The partnership between humans and machines can enhance clinical decisions accuracy, leading to improved patient outcomes. Despite this, the application of machine learning techniques in the healthcare sector, particularly in guiding heart failure patient management, remains unpopular. This systematic review aims to identify factors restricting the integration of machine learning derived risk scores into clinical practice when treating adults with acute and chronic heart failure. Four academic research databases and Google Scholar were searched to identify original research studies where heart failure patient data was used to build models predicting all-cause mortality, cardiac death, all-cause and heart failure-related hospitalization. Thirty studies met the inclusion criteria. The selected studies' sample size ranged between 71 and 716 790 patients, and the median age was 72.1 (interquartile range: 61.1–76.8) years. The minimum and maximum area under the receiver operating characteristic curve (AUC) for models predicting mortality were 0.48 and 0.92, respectively. Models predicting hospitalization had an AUC of 0.47 to 0.84. Nineteen studies (63%) used logistic regression, 53% random forests, and 37% of studies used decision trees to build predictive models. None of the models were built or externally validated using data originating from Africa or the Middle-East. The variation in the aetiologies of heart failure, limited access to structured health data, distrust in machine learning techniques among clinicians and the modest accuracy of existing predictive models are some of the factors precluding the widespread use of machine learning derived risk calculators.

Contemporary World Health Organization data indicates that ≈39 million people are living with the human immunodeficiency virus. Of these, 24 million have been reported to have successfully accessed combination antiretroviral therapy. In 1996, the World Health Organization endorsed the widespread use of combination antiretroviral therapy, transforming human immunodeficiency virus infection from being a life-threatening disease to a chronic illness characterized by multiple comorbidities. The increased access to combination antiretroviral therapy has translated to people living with human immunodeficiency virus (PLWH) no longer having a reduced life expectancy. Although aging as a biological process increases exposure to oxidative stress and subsequent systemic inflammation, this effect is likely enhanced in PLWH as they age. This narrative review engages the intricate interplay between human immunodeficiency virus associated chronic inflammation, combination antiretroviral therapy, and cardiac and renal comorbidities development in aging PLWH. We examine the evolving demographic profile of PLWH, emphasizing the increasing prevalence of aging individuals within this population. A central focus of the review discusses the pathophysiological mechanisms that underpin the heightened susceptibility of PLWH to renal and cardiac diseases as they age.

Background: The age of onset and causes of heart failure differ between high-income and low-and-middle-income countries (LMIC). Heart failure patients in LMIC also experience a higher mortality rate. Innovative ways that can risk stratify heart failure patients in this region are needed. The aim of this study was to demonstrate the utility of machine learning in predicting all-cause mortality in heart failure patients hospitalised in a tertiary academic centre.Methods: Six supervised machine learning algorithms were trained to predict in-hospital all-cause mortality using data from 500 consecutive heart failure patients with a left ventricular ejection fraction (LVEF) less than 50%.Results: The mean age was 55.2 ± 16.8 years. There were 271 (54.2%) males, and the mean LVEF was 29 ± 9.2%. The median duration of hospitalisation was 7 days (interquartile range: 4–11), and it did not differ between patients discharged alive and those who died. After a prediction window of 4 years (interquartile range: 2–6), 84 (16.8%) patients died before discharge from the hospital. The area under the receiver operating characteristic curve was 0.82, 0.78, 0.77, 0.76, 0.75, and 0.62 for random forest, logistic regression, support vector machines (SVM), extreme gradient boosting, multilayer perceptron (MLP), and decision trees, and the accuracy during the test phase was 88, 87, 86, 82, 78, and 76% for random forest, MLP, SVM, extreme gradient boosting, decision trees, and logistic regression. The support vector machines were the best performing algorithm, and furosemide, beta-blockers, spironolactone, early diastolic murmur, and a parasternal heave had a positive coefficient with the target feature, whereas coronary artery disease, potassium, oedema grade, ischaemic cardiomyopathy, and right bundle branch block on electrocardiogram had negative coefficients.Conclusion: Despite a small sample size, supervised machine learning algorithms successfully predicted all-cause mortality with modest accuracy. The SVM model will be externally validated using data from multiple cardiology centres in South Africa before developing a uniquely African risk prediction tool that can potentially transform heart failure management through precision medicine.

Open-heart surgery is the leading cause of neuronal injury in the perioperative state, with some patients complicating with cerebrovascular accidents and delirium. Neurological fallout places an immense burden on the psychological well-being of the person affected, their family, and the healthcare system. Several randomised control trials (RCTs) have attempted to identify therapeutic and interventional strategies that reduce the morbidity and mortality rate in patients that experience perioperative neurological complications. However, there is still no consensus on the best strategy that yields improved patient outcomes, such that standardised neuroprotection protocols do not exist in a significant number of anaesthesia departments. This review aims to discuss contemporary evidence for preventing and managing risk factors for neuronal injury, mechanisms of injury, and neuroprotection interventions that lead to improved patient outcomes. Furthermore, a summary of existing RCTs and large observational studies are examined to determine which strategies are supported by science and which lack definitive evidence. We have established that the overall evidence for pharmacological neuroprotection is weak. Most neuroprotective strategies are based on animal studies, which cannot be fully extrapolated to the human population, and there is still no consensus on the optimal neuroprotective strategies for patients undergoing cardiac surgery. Large multicenter studies using universal standardised neurological fallout definitions are still required to evaluate the beneficial effects of the existing neuroprotective techniques.

Heart failure is a clinical syndrome resulting from various cardiovascular diseases of different aetiologies and pathophysiology. These varying pathologies involve several complex mechanisms that lead to the activation of the neurohumoral system, inflammation, angiogenesis, apoptosis, fibrosis, and eventually adverse cardiac remodelling associated with a progressive decline in cardiac function. Once a diagnosis is made, the cardiac function has a gradual decline characterised by multiple hospital admissions. It is therefore imperative to identify patients at different stages of the heart failure continuum to better risk stratify and initiate optimal management strategies. Biomarkers may play a role in the diagnosis, prognostication, and monitoring response to treatment. This review discusses the epidemiology of heart failure and biomarkers commonly used in clinical practice such as natriuretic peptides and cardiac troponins. In addition, we provide a brief overview of novel biomarkers and genetic coding and non-coding biomarkers used in the management of patients with heart failure. We also discuss barriers that hinder the clinical application of novel biomarkers. Finally, we appraise the value of polygenic risk scoring, focusing on sub-Saharan Africa.