Frailty Is a Risk Factor for Falls in the Older Adults: A Systematic Review and Meta-Analysis.

OBJECTIVES: There is little evidence in the literature about the relationship between frailty and falls in older adults. Our objective was to explore the relationship between frailty and falls, and to analyze the effect factors (e.g., gender, different frailty assessment tools, areas, level of national economic development, and year of publication) of the association between frailty and falls among older adults. DESIGN: Systematic review and meta-analysis. SETTING AND PARTICIPANTS: Cohort studies that evaluated the association between frailty and falls in the older adults were included. We excluded any literature outside of cohort studies. METHODS: We did a systematic literature search of English databases PubMed, Scopus, Web of Science, EBSCOhost, and SciElO, as well as the Chinese databases CNKI, WANFANG, and VIP from 2001 until October 2022. The eligible studies were evaluated for potential bias using the Newcastle-Ottawa Scale (NOS). Study selection, data extraction and assessment of study quality were each conducted by two investigators. In Stata/MP 17.0 software, we calculated pooled estimates of the prevalence of falls by using a random-effects model, Subgroup analysis was conducted based on gender, different frailty assessment tools, areas, level of economic development, and year of publication. The results are presented using a forest plot. RESULTS: Twenty-nine studies were included in this meta-analysis and a total of 1,093,270 participants aged 65 years and above were enrolled. Among the older adults, frailty was significantly associated with a higher risk for falls, compared with those without frailty (combined RR-relative risk = 1.48, 95% CI-confidence interval: 1.27-1.73, I2=98.9%). In addition, the results of subgroup analysis indicated that men had a higher risk for falls than women among the older adults with frailty (RR 1.94, 95% CI: 1.18-3.2 versus RR 1.44, 95% CI: 1.24-1.67). Subgroup analysis by different frailty assessment tools revealed an increased risk of falls in older adults with frailty when assessed using the Frailty Phenotype (combined RR 1.32, 95%CI: 1.17-1.48), FRAIL score (combined RR 1.82, 95%CI: 1.36-2.43), and Study of Osteoporotic Fractures index (combined RR 1.54, 95%CI: 1.10-2.16). Furthermore, subgroup analysis by areas and level of national economic development found the highest fall risk in Oceania (combined RR 2.35, 95%CI: 2.28-2.43) and the lowest in Europe (combined RR 1.20, 95%CI: 1.05-1.38). Developed countries exhibited a lower fall risk compared to developing countries (combined RR 1.44, 95%CI: 1.21-1.71). Analysis by year of publication showed the highest fall risk between 2013-2019 (combined RR 1.79, 95%CI: 1.45-2.20) and the lowest between 2001-2013 (combined RR 1.21, 95%CI: 1.13-1.29). CONCLUSION: Frailty represents a significant risk factor for falls in older adults, with the degree of risk varying according to the different frailty assessment tools employed, and notably highest when using the FRAIL scale. Additionally, factors such as gender, areas, level of national economic development, and healthcare managers' understanding of frailty may all impact the correlation between frailty and falls. Thus, it's imperative to select suitable frailty diagnostic tools tailored to the specific characteristics of the population in question. This, in turn, facilitates the accurate identification of frailty in older adults and informs the development of appropriate preventive and therapeutic strategies to mitigate fall risk.

[A deep-learning model for the assessment of coronary heart disease and related risk factors via the evaluation of retinal fundus photographs].

Objective: To develop and validate a deep learning model based on fundus photos for the identification of coronary heart disease (CHD) and associated risk factors. Methods: Subjects aged>18 years with complete clinical examination data from 149 hospitals and medical examination centers in China were included in this retrospective study. Two radiologists, who were not aware of the study design, independently evaluated the coronary angiography images of each subject to make CHD diagnosis. A deep learning model using convolutional neural networks (CNN) was used to label the fundus images according to the presence or absence of CHD, and the model was proportionally divided into training and test sets for model training. The prediction performance of the model was evaluated in the test set using monocular and binocular fundus images respectively. Prediction efficacy of the algorithm for cardiovascular risk factors (e.g., age, systolic blood pressure, gender) and coronary events were evaluated by regression analysis using the area under the receiver operating characteristic curve (AUC) and R2 correlation coefficient. Results: The study retrospectively collected 51 765 fundus images from 25 222 subjects, including 10 255 patients with CHD, and there were 14 419 male subjects in this cohort. Of these, 46 603 fundus images from 22 701 subjects were included in the training set and 5 162 fundus images from 2 521 subjects were included in the test set. In the test set, the deep learning model could accurately predict patients' age with an R2 value of 0.931 (95%CI 0.929-0.933) for monocular photos and 0.938 (95%CI 0.936-0.940) for binocular photos. The AUC values for sex identification from single eye and binocular retinal fundus images were 0.983 (95%CI 0.982-0.984) and 0.988 (95%CI 0.987-0.989), respectively. The AUC value of the model was 0.876 (95%CI 0.874-0.877) with either monocular fundus photographs and AUC value was 0.885 (95%CI 0.884-0.888) with binocular fundus photographs to predict CHD, the sensitivity of the model was 0.894 and specificity was 0.755 with accuracy of 0.714 using binocular fundus photographs for the prediction of CHD. Conclusion: The deep learning model based on fundus photographs performs well in identifying coronary heart disease and assessing related risk factors such as age and sex.

[Summary of research methods of stroke disease burden in big data era].

Stroke has high disability rate and high mortality rate, resulting in huge disease burden to society and individuals. In the context of highly informationization of global health system, countries have built and improved various public health information platform to provide support for health decision-making through public health information collection, classification, extraction, analysis and sharing in the research of disease burden of stroke. Based on the retrieval of domestic and foreign literatures, this paper summarizes the research methods of stroke-caused disease burden and its public health information sources in China, evaluates the significance of public health as well as the limitations of each research method of disease burden and describes the application and development of stroke-caused disease burden big data platform in the world, and provide suggestions for establishing a more modern and information-based stroke-caused disease burden evaluation system in China by analyzing the limitations of the existing stroke-caused disease burden evaluation system.

[Changing trends regarding the rates of prevalence, awareness and treatment of hypertension and the differences by demographic and economic factors, among adult farmers in nine provinces in China, from 1991 to 2015].

Objective: To analyze the changes on the rates of prevalence, awareness and treatment of hypertension as well as the differences of the disease by demographic and economic factors, among adult farmers in nine provinces of China. Methods: Adult farmers, (including fishermen and hunters) with completed data on demographics, socio-economic characteristics, disease histories and physical measurements were selected from the 1991-2015 China Health and Nutrition Survey. Hypertension was defined based on the Chinese guidelines. Multivariate logistic regression was used to analyze the influence of demographic and economic factors on the rates of prevalence, awareness and treatment of hypertension among those adult farmers in 2015. Results: A total of 18 236 participants were included. From 1991 to 2015, rates of prevalence, awareness and treatment of hypertension among the adult farmers in nine provinces all significantly increased (P<0.05). Results from the multivariate analysis showed that farmers aged ≥45 years were 98% more likely to suffer from hypertension than those aged 18-44 years. Farmers who were overweight or obese were 1.58 times and 5.21 times more likely to suffer from hypertension than those in the control group (BMI<24.0 kg/m(2)), respectively. The rates on awareness of hypertension among farmers with junior high school education (OR=0.23) or above (OR=0.12) or with junior high school education (OR=0.30) were both lower than those with primary school education or below. Rates on the awareness and treatment of hypertension in obese farmers were 5.77 times and 7.52 times of those in the control group. Conclusions: The rates of prevalence of hypertension showed increasing trends during 1991-2015, with a slower increase in the rates of awareness and treatment in the farmers. Age ≥45 year-olds, and being obese were the risk factors for farmers to have hypertension, which suggested the above population should be focused on hypertension prevention strategy and practice.

Biomarker assessment of the CBCSG006 trial: a randomized phase III trial of cisplatin plus gemcitabine compared with paclitaxel plus gemcitabine as first-line therapy for patients with metastatic triple-negative breast cancer.

Background: CBCSG006 trial reported the superior efficacy of cisplatin plus gemcitabine (GP) regimen than paclitaxel plus gemcitabine (GT) regimen as first-line treatment of metastatic triple-negative breast cancer (mTNBC). This study focused on the updated survival data and the explorations of potential biomarkers for efficacy. Patients and methods: Germ-line mutations of homologous recombination (HR) panel, BRCA1/2 included, were evaluated in 55.9% (132/236) patients. PD-L1 expression was evaluated in 48.3% (114/236) patients. A nonparametric sliding-window subpopulation treatment effect pattern plot (STEPP) methodology was used to analyze the absolute survival benefits. All statistical tests were two-sided. Results: Median progression-free survival (PFS) was 7.73 [95% confidence interval (CI) 6.46-9.00] months for GP arm and 6.07 (95% CI 5.32-6.83) months for GT arm (P = 0.005). No significant difference in overall survival (OS) was observed. There was significant interaction between HR status and treatment for PFS and status of HR deficient significantly correlated with higher objective response rate (ORR) and longer PFS in GP arm than in GT arm (71.9% versus 38.7%, P = 0.008; 10.37 versus 4.30 months, P = 0.011). There was no significant interaction between germ-line BRCA1/2 (gBRCA1/2) status and treatment for PFS. Patients with gBRCA1/2 mutation had numerically higher ORR and prolonged PFS in GP arm than in GT arm (83.3% versus 37.5%, P = 0.086; 8.90 versus 3.20 months, P = 0.459). There was no significant interaction between PD-L1 status and treatment for PFS, and no significant differences in ORR, PFS or OS between two arms regardless of PD-L1 status. In STEPP analysis, patients with lower composite risks had more absolute benefits in PFS than those with higher composite risks. Conclusions: GP regimen has superior efficacy than GT regimen as first-line chemotherapy for mTNBC patients. Germ-line mutations of BRCA1/2 and HR panel are possible biomarkers for better performance of cisplatin-based regimens. A composite risk model was developed to guide patient selection for GP treatment in TNBC patients. Trial registration: ClinicalTrials.gov, NCT01287624.

[A meta-analysis of Symptom Checklist-90 assessment results in Chinese nurses].

Objective: To investigate the application of Symptom Checklist-90 (SCL-90) in Chinese nurses. Methods: We performed a literature search for articles in Chinese on the mental health of nurses investigated using the SCL-90 that were published from January 2007 to December 2016. A total of 129 articles were included, involving 178 groups of valid samples as well as 36316 subjects in 27 provinces or cities in China. The metafor package of R 3.2.1 software and Excel were used for meta-analysis and data statistics. Results: Heterogeneity was identified for the nine factors included in the literature data (Q test: P<0.05; I(2) test >40%) , so a random effects model was used. For all factors except interpersonal relationship factor, the 95% confidence intervals of the pooled effect sizes did not include 0. For all factors except interpersonal relationship factor (Egger test: P<0.05) , no significant asymmetry or incompleteness appeared in the funnel plots. The fail-safe number of each factor was much larger than 5k+10. The merger scores and mean values of nine factors were higher than the national norm. Conclusion: The SCL-90 is widely used in the study of mental status of Chinese nurses, but the SCL-90 norm for nurses is lacking.