(1) Background: To review the associated factors of sarcopenia in community-dwelling older adults. (2) Methods: PubMed, Embase, Web of Science, and four Chinese electronic databases were searched for observational studies that reported the associated factors of sarcopenia from inception to August 2021. Two researchers independently selected the literature, evaluated their quality, and extracted relevant data. The pooled odds ratio (OR) and its 95% confidence interval (CI) were calculated for each associated factors of sarcopenia using random-effects/fixed-effects models. Publication bias was assessed using funnel plot and the Eggers test. We performed statistical analysis using Stata 15.0 software. (3) Results: A total of 68 studies comprising 98,502 cases were included. Sociodemographic associated factors of sarcopenia among community-dwelling older adults included age (OR = 1.12, 95% CI: 1.10–1.13), marital status (singled, divorced, or widowed) (OR = 1.57, 95% CI: 1.08–2.28), disability for activities of daily living (ADL) (OR = 1.49, 95% CI: 1.15–1.92), and underweight (OR = 3.78, 95% CI: 2.55–5.60). Behavioral associated factors included smoking (OR = 1.20, 95% CI: 1.10–1.21), physical inactivity (OR = 1.73, 95% CI: 1.48–2.01), malnutrition/malnutrition risk (OR = 2.99, 95% CI: 2.40–3.72), long (OR = 2.30, 95% CI: 1.37–3.86) and short (OR = 3.32, 95% CI: 1.86–5.93) sleeping time, and living alone (OR = 1.55, 95% CI: 1.00–2.40). Disease-related associated factors included diabetes (OR = 1.40, 95% CI: 1.18–1.66), cognitive impairment (OR = 1.62, 95% CI: 1.05–2.51), heart diseases (OR = 1.14, 95% CI: 1.00–1.30), respiratory diseases (OR = 1.22, 95% CI: 1.09–1.36), osteopenia/osteoporosis (OR = 2.73, 95% CI: 1.63–4.57), osteoarthritis (OR = 1.33, 95% CI: 1.23–1.44), depression (OR = 1.46, 95% CI: 1.17–1.83), falls (OR = 1.28, 95% CI: 1.14–1.44), anorexia (OR = 1.50, 95% CI: 1.14–1.96), and anemia (OR = 1.39, 95% CI: 1.06–1.82). However, it remained unknown whether gender (female: OR = 1.10, 95% CI: 0.80–1.51; male: OR = 1.50, 95% CI: 0.96–2.34), overweight/obesity (OR = 0.27, 95% CI: 0.17–0.44), drinking (OR = 0.92, 95% CI: 0.84–1.01), hypertension (OR = 0.98, 95% CI: 0.84–1.14), hyperlipidemia (OR = 1.14, 95% CI: 0.89–1.47), stroke (OR = 1.70, 95% CI: 0.69–4.17), cancer (OR = 0.88, 95% CI: 0.85–0.92), pain (OR = 1.08, 95% CI: 0.98–1.20), liver disease (OR = 0.88, 95% CI: 0.85–0.91), and kidney disease (OR = 2.52, 95% CI: 0.19–33.30) were associated with sarcopenia. (4) Conclusions: There are many sociodemographic, behavioral, and disease-related associated factors of sarcopenia in community-dwelling older adults. Our view provides evidence for the early identification of high-risk individuals and the development of relevant interventions to prevent sarcopenia in community-dwelling older adults.

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.

Background: Pressure injury (PI) is a common and preventable problem, yet it is a challenge for at least two reasons. First, the nurse shortage is a worldwide phenomenon. Second, the majority of nurses have insufficient PI-related knowledge. Machine learning (ML) technologies can contribute to lessening the burden on medical staff by improving the prognosis and diagnostic accuracy of PI. To the best of our knowledge, there is no existing systematic review that evaluates how the current ML technologies are being used in PI management. Objective: The objective of this review was to synthesize and evaluate the literature regarding the use of ML technologies in PI management, and identify their strengths and weaknesses, as well as to identify improvement opportunities for future research and practice. Methods: We conducted an extensive search on PubMed, EMBASE, Web of Science, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Cochrane Library, China National Knowledge Infrastructure (CNKI), the Wanfang database, the VIP database, and the China Biomedical Literature Database (CBM) to identify relevant articles. Searches were performed in June 2020. Two independent investigators conducted study selection, data extraction, and quality appraisal. Risk of bias was assessed using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). Results: A total of 32 articles met the inclusion criteria. Twelve of those articles (38%) reported using ML technologies to develop predictive models to identify risk factors, 11 (34%) reported using them in posture detection and recognition, and 9 (28%) reported using them in image analysis for tissue classification and measurement of PI wounds. These articles presented various algorithms and measured outcomes. The overall risk of bias was judged as high. Conclusions: There is an array of emerging ML technologies being used in PI management, and their results in the laboratory show great promise. Future research should apply these technologies on a large scale with clinical data to further verify and improve their effectiveness, as well as to improve the methodological quality.