Background: Heart failure (HF) is a global health challenge affecting millions, with significant variations in patient characteristics and outcomes based on ejection fraction. This study aimed to differentiate between HF with reduced ejection fraction (HFrEF) and HF with preserved ejection fraction (HFpEF) with respect to patient characteristics, risk factors, comorbidities, and clinical outcomes, incorporating advanced machine learning models for mortality prediction.
Methodology: The study included 1861 HF patients from 21 centers in Jordan, categorized into HFrEF (EF <40%) and HFpEF (EF ≥ 50%) groups.
The Corona Virus Disease 2019 (COVID-19) has become an unprecedented global public health crisis and a pandemic associated with vicarious psychosocial and economic stresses. Such stresses were reported to lead to behavioral and emotional disturbances in individuals not infected with the COVID-19 virus. It is largely unknown if these stresses can trigger acute cardiovascular events (CVE) in such individuals.
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