Journal of Advanced Nursing

Factors Associated With Decision‐Making Self‐Efficacy Among Family Members of ICU Patients: A Cross‐Sectional Study

ABSTRACT

Aims

To describe the level of family decision-making self-efficacy and its associated factors among Chinese family members of ICU patients.

Design

Cross-sectional descriptive quantitative study.

Methods

Using convenience sampling, 154 ICU patients and their family members from two tertiary hospitals completed a paper-based questionnaire assessing sociodemographic characteristics of patients and their family members, patients' disclosure of preferences to their family members, and family members' decision-making self-efficacy, anxiety and depression, uncertainty of illness, coping and social support. The data were analysed using independent-samples t-tests, one-way analysis of variance, Pearson correlation and multiple linear regression.

Results

The average scores of self-efficacy in treatment, comfort promotion and facing death decision-making were 4.3 (SD = 0.6; range = 1–5), 4.2 (SD = 0.6; range = 1–5) and 3.5 (SD = 0.6; range = 1–5), respectively. Active coping was a predictor of self-efficacy in treatment, comfort-promoting and facing death decision-making. Patients' disclosure of preferences regarding mechanical ventilation, family members' anxiety and illness uncertainty were predictors of self-efficacy in treatment decision-making. Patients' disclosure of preferences regarding expensive medications was a predictor of self-efficacy in comfort-promoting decision-making, and patients' age was a predictor of self-efficacy in facing death decision-making.

Conclusions

Chinese family members of ICU patients reported relatively high self-efficacy in treatment and comfort promotion decision-making but lower self-efficacy in facing death decision-making. Active coping plays a critical role in enhancing decision-making self-efficacy across these three types of decisions. The predictors of decision-making self-efficacy varied according to the specific type of decision.

Implications for the Profession and/or Patient Care

For Chinese family members of ICU patients, targeted strategies to strengthen their active coping skills are key to enhancing their confidence in making decisions with or for patients. Patients' disclosure of preferences to their family members is helpful for improving family members' confidence in making treatment and comfort promotion decisions. Extra support is especially needed for end-of-life decision-making, particularly when the patient is younger.

Impact

This research informs future interventions by highlighting active coping and patients' disclosure of preferences to family members as key factors to strengthen decision-making self-efficacy among Chinese family members of ICU patients. However, family members' decision-making self-efficacy appears to be culturally specific, underscoring the need to design family-centered critical care approaches that are tailored to cultural contexts in other settings. Besides, while our research found a positive association between anxiety and self-efficacy in treatment decision-making, the relationship between them requires further investigation.

Reporting Method

STROBE guidelines.

Patient or Public Contribution

No Patient or Public Contribution.

PLOS ONE Medicine&Health

Construct prediction models for low muscle mass with metabolic syndrome using machine learning

by Yanxuan Wu, Fu Li, Hao Chen, Liang Shi, Meng Yin, Fan Hu, Gongchang Yu

Metabolic syndrome (MetS) and sarcopenia are major global public health problems, and their coexistence significantly increases the risk of death. In recent years, this trend has become increasingly prominent in younger populations, posing a major public health challenge. Numerous studies have regarded reduced muscle mass as a reliable indicator for identifying pre-sarcopenia. Nevertheless, there are currently no well-developed methods for identifying low muscle mass in individuals with MetS.

A total of 2,467 MetS patients (aged 18–59 years) with low muscle mass assessed by dual-energy X-ray absorptiometry (DXA) were included using data from the 2011–2018 National Health and Nutrition Examination Survey (NHANES). Least Absolute Shrinkage and Selection Operator (LASSO) regression was then used to screen for important features. A total of nine Machine learning (ML) models were constructed in this study. Area under the curve (AUC), F1 Score, Recall, Precision, Accuracy, Specificity, PPV, and NPV were used to evaluate the model’s performance and explain important predictors using the Shapley Additive Explain (SHAP) values.

The Logistic Regression (LR) model performed the best overall, with an AUC of 0.925 (95% CI: 0.9043, 0.9443), alongside strong F1-score (0.87) and specificity (0.89). Five important predictors are displayed in the summary plot of SHAP values: height, gender, waist circumference, thigh length, and alkaline phosphatase (ALP).

This study developed an interpretable ML model based on SHAP methodology to identify risk factors for low muscle mass in a young population of MetS patients. Additionally, a web-based tool was implemented to facilitate sarcopenia screening.