Conectar
To examine chain mediating roles of social support and self-efficacy between quality of frailty and depression in elderly people after percutaneous coronary intervention (PCI).
Frailty is a cause of depression among elderly patients after PCI. Although previous studies have shown that frailty, social support, self-efficacy and depression are significantly related, the interaction mechanism remains unclear.
Employing a cross-sectional research design and convenience sampling methodology, data were collected at a tertiary hospital in China. Participants completed the FRAIL Scale, Social Support Rate Scale, General Self-Efficacy Scale and the Hospital Anxiety and Depression Scale. We utilised the PROCESS macro in SPSS to ascertain the mediating roles of social support and self-efficacy between frailty and depression.
The study used the STROBE checklist for reporting.
A total of 241 elderly patients were included in the study, with an average age of 68.05 (6.04) years. Among them, 63.1% of the participants had experienced depression. Elderly patients after PCI's frailty score was negatively correlated with social support and self-efficacy, and positively correlated with depression. Social support was significantly positively correlated with self-efficacy, significantly negatively correlated with depression. Finally, self-efficacy was significantly negatively correlated with depression. Social support and self-efficacy mediated 11.61% and 29.46% of the total depressive role in elderly after frailty and PCI, respectively.
Frailty in elderly PCI patients is directly associated with depression and indirectly related through social support or self-efficacy.
To enhance the quality of life for elderly patients following PCI, healthcare providers should address the impact of frailty on depression and develop intervention strategies based on the levels of social support and self-efficacy.
Questionnaires filled out by patients were used to understand the relationship between frailty, social support, self-efficacy and depression.
The objective of this study was to construct and validate a structural equation model (SEM) to identify factors associated with sleep quality in awake patients in the intensive care unit (ICU) and to assist in the development of clinical intervention strategies.
In this cross-sectional study, 200 awake patients who were cared for in the ICU of a tertiary hospital in China were surveyed via several self-report questionnaires and wearable actigraphy sleep monitoring devices. Based on the collected data, structural equation modelling analysis was performed using SPSS and AMOS statistical analysis software. The study is reported using the STROBE checklist.
The fit indices of the SEM were acceptable: χ2/df = 1.676 (p < .001) and RMSEA = .058 (p < 0.080). Anxiety/depression had a direct negative effect on the sleep quality of awake patients cared for in the ICU (β = −.440, p < .001). In addition, disease-freeness progress had an indirect negative effect on the sleep quality of awake patients cared for in the ICU (β = −.142, p < .001). Analgesics had an indirect negative effect on the sleep quality of awake patients cared for in the ICU through pain and sedatives (β = −.082, p < .001). Sedation had a direct positive effect on the sleep quality of conscious patients cared for in the ICU (β = .493; p < .001).
The results of the SEM showed that the sleep quality of awake patients cared for in the ICU is mainly affected by psychological and disease-related factors, especially anxiety, depression and pain, so we can improve the sleep quality of patients through psychological intervention and drug intervention.
To investigate the prevalence of anxiety and depression symptoms in intensive care unit (ICU) patients with cardiovascular disease (CVD) and to explore which elements are risk factors for the development of anxiety and depression symptoms.
A cross-sectional study.
A total of 1028 ICU patients with CVD were enrolled in this cross-sectional study. Logistic regression was used to assess risk factors and associations between anxiety and depression symptoms, and mediation analysis was used to explore the effect of risk factors on the association between anxiety and depression symptoms. Reporting of the study followed the STROBE checklist.
The results showed that among ICU patients with CVD, 38.1% had anxiety symptoms, 28.7% had depression symptoms and 19.3% had both anxiety and depression symptoms, and there was a significant association between anxiety and depression symptoms. We also identified female gender, hypertension, hyperlipidemia and cardiac function class IV as independent risk factors for anxiety and depression symptoms. Importantly, these factors also mediated the association between anxiety and depression symptoms, emphasising their role in the psychological well-being of this patient group.
ICU patients with CVD were prone to anxiety and depression symptoms. Female gender, hypertension, hyperlipidemia and cardiac function class IV were identified as independent risk factors that also served as mediators in the relationship between anxiety and depression symptoms. Especially, cardiac function class IV emerged as a critical factor in this association.
It is imperative for critical care professionals to recognize the elevated risk of depression and anxiety among ICU patients with severe CVD, especially those with cardiac function class IV, hypertension, hyperlipidemia and females. Proactive and supportive measures are essential for this vulnerable group during their ICU stay to safeguard their mental health and prevent negative outcomes.
No Patient or Public Contribution.
In this study, we explored the use of thermal imaging technology combined with computer vision techniques for assessing surgical incision healing. We processed 1189 thermal images, annotated by experts to define incision boundaries and healing statuses. Using these images, we developed a machine learning model based on YOLOV8, which automates the recognition of incision areas, lesion segmentation and healing classification. The dataset was divided into training, testing and validation sets in a 7:2:1 ratio. Our results show high accuracy rates in incision location recognition, lesion segmentation and healing classification, indicating the model's effectiveness as a precise and automated diagnostic tool for surgical incision healing assessment. Conclusively, our thermal image-based machine learning model demonstrates excellent performance in wound assessment, paving the way for its clinical application in intelligent and standardized wound management.
FreshRSS 