Risk factors and prediction models for cardiovascular complications of hypertension in older adults with machine learning: A cross-sectional study.

Background: Hypertension has emerged as a chronic disease prevalent worldwide that may cause severe cardiovascular complications, particularly in older patients. However, there is a paucity of studies that use risk factors and prediction models for cardiovascular complications associated with hypertension in older adults. Objectives: To identify the risk factors and develop prediction models for cardiovascular complications among older patients with hypertension. Methods: A convenience sample of 476 older patients with hypertension was recruited from a university-affiliated hospital in China. Demographic data, clinical physiological indicators, regulatory emotional self-efficacy, medication adherence, and lifestyle information were collected from participants. Binary logistic regression analysis was performed to screen for preliminary risk factors associated with cardiovascular complications. Two machine learning methods, Back-Propagation neural network, and random forest were applied to develop prediction models for cardiovascular complications among the study cohort. The sensitivity, specificity, accuracy, receiver operating characteristic curve, and area under the curve (AUC) values were used to assess the performance of the prediction models. Results: Binary logistic regression identified nine risk factors for cardiovascular complications among older patients with hypertension. The machine learning models displayed excellent performance in predicting cardiovascular complications, with the random forest model (AUC 0.954) outperforming the Back-Propagation neural network model (AUC 0.811), as confirmed by model comparison analysis. The sensitivity, specificity and accuracy of the Back-Propagation neural network model compared to the random forest model were 74.2% vs. 86.5%, 75.2% vs. 94.3%, and 74.7% vs. 90.4%, respectively. Conclusion: The machine learning methods employed in this study demonstrated feasibility in predicting cardiovascular complications among older patients with hypertension, with the random forest model based on nine risk factors exhibiting excellent prediction performance. These models could be used to identify high-risk populations and suggest early interventions aimed at preventing cardiovascular complications in such cohorts.

Role function and job satisfaction of community nurses in China: A cross-sectional study.

AIM: To evaluate role function and job satisfaction, determine their relationship, and explore the factors influencing job satisfaction among community nurses in China. DESIGN: Cross-sectional study. METHODS: This study was conducted between March and June 2020 on a cluster random sampling of 302 community nurses from 24 community health centres and stations in Xi'an, China. Self-reported data were collected using the Demographics Questionnaire, Role Function of Community Nurses Questionnaire, and Job Satisfaction of Community Nurses Scale. Descriptive statistics, Pearson's correlation analysis, and multiple linear regression analyses were performed to analyse data. RESULTS: Community nurses' main role function was organiser and manager (M = 2.56, SD = 0.987) and coordinator (M = 2.43, SD = 0.971). The lowest job satisfaction was for salary and benefits (M = 3.12, SD = 0.891) and personal development (M = 3.65, SD = 0.738). A positive correlation was found between the roles of caregiver, educator, navigator, and salary and benefits (p < 0.05) among community nurses. Multiple linear regression analyses indicated that monthly income and working experience in nursing explained 61.1% of the variance in job satisfaction.