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This study aims to explore the trajectories and co-occurrence of perceived control and caregiver self-efficacy among patients with heart failure (HF) and their caregivers within 3 months post-discharge and identify associated risk factors.
A prospective cohort design.
A prospective cohort study was conducted from March to June 2024 in Tianjin, China. Information on perceived control and caregiver self-efficacy was collected 24 h before discharge, 2 weeks, 1 month, and 3 months after discharge. Group-Based Dual Trajectory Modelling (GBDTM) and logistic regression were used for analysis.
The study included 203 dyads of patients with HF and their caregivers (HF dyads). Perceived control identified three trajectories: low curve (15.3%), middle curve (57.1%) and high curve (27.6%). Caregiver self-efficacy demonstrated three trajectories: low curve (17.2%), middle curve (56.7%) and high stable (26.1%). GBDTM revealed nine co-occurrence patterns, with the highest proportion (36.7%) being ‘middle-curve group for perceived control and middle-curve group for caregiver self-efficacy’, and 16.7% being ‘high-curve group for perceived control and high-stable group for caregiver self-efficacy’. Age, gender, household income, NYHA class, symptom burden and psychological resilience were identified as risk factors for perceived control trajectories; marital status, regular exercise and psychological resilience were identified as risk factors for caregiver self-efficacy trajectories.
We identified distinct trajectories, co-occurrence patterns and risk factors of perceived control and caregiver self-efficacy among HF dyads. These findings help clinical nurses to better design and implement interventions, strengthening the comprehensive management and care outcomes for HF dyads.
These findings highlighted the interactive relationship between perceived control and caregiver self-efficacy trajectories, suggesting that interventions should boost both to improve personalised treatment plans and outcomes for HF dyads.
This study adhered to the STROBE checklist.
Patients and their caregivers contributed by participating in the study and completing the questionnaire.
This study was to create an interpretable machine learning model to predict the risk of mortality within 90 days for ICU patients suffering from pressure ulcers.
We retrospectively analysed 1774 ICU pressure ulcer patients from the Medical Information Mart for Intensive Care (MIMIC)-IV database.
We used the LASSO regression and the Boruta algorithm for feature selection. The dataset was split into training and test sets at a 7:3 ratio for constructing machine learning models. We employed logistic regression and nine other machine learning algorithms to build the prediction model. Restricted cubic spline (RCS) was used to analyse the linear relationship between the Braden score and the outcome, whereas the SHAP (Shapley additive explanations) method was applied to visualise the model's characteristics.
This study compared the predictive ability of the Braden Scale with other scoring systems (SOFA, APSIII, Charlson, SAPSII). The results showed that the Braden Scale model had the highest performance, and SHAP analysis indicated that the Braden Scale is an important influencing factor for the risk of 90-day mortality in the ICU. The restricted cubic spline curve demonstrated a significant negative correlation between the Braden Scale and mortality. Subgroup analysis showed no significant interaction effects among subgroups except for age.
The machine learning-enhanced Braden Scale has been developed to forecast the 90-day mortality risk for ICU patients suffering from pressure ulcers, and its efficacy as a clinically reliable tool has been substantiated.
Patients or public members were not directly involved in this study.
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.
Advance care planning is a process through which people communicate their goals and preferences for future medical care. Due to the complexity of the decision-making process, decision aids can assist individuals in balancing potential benefits and risks of treatment options.
While decision aids have the potential to better promote advance care planning, their characteristics, content and application effectiveness are unclear and lack systematic review. Therefore, we aimed to explore these three aspects and establish a foundation for future research.
Scoping review.
This scoping review adheres to the framework proposed by Arksey and O'Malley and the PRISMA-ScR list. Six English-language databases were systematically searched from the time of construction until 1 December 2023. Two researchers conducted the article screening and data extraction, and the extracted data was presented in written tables and narrative summaries.
Of the 1479 titles and abstracts, 20 studies fulfilled the inclusion criteria. Types of decision aids were employed, mainly websites and videos. Decision aid's primary components center around 11 areas, such as furnishing information, exploring treatment and care preferences. The main manifestations were a significant increase in knowledge and improved recognition of patients' target value preferences. Among the aids, websites and videos for advance care planning have relatively high content acceptability and decision-making process satisfaction, but their feasibility has yet to be tested.
Decision aids were varied, with content focused on describing key information and exploring treatment and care preferences. Regarding application effects, the aids successfully facilitated the advance care planning process and improved the quality of participants' decisions. Overall, decision aids are efficient in improving the decision-making process for implementing advance care planning in cancer and geriatric populations. In the future, personalised decision aids should be developed based on continuous optimization of tools' quality and promoted for clinical application.
The paper has adhered to the EQUATOR guidelines and referenced the PRISMAg-ScR checklist.
This is a review without patient and public contribution.
Registration: https://doi.org/10.17605/OSF.IO/YPHKF, Open Science DOI: 10.17605/OSF.IO/YPHKF.
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