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To examine the association between cognitive frailty and the risk of future falls among older adults. Systematic review and meta-analysis. Older people aged ≥60 years with cognitive frailty from community, hospital, or both. PubMed, EMBASE, Web of Science, the Cochrane Library, Wanfang Database, China Knowledge Resource Integrated Database (CNKI), Weipu Database (VIP), and Chinese Biomedical Database (CBM) were searched for relevant studies published from the inception of the database until June 14, 2022. Stata 16.0 software was used to perform the meta-analysis. A random effects model was used to pool the prevalence of falls in older adults over age 60 years with cognitive frailty and the strength of the association between cognitive frailty and falls [odds ratios (ORs) and 95% CIs]. Quality assessment, heterogeneity, and sensitivity analyses were also conducted. A study protocol was registered in PROSPERO (CRD42022331323). The review included 18 studies in qualitative synthesis, 14 of which were in meta-analysis. Eleven sets of cross-sectional data involving 23,025 participants and 5 sets of longitudinal data involving 11,924 participants were used in the meta-analysis. The results showed that the overall prevalence of falls in 1742 people with cognitive frailty was 36.3% (95% CI 27.9-44.8, I 2 = 93.4%). Longitudinal study results showed that cognitively frail individuals had a higher risk of falls (OR 3.02, 95% CI 2.11-4.32, I 2 = 0.0%, P =.406), compared to robust participants without cognitive impairment; physically frail people (alone) had a moderate risk of falls (OR 2.16, 95% CI 1.42-3.30, I 2 = 9.7%, P =.351); cognitively impaired people (alone) had a lower risk of falls (OR 1.36, 95% CI 1.03-1.79, I 2 = 0.0%, P =.440). Among cross-sectional studies, cognitive frailty was associated with the risk of falls (OR 2.74, 95% CI 2.20-3.40, I 2 = 53.1%, P =.019). Although high heterogeneity was noted among 11 cross-sectional studies reporting ORs, the sensitivity analysis showed that no single study significantly affected the final pooled results. This systematic review and meta-analysis confirms the findings that cognitive frailty was demonstrated to be a significant predictor of future falls in older adults. However, further prospective investigations are warranted. [ABSTRACT FROM AUTHOR]

• This study provides the latest evidence on the level of life space mobility of community elderly people. • The life space mobility of community elderly people is at a moderate level globally, with 42% of them living in a state of restricted life space. • The level of life space mobility among community elderly people gradually achieved stability after 2017. • South America, females and earlier survey years have a lower level of LSM. Multiple countries have conducted surveys on the level of life space mobility for community-dwelling elderly through the Life-Space Assessment, the results vary greatly, from 41.7 to 88.6. However, there is no meta-analysis on the current situation of community-dwelling elderly life space mobility. To systematically assess the global level of life space mobility for community-dwelling elderly, to identify potential covariates such as geographical regions, survey years, gender, and age that contribute to the heterogeneity between the studies, and to identify the dynamic trend based on survey years. Systematic review and meta-analysis. Two reviewers searched the following 8 electronic bibliographic databases from inception until May 28, 2023: PubMed, The Cochrane Library, Web of Science, Embase, Chinese Biomedical Database, China Knowledge Resource Integrated Database, WanFang, and Weipu Database. This review was conducted using the Stata 14.1 and R 4.3.1. The Cochrane's Q statistical and I2 index were used to test for heterogenicity and assess the degree of heterogenicity, respectively. Studies were appraised using the Agency for Healthcare Research and Quality tool, the Newcastle-Ottawa Scale for the quality of cross-sectional studies, cohort studies, respectively. A total of 29 studies were selected from databases and reference lists. The pooled score of Life-Space Assessment was 66.84 (95% CI: 63.30–70.39) and the prevalence of restricted life space was 42% (95% CI: 0.27–0.57). The geographical regions, survey years, gender were found to be a significant covariate of the pooled score of life space mobility estimate in the subgroup analysis. The mean score of Life-Space Assessment gradually achieved stability after 2017. The life space mobility of community-dwelling elderly in the global is at a moderate level, with 42% of them experiencing restricted life space. South America, females and earlier survey years have a lower level of life space mobility. In the future, the government should identify vulnerable groups for targeted intervention to promote the level of LSM in the community-dwelling elderly. PROSPERO [CRD42023443054]. [ABSTRACT FROM AUTHOR]

To critically appraise and quantify the performance studies by employing machine learning (ML) to predict delirium. A systematic review and meta-analysis. Articles reporting the use of ML to predict delirium in adult patients were included. Studies were excluded if (1) the primary goal was only the identification of various risk factors for delirium; (2) the full-text article was not found; and (3) the article was published in a language other than English/Chinese. PubMed, Embase, Cochrane Library database, Web of Science, Grey literature, and other relevant databases for the related publications were searched (from inception to December 15, 2021). The data were extracted using a standard checklist, and the risk of bias was assessed through the prediction model risk of bias assessment tool. Meta-analysis with the area under the receiver operating characteristic curve, sensitivity, and specificity as effect measures, was performed with Metadisc software. Cochran Q and I 2 statistics were used to assess the heterogeneity. Meta-regression was performed to determine the potential effect of adjustment for the key covariates. A total of 22 studies were included. Only 4 of 22 studies were quantitatively analyzed. The studies varied widely in reporting about the study participants, features and selection, handling of missing data, sample size calculations, and the intended clinical application of the model. For ML models, the overall pooled area under the receiver operating characteristic curve for predicting delirium was 0.89, sensitivity 0.85 (95% confidence interval 0.84‒0.85), and specificity 0.80 (95% confidence interval 0.81–0.80). We found that the ML model showed excellent performance in predicting delirium. This review highlights the potential shortcomings of the current approaches, including low comparability and reproducibility. Finally, we present the various recommendations on how these challenges can be effectively addressed before deploying these models in prospective analyses. [ABSTRACT FROM AUTHOR]