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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.
Optimal machine learning model selection and validation for predicting emergency response capability of stroke caregivers in stroke recurrence
LI Bo;YANG Mingying;WANG Ya;ZHAN Anning;XIAO Yang;YANG Qiqi;YANG Zihang;Objective To apply machine learning algorithms to construct a stroke recurrence emergency response capability prediction model for stroke caregivers and to select and verify the model with the best predictive performance.Methods A total of 515 caregivers of stroke patients hospitalized from April to August 2024 were recruited. Caregivers were categorized into "deficient" and "non-deficient" groups based on their emergency response capacity deficits. Four machine learning algorithms, random forest, artificial neural network, extreme gradient boosting, and gradient boosting decision tree(GBDT), were employed to construct predictive models. The performance of the models was compared using accuracy, precision, recall, specificity, sensitivity, Youden's index, and the area under the receiver operating characteristic curve(AUC). The Gini index was applied to determine significant influencing factors of stroke caregivers' emergency response capacity. Results The GBDT model demonstrated the best performance in predicting stroke recurrence emergency response capability among stroke caregivers, with an AUC value reaching 0.896, indicating high prediction accuracy. Ten core predictors were identified in the GBDT model, including caregiver burden score, social support score, education level, personal burden score, relationship to the patient, age, primary family economic provider status, participation in stroke knowledge education programs, monthly income, and subjective support score. Conclusions By comparing multiple machine learning algorithms, this study found that the GBDT model excelled in predicting stroke recurrence emergency response capability of stroke caregivers. The model effectively pinpointed critical factors influencing this capacity, enabling dynamic monitoring of predictor changes. These findings lay a technical foundation for personalized intervention protocols, thereby forming a closed-loop support system to improve home-based care quality for stroke patients.
A scoping review of psychological resilience assessment tools for stroke patients
SU Xuan;CHENG Qiaomei;ZHANG Yu;WANG Kexin;XIAO Mengwei;WANG Yu;Objective To conduct a scoping review of national and international tools for assessing psychological resilience in stroke patients. Methods A comprehensive systematic search was conducted across both Chinese and English databases, including China National Knowledge Infrastructure, Wanfang Database, China Biology Medicine Database, Embase, PubMed, Web of Science, and Cochrane Library. The search timeframe spanned from database inception to March 19, 2024. Eligible studies were analyzed with data extraction performed using standardized protocols. The method of scoping review was employed to ensure standardized reporting of the findings in accordance with evidence synthesis guidelines. Results A total of 23 papers were finally included, involving 7 tools for assessing psychological resilience in stroke, all with good internal consistency reliabilities, but most of them had not completed validation of their construct validity in stroke samples, and further validation was recommended before clinical application. CD-RISC-25, CD-RISC-10, RS-25, RS-14, and RSA were considered to be the more appropriate choices for assessing psychological resilience in stroke patients. Conclusions The existing stroke psychological resilience assessment tools are complex and diverse, which should be considered comprehensively in clinical application. In the future, targeted and dynamic assessment entries should be developed with the unique psychological distress and challenges of stroke patients, and comprehensive cross-cultural validation should be conducted to provide a scientific basis for the development of effective psychological interventions for stroke patients and the improvement of patient recovery outcomes.
A qualitative study of sources and manifestations of caregiver guilt in patients with post-stroke physical dysfunction
WANG Xinyu;LIU Dan;KE Qing;LENG Pengqun;Objective To explore the sources and manifestations of caregiver guilt in patients with post-stroke physical dysfunction. Methods The purposive sampling was used to select 13 caregivers of post-stroke patients with limb dysfunction in the Rehabilitation Department of Guizhou Provincial People's Hospital form March to May 2024. Semi-structured in-depth interviews were conducted using the qualitative research method, and the data were analyzed using Colaizzi's seven-step method. Results Caregiver guilt in patients with post-stroke physical dysfunction originated from three aspects: conflicts between personal life and caregiving responsibilities, the limitations of medical and caregiving support, and the slow progress of patient rehabilitation. Their guilt manifested as emotional self-blame, behavioral anxiety and tension, and a tendency toward either excessive caregiving or avoidance of caregiving responsibilities. Conclusions The sources of caregiver guilt in patients with post-stroke physical dysfunction are multidimensional, and the manifestation of guilt is more complex. In order to alleviate caregiver guilt, healthcare professionals should pay attention to their emotional needs, provide support and professional guidance, and help to adjust their expectations and improve their confidence in caregiving.
Application of comprehensive geriatric assessment in identifying risk and protective factors for sarcopenia in individuals aged ≥90 years
ZHANG Tian;SHA Jiangming;JIANG Liming;HU Xinyi;YIN Quanzhong;GU Yihang;Objective To explore the application of comprehensive geriatric assessment(CGA) in identifying risk and protective factors for sarcopenia among individuals aged ≥90 years. Methods A total of 190 ultra-elderly patients aged ≥90 years who were treated in Jiangyin People's Hospital Medical Group and Jiangyin Community Health Service Center from January 2018 to August 2022 were enrolled. CGA was conducted for comprehensive evaluation, and logistic regression analysis was performed to identify sarcopenia-related factors. Results Among 190 participants, 118(62. 11%) were diagnosed with sarcopenia. Risk factors included polypharmacy(OR=1. 303, 95% CI: 1. 100-1. 543), sleep disorders(OR=2. 974, 95% CI: 1. 307-6. 770), smoking history(OR=2. 511, 95% CI: 1. 067-5. 910), depression(OR=4. 157, 95% CI: 1. 776-9. 730), and nutritional risk(OR=2. 402, 95% CI: 1. 071-5. 389)(all P<0. 05). Protective factors included being married(OR=0. 345, 95% CI: 0. 139-0. 858), BMI(OR=0. 702, 95% CI: 0. 592-0. 833), elevated uric acid(OR=0. 996, 95% CI: 0. 993-1. 000), and total cholesterol(OR=0. 535, 95% CI: 0. 314-0. 912)(all P<0. 05). Conclusions The prevalence of sarcopenia in individuals aged ≥90 years is significantly higher than in the general elderly population. CGA highlights modifiable factors such as polypharmacy, smoking, marital status, metabolic indicators(BMI, cholesterol, uric acid, nutritional risk), and psychosocial factors(depression, sleep disorders), offering evidence for multidimensional interventions.
Summary of the best evidence for prevention of pressure injuries related to non-invasive ventilation devices in patients with chronic obstructive pulmonary disease
WU Shuang;PENG Jia;JIANG Haiqiang;HUANG Yanjin;LI Peiyi;HALILI Xirongguli;WU Chuanfang;Objective To systematically retrieve and summarize effective evidence on the prevention of device-relat-ed pressure injuries(DRPIs) during non-invasive ventilation(NIV) in patients with chronic obstructive pulmonary disease(COPD), providing evidence-based guidance for clinical nursing. Methods Following the "6S" evidence pyramid mod-el, domestic and international databases for literature on the prevention and management of DRPIs during NIV in COPD pa-tients were searched, with a time span from the establishment of the databases to April 30, 2024. The literature was evalu-ated and the level of evidence was assessed using the appraisal standards and evidence hierarchy system from the JBI Evi-dence-Based Health Care Center by 2 researchers. Results A total of 8 articles were included, comprising 2 guidelines, 3 systematic review, 1 expert consensus, and 2 evidence summaries. Ultimately, 28 best evidence items were collated cov-ering seven aspects, including patient risk assessment, skin and tissue assessment, preventive skin care, nutrition manage-ment, ventilator mask selection and fitting, support surfaces/dressings, and training and education. Conclusion The best evidence summarized in this study for preventing DRPIs during NIV in COPD patients can be flexibly implemented by healthcare professionals based on clinical contexts, facilitating the effective translation of evidence-based findings into clin-ical practice and reducing the incidence of DRPIs during NIV in COPD patients.
