Use of health and aged care services in Australia following hospital admission for myocardial infarction, stroke or heart failure

Factors Associated With Intended Utilization of Home-Based Long-Term Care Among Older Adults in China: The Moderating Effect of Community Support

OBJECTIVES

Guided by Andersen's behavioral model, the objectives of this study were to (a) examine the associations between individual factors and the intended utilization of home-based long-term care (LTC) services among older adults in China and (b) explore the influence of community support, if any, on these associations.

METHODS

Using a multistage random sampling approach, we recruited 1,064 older adults in Chengdu, China. Hierarchical regression was employed to investigate the direct effects of individual predictors and community support on the intended use of home-based LTC services. The moderating effect of community support was examined using the Hayes Process.

RESULTS

Two predisposing (i.e., being old and unmarried), 4 enabling (i.e., living alone, having fewer children, and receiving less family and peer support), and 6 need factors (i.e., having more chronic illnesses; decreased performance in instrumental activities of daily living; higher levels of depression, anxiety, loneliness; and a lower self-image of aging), and low community support were significantly associated with increased intended utilization of home-based LTC. In addition, community support moderated the effects of family support (B = -0.20, p < .001), peer support (B = -0.20, p < .001), self-image of aging (B = -0.39, p < .001), depression (B = -0.34, p < .001), and loneliness (B = -0.48, p < .001) on the intended utilization of home-based LTC services.

DISCUSSION

Policy-makers and practitioners should consider delivering tailored services for older adults and involve the community in the context of enhancing home-based LTC services.

Factors Associated with the Utilization of Home and Community-Based Services (HCBS) Among Older Adults: A Systematic Review of the Last Decade

Home and community-based services (HCBS) for older adults have been promoted worldwide to address the growing problems of aging. This systematic review included 59 studies published from 2013 to 2023 to explore factors influencing the utilization of HCBS among older adults. The review identified 15 common factors grouped into four levels of influence: individual, inter-relationship, community, and social contextual levels. The findings suggest that HCBS utilization is a dynamic process influenced by multiple factors at different levels. Gerontological social work should incorporate ecological thinking to improve practice and strengthen caregiver-recipient relationships.

A prediction model for major adverse cardiovascular events in patients with heart failure based on high-throughput echocardiographic data

Background

Heart failure (HF) is a serious end-stage condition of various heart diseases with increasing frequency. Few studies have combined clinical features with high-throughput echocardiographic data to assess the risk of major cardiovascular events (MACE) in patients with heart failure. In this study, we assessed the relationship between these factors and heart failure to develop a practical and accurate prognostic dynamic nomogram model to identify high-risk groups of heart failure and ultimately provide tailored treatment options.

Materials and methods

We conducted a prospective study of 468 patients with heart failure and established a clinical predictive model. Modeling to predict risk of MACE in heart failure patients within 6 months after discharge obtained 320 features including general clinical data, laboratory examination, 2-dimensional and Doppler measurements, left ventricular (LV) and left atrial (LA) speckle tracking echocardiography (STE), and left ventricular vector flow mapping (VFM) data, were obtained by building a model to predict the risk of MACE within 6 months of discharge for patients with heart failure. In addition, the addition of machine learning models also confirmed the necessity of increasing the STE and VFM parameters.

Results

Through regular follow-up 6 months after discharge, MACE occurred in 156 patients (33.3%). The prediction model showed good discrimination C-statistic value, 0.876 (p < 0.05), which indicated good identical calibration and clinical efficacy. In multiple datasets, through machine learning multi-model comparison, we found that the area under curve (AUC) of the model with VFM and STE parameters was higher, which was more significant with the XGboost model.

Conclusion

In this study, we developed a prediction model and nomogram to estimate the risk of MACE within 6 months of discharge among patients with heart failure. The results of this study can provide a reference for clinical physicians for detection of the risk of MACE in terms of clinical characteristics, cardiac structure and function, hemodynamics, and enable its prompt management, which is a convenient, practical and effective clinical decision-making tool for providing accurate prognosis.

Development and Validation of a Risk Score in Chinese Patients With Chronic Heart Failure

Background

Acute exacerbation of chronic heart failure contributes to substantial increases in major adverse cardiovascular events (MACE). The study developed a risk score to evaluate the severity of heart failure which was related to the risk of MACE.

Methods

This single-center retrospective observational study included 5,777 patients with heart failure. A credible random split-sample method was used to divide data into training and validation dataset (split ratio = 0.7:0.3). Least absolute shrinkage and selection operator (Lasso) logistic regression was applied to select predictors and develop the risk score to predict the severity category of heart failure. Receiver operating characteristic (ROC) curves, and calibration curves were used to assess the model’s discrimination and accuracy.

Results

Body-mass index (BMI), ejection fraction (EF), serum creatinine, hemoglobin, C-reactive protein (CRP), and neutrophil lymphocyte ratio (NLR) were identified as predictors and assembled into the risk score (P &lt; 0.05), which showed good discrimination with AUC in the training dataset (0.770, 95% CI:0.746–0.794) and validation dataset (0.756, 95% CI:0.717–0.795) and was well calibrated in both datasets (all P &gt; 0.05). As the severity of heart failure worsened according to risk score, the incidence of MACE, length of hospital stay, and treatment cost increased (P &lt; 0.001).

Conclusion

A risk score incorporating BMI, EF, serum creatinine, hemoglobin, CRP, and NLR, was developed and validated. It effectively evaluated individuals’ severity classification of heart failure, closely related to MACE.