalent factors for conversion from MCI to AD.Methods: We systematically reviewed the studies from the databases of PubMed, CINAHL Plus, Web of Science, Embase, and Cochrane Library, which were searched through September 2021. Two reviewers independently identified eligible articles and extracted the data. We used the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies (CHARMS) checklist for the risk of bias assessment.Results: In total, 18 articles describing the prediction models for conversion from MCI to AD were identified. The dementia conversion rate of elderly patients with MCI ranged from 14.49 to 87%. Models in 12 studies were developed using the data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). C-index/area under the receiver operating characteristic curve (AUC) of development models were 0.67–0.98, and the validation models were 0.62–0.96. MRI, apolipoprotein E genotype 4 (APOE4), older age, Mini-Mental State Examination (MMSE) score, and Alzheimer’s Disease Assessment Scale cognitive (ADAS-cog) score were the most common and strongest predictors included in the models.Conclusion: In this systematic review, many prediction models have been developed and have good predictive performance, but the lack of external validation of models limited the extensive application in the general population. In clinical practice, it is recommended that medical professionals adopt a comprehensive forecasting method rather than a single predictive factor to screen patients with a high risk of MCI. Future research should pay attention to the improvement, calibration, and validation of existing models while considering new variables, new methods, and differences in risk profiles across populations.