Heart failure is one of the most common heart conditions, with consequential impact on patient quality of life.
Meanwhile, researchers at the University of Birmingham, have developed a new way to identify patients with heart failure with the use of a series of artificial intelligence (AI) techniques to deeply interrogate data from clinical trial. Such patients will be benefiting from treatment with beta-blockers.
The study involved 15,669 patients with heart failure and reduced left ventricular ejection fraction (low function of the heart’s main pumping chamber), 12,823 of which were in normal heart rhythm and 2,837 of which had atrial fibrillation (AF)—a heart rhythm condition commonly associated with heart failure that leads to worse outcomes.
The research was led by the cardAIc group, a multi-disciplinary team of clinical and data scientists at the University of Birmingham and the University Hospitals Birmingham NHS Foundation Trust, aiming to integrate AI techniques to improve the care of cardiovascular patients.
“Development of these new AI approaches is vital to improving the care we can give to our patients; in the future this could lead to personalized treatment for each individual patient, taking account of their particular health circumstances to improve their well-being.” says the corresponding author Dipak Kotecha, Professor and Consultant in Cardiology at the University of Birmingham, international lead for the Beta-blockers in Heart Failure Collaborative Group, and also a co-lead for the cardAIc group.
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