# Clustering of Heart Failure Phenotypes in Johannesburg Using Unsupervised Machine Learning
## 4 Discussion  
In this study, we used retrospective data of consecutive patients hospitalised with new-onset heart failure symptoms or decompensated chronic heart failure to describe demographic and clinical characteristics and categorise heart failure phenotypes using unsupervised machine algorithms. The mean age in our cohort was 55.6 \(\pm\) 16.9 years, and 14.5% of all patients hospitalised with the diagnosis of heart failure died. Heart failure is commonly associated with poorer outcomes in LMICs, and the onset of heart failure occurs at a younger age of 53 years, affecting economic productivity in this region [7,8]. The most plausible explanations for poorer outcomes include, but are not limited to, the unavailability of life-prolonging therapy such as cardiac resynchronisation defibrillators and heart transplant services. Furthermore, our hospital is a quartenary-level institution, implying that there might be a delay in the referral of patients, resulting our patients being hospitalised with advanced stages of heart failure. The younger age of onset may be partly related to the dominance of non-ischaemic cardiomyopathy, possibly with a genetic aetiology, considering that in sub-Saharan Africa (SSA), the most common cardiovascular diseases are still hypertensive heart disease, cardiomyopathies, rheumatic  
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heart disease, and congenital heart disease [7; 8], unlike in high-income countries, where ischaemic heart disease is the primary cause of heart failure. After subjecting our heart failure dataset to unsupervised machine learning algorithms, the agglomerative clustering algorithm identified three clusters: the uraemic cluster, with relatively high urea levels; the hypotensive cluster, with lower systolic and diastolic blood pressure; and the congested cluster, with a high burden of heart failure symptoms and signs of fluid overload. The uraemic cluster had slightly older patients, potentially with underlying acute kidney injury or chronic renal dysfunction. The heart is responsible for maintaining adequate perfusion to all organs. Low systolic and diastolic pressure were a dominant finding in cluster 2, the hypotensive cluster. The reduction in myocardial contractility in this cluster likely caused a decrease in cardiac output, systemic vasoconstriction, and hypotension in the hypotensive cluster [9]. This finding resonates with the importance of preventing hypotension in heart failure patients, considering that hypoperfusion may lead to cardiogenic shock and subsequent mortality. The in-hospital all-cause mortality rate in the hypotensive cluster was 38%, similar to the congestive cluster, with the highest mortality rate of 39.1%. It is well established that patients who present with severe symptoms of acute decompensated heart failure experience poor outcomes, suggesting the need to expedite their management, irrespective of the LVEF. Most studies have confirmed the utility of supervised machine learning algorithms for classification tasks [10; 11; 12; 13; 14; 15; 16; 17; 18]. However, few studies have since been published on phenotyping heart failure patients using unsupervised machine learning algorithms [19; 20; 21; 22; 23; 24; 25]. In a paper summarising 40 studies on heart failure subtype definition using unsupervised machine learning, hierarchical clustering was used in 25 studies. Most clusters were derived based on heart failure symptoms only (\(n=20\)) [6]. [PERSON] et al. used K-Means clustering to stratify heart failure patients with an LVEF above 40% and found four distinct groups. They concluded that older patients might be at a higher risk of right ventricular afterload mismatch and renal dysfunction [26]. In addition, in Russia, 25952 heart failure patients were clustered using K-Means, and 15 distinct heart failure phenotypes were identified. The clusters identified in the study from Russia were characterised and labelled based on comorbidities and causes of heart failure only.