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Geriatrics Research
CN: 37-1522/R
ISSN: 2096-9058
Governed by: Health Commission of Shandong Province
Sponsored by: Shandong Provincial Hospital Affiliated to Shandong First Medical University
Editor-in-Chief: SUN Zhijian
Deputy Editor-in-Chief: WANG Jianchun
Founded in 2020, bimonthly.
Indexed by: CNKI, Wanfang, Weipu, Chaoxing, CBM.
Meta-analysis of the prevalence and influencing factors of cognitive frailty in elderly patients with ischemic stroke in China
YIN Jie;XU Yiyong;CAI Mian;FANG Xiwei;Objective To systematically review the prevalence and influencing factors of cognitive frailty in elderly patients with ischemic stroke in China. Methods Embase, PubMed, Web of Science, EBSCO, CNKI, Wanfang, VIP, and CBM were searched to collect studies on the prevalence and factors affecting cognitive frailty in elderly patients with ischemic stroke in China. The retrieval period was from the establishment of the database to March 10, 2025. The American Healthcare Quality and Research Organization(AHRQ) and Newcastle Ottawa Scale(NOS) were used to evaluate the quality of the included literature, and Stata 18. 0 software was used for meta-analysis. Results A total of 4 794 elderly patients with ischemic stroke in China and 1 284 patients with cognitive frailty were included in 15 medium to high-quality literature. Meta-analysis showed that the prevalence of cognitive frailty in elderly patients with ischemic stroke in China was 26. 6%(95% CI: 0. 240-0. 292), and the influencing factors included age(OR=2. 828, 95% CI: 2. 110-3. 792), sleep status(OR=2. 219, 95% CI: 1. 730-2. 846), malnutrition(OR=2. 724, 95% CI: 2. 035-3. 648), multimorbidity(OR=1. 620, 95% CI: 1. 253-2. 049), blood pressure(OR=1. 829, 95% CI: 1. 272-2. 631), depression(OR=2. 861, 95% CI: 1. 770-4. 624), exercise(OR=0. 239, 95% CI: 0. 125-0. 456), and self-care ability(OR=0. 280, 95% CI: 0. 097-0. 805)with all P<0. 05. Conclusions The prevalence of cognitive frailty is high in elderly patients with ischemic stroke in China, and insufficient attention is paid to the pre-frailty stage. Advanced age, sleep disorders, malnutrition, multimorbidity, poor blood pressure control and depression are the major risk factors for cognitive frailty, while regular exercise and good self-care ability have protective effects.
A scoping review of frailty risk prediction models in Chinese patients with diabetes
HU Shiya;WU Peiyun;HUANG Renling;LIN Fenglan;Objective To conduct a scoping review of the risk prediction models for frailty in Chinese patients with diabetes. Methods PubMed, Embase,Web of Science, EBSCO, The Cochrane Library, CNKI, Wanfang, VIP, and CBM were systematically searched to collect Chinese and English literature on the risk prediction models for frailty in Chinese patients with diabetes. The retrieval period was from the establishment of the database to February 22, 2025. Two researchers independently screened and extracted information from the literature, and the quality of the included literature was evaluated using the prediction model bias risk assessment tool. Results A total of 17 studies and 26 diabetes frailty risk prediction models were included, and the incidence of frailty in Chinese diabetic patients was 10. 9%-51. 2%. The modeling methods included logistic regression and machine learning algorithms. All models reported the AUC(0. 768-0. 975), and 15 studies calibrated the models. The risk of bias of the included literature was high, mainly in the research subjects and analysis fields, and the overall applicability was good. Age, depression and glycated hemoglobin level were the important predictors of frailty in Chinese patients with diabetes. Conclusions The included models have good predictive performance, but the risk of bias is high. Future research should focus on developing or validating risk prediction models with low risk of bias and high applicability.
Analysis of influencing factors for frailty among Chinese elderly based on health ecology model
WANG Xige;Objective To comprehensively and multi-levelly explore the influencing factors of frailty among Chinese elderly based on health ecology model, providing a theoretical basis for developing interventions to improve frailty in the elderly. Methods A total of 5 935 adults aged 65 and above from the 2017-2018 China Longitudinal Healthy Longevity Survey(CLHLS) were included. Using frailty status as the dependent variable and factors across the five levels of health ecology model as independent variables, logistic regression models were constructed to identify influencing factors of frailty. Results The explanatory power of the model increased with the inclusion of variables from more levels, with the most significant improvements observed after adding interpersonal network and psychosocial-behavioral factors. The regression results showed that at the psychological-behavioral level, drinking(OR=0. 632, 95% CI: 0. 484-0. 825), smoking(OR=0. 674, 95% CI: 0. 519-0. 876), physical exercise(OR=0. 440, 95% CI: 0. 361-0. 535), vegetable consumption(OR=0. 640, 95% CI: 0. 504-0. 813); in the social network layer of family community, rural residence(OR=0. 814, 95% CI: 0. 685-0. 967), living alone(OR=0. 370, 95% CI: 0. 292-0. 468), being married(OR=0. 492, 95% CI: 0. 402-0. 603), occasionally socialize with friends(OR=0. 297, 95% CI: 0. 235-0. 376), frequently socialize with friends(OR=0. 196, 95% CI: 0. 162-0. 236); at the living and working environment level, primary education(OR=0. 752, 95% CI: 0. 616-0. 916), sufficient income(OR=0. 676, 95% CI: 0. 527-0. 867); at the economic policy environment level, and high annual precipitation of the economic policy environment layer(OR=0. 805, 95% CI: 0. 650-0. 998) were protective factors against frailty in the elderly(all P<0. 05). In the personal trait level, 75-84 years old(OR=3. 352,95% CI:2. 345-4. 791), ≥85 years old(OR=13. 045, 95% CI:9. 192-18. 511); at the psychological-behavioral level, depression(OR=1. 516,95% CI:1. 281-1. 795), anxiety(OR=1. 500,95% CI:1. 170-1. 922),and high per capita GDP of the economic policy environment layer(OR=1. 337,95% CI:1. 132-1. 579) were risk factors against frailty in the elderly(all P<0. 05). Conclusions The health ecology model offers a comprehensive framework for analyzing frailty influencing factors. Psychosocial-behavioral factors and interpersonal networks should be prioritized when designing frailty management and intervention strategies for the elderly.
Meta-analysis of incidence of frailty and its influencing factors in elderly patients with hip fractures
WANG Qing;JIN Qi;YUAN Yanling;AN Yongchao;ZHAO Xuehong;Objective To conduct a systematic review on the incidence of frailty and its influencing factors in elderly patients with hip fracture, and to provide evidence for early identification of frailty high risk group and implementation of individualized intervention measures. Methods Observational studies related to frailty in elderly patients with hip fracture were retrieved from databases including CNKI, Wanfang, VIP, CBM, Pubmed, Embase, Web of Science, and The Cocrone Library via computer-based search. The search was restricted to the period from the databases' establishment up to February 28, 2025. Literature screening and quality evaluation were performed by two investigators. Meta analysis was performed by Stata 17. 0 software. Results A total of 25 studies were included. The results of meta-analysis showed that the incidence of frailty in elderly patients with hip fracture was 34%. BMI and hemoglobin were protective factors for frailty in elderly patients with hip fracture; age, gender, CCI, never exercised, osteoporosis, history of falls in the past year, depression, cognitive impairment, multiple underlying disease, polypharmacy, creatinine, prolonged bed rest after operation, anemia, malnutrition, living alone, and lack of family care were the risk factors of frailty in elderly patients with hip fracture(all P<0. 05). Conclusion The incidence of frailty is relatively high in elderly patients with hip fracture, which is influenced by physical factors, disease-related factors, and other factors.
Systematic review of frailty risk prediction models in stroke patients
JIANG Yanling;LIAO Jianmei;HUANG Niyan;Objective To systematically evaluate frailty risk prediction models for stroke patients and provide a scientific screening tool for healthcare workers. Methods CNKI, Wanfang, VIP, CBM, PubMed, Embase, Web of Science, and The Cochrane Library databases were systematically searched for studies on frailty risk prediction models for stroke patients, with the search period from database establishment to April 2025. Two researchers independently screened literature and extracted data. The PROBAST tool was used for assessment of bias risk and applicability. RevMan 5. 4 software was applied for Meta-analysis of predictors with a frequency>2 times, and MedCalc software was used to evaluate the performance of the included prediction models. Results A total of 13 studies were included. The AUC of the prediction models ranged from 0. 629 to 0. 94, and the pooled AUC by MedCalc was 0. 857(95% CI: 0. 796-0. 917), indicating moderate predictive performance. Meta-analysis results showed that age, depression, falls, dysphagia, diabetes, malnutrition, National Institutes of Health Stroke Scale(NIHSS) score, comorbidity, living alone, activities of daily living(ADL), and physical exercise were significant predictors of frailty in stroke patients(all P<0. 05). Conclusions Frailty risk prediction models for stroke patients have certain value, but they carry a high risk of bias and their performance needs further improvement. Future studies should adopt machine learning technology and conduct large-sample, multi-center, prospective research. Healthcare workers should scientifically and reasonably apply the prediction models according to patients' individual differences to improve model accuracy.
