These unique data suggest that urban Africans in Soweto develop AF at a relatively young age. Conventional strategies used to manage and treat AF need to be carefully evaluated in this setting.
Background
Mental health illnesses are associated with frequent hospitalisation and an increased risk of all-cause mortality. Despite the high prevalence of depression in patients with chronic heart failure (CHF), there is a paucity of data on this subject from low and middle-income countries (LMIC). The aim of this study was to determine the prevalence of depression, anxiety, and stress symptoms in patients attending a dedicated CHF clinic.
Methods
A prospective study was conducted at an outpatient heart failure clinic in a tertiary academic centre. The study participants completed a Depression, Anxiety and Stress (DASS-21) questionnaire to screen for the presence and severity of depression, anxiety and stress symptoms. Furthermore, the Minnesota Living with Heart Failure Questionnaire (MLHFQ) was completed and used to evaluate the impact of CHF on health-related quality of life (QoL). Descriptive statistics were used to describe patients' characteristics and logistic regression analysis to identify predictors of symptoms of depression.
Results
The study population comprised of 103 patients, predominantly female (62.1%) with a median age of 53 (interquartile range 38–61) years. Symptoms of depression were reported by 52.4%, with 11.6% reporting symptoms suggestive of extremely severe depression. Anxiety was diagnosed in 53.4% of patients and extremely severe anxiety was reported by 18.4% of patients. Fifty patients were classified as stressed, and only 7.7% had extremely severe stress. More than half of the patients (54.4%) were in New York Heart Association functional class I. The mean left ventricular ejection fraction in the entire cohort was 30% (SD = ± 11.1%). In the multivariable logistic regression model, the MLHFQ score [odds ratio (OR) 1.04, 95% CI:1.02–1.06, p = 0.001] and the six-minute walk test [OR 0.99, 95% CI: 0.98–0.99, p = 0.014] were identified as independent predictors of depression.
Conclusion
Depression and anxiety symptoms were found in over half of patients attending the CHF clinic. We recommend that mental health screening should be routinely performed in patients with CHF. Prospective, adequately powered, multicentre studies from LMIC investigating the impact of depression, anxiety and stress on CHF outcomes such as health-related QoL, hospitalisation and mortality are required.
Over the past decades, South Africa has undergone rapid demographic changes, which have led to marked increases in specific cardiac disease categories, such as rheumatic heart disease (now predominantly presenting in young adults with advanced and symptomatic disease) and coronary artery disease (with rapidly increasing prevalence in middle age). The lack of screening facilities, delayed diagnosis and inadequate care at primary, secondary and tertiary levels have led to a large burden of patients with heart failure. This leads to suffering of the patients and substantial costs to society and the healthcare system.In this position paper, the South African Heart Association (SA Heart) National Council members have summarised the current state of cardiology, cardiothoracic surgery and paediatric cardiology reigning in South Africa. Our report demonstrates that there has been minimal change in the number of successfully qualified specialists over the last decade and, therefore, a de facto decline per capita. We summarise the major gaps in training and possible interventions to transform the healthcare system, dealing with the colliding epidemic of communicable disease and the rapidly expanding epidemic of non-communicable disease, including cardiac disease.
Background: The diagnosis and therapy of heart failure are guided mainly by a single imaging parameter, the left ventricular ejection fraction (LVEF). Recent studies have reported on the value of machine learning in characterising the various phenotypes of heart failure patients. Therefore, this study aims to use unsupervised machine learning algorithms to phenotype heart failure patients into different clusters using multiple clinical parameters. Methods: Seven unsupervised machine learning clustering algorithms were used to cluster heart failure patients hospitalised with acute and chronic heart failure. Results: The agglomerative clustering algorithm identified three clusters with a silhouette score of 0.72. Cluster 1 (uraemic cluster) comprised 229 (36.0%) patients with a mean age of 56.2 ± 17.2 years and a serum urea of 14.5 ± 31.3 mmol/L. Cluster 2 (hypotensive cluster) comprised 117 (18.4%) patients with a minimum systolic and diastolic blood pressure of 91 and 60 mmHg, respectively. In cluster 3 (congestive cluster), patients predominantly had symptoms of fluid overload, and 93 (64.6%) patients had ascites. Among the 636 heart failure patients studied, the median LVEF was 32% (interquartile range: 25–45), and the rate of in-hospital all-cause mortality was 14.5%. Systolic and diastolic blood pressure, age, and the LVEF had the most substantial impact on discriminating between the three clusters. Conclusions: Clinicians without access to echocardiography could potentially rely on blood pressure measurements and age to risk stratify heart failure patients. However, larger prospective studies are mandatory for the validation of these clinical parameters.
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