AI identifies heart failure patients best suited to beta-blocker treatment - Health Europa
A new AI-based approach could help to quickly and easily identify the best treatment options for heart failure patients.
Researchers from the University of Birmingham have used a series of Artificial Intelligence (AI) techniques to identify the heart failure patients most likely to benefit from treatment with beta-blockers.
The findings from the study have been published in The Lancet.
Beta-blockers work predominantly by slowing down the heart, which they do by blocking the action of hormones like adrenaline. Although they are commonly used to treat conditions such as angina, heart failure, and atrial fibrillation (AF), beta-blockers are not suitable for everyone. For example, beta-blockers are not recommended for patients with low blood pressure, metabolic acidosis, or lung disease.
Aiming to integrate AI techniques to improve the care of cardiovascular patients, researchers looked at data involving 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- 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.
Isolating response to beta-blocker therapy
Using AI techniques to deeply investigate the clinical trial data, the team found that this approach could determine different underlying health conditions for each patient, as well as the interactions of these conditions, to isolate response to beta-blocker therapy. This worked in patients with normal heart rhythm, where doctors would normally expect beta-blockers to reduce the risk of death, as well as in patients with AF where previous work has found a lack of effectiveness. In normal heart rhythm, a cluster of patients (who had a combination of older age, less severe symptoms, and lower heart rate than average) was identified with reduced benefit from beta-blockers. Conversely, in patients with AF, the research found a cluster of younger patients with lower rates of prior heart attack but similar heart function to the average AF patient who had a substantial reduction in death with beta-blockers (from 15% to 9%).
The study used data collated and harmonised by the Beta-blockers in Heart Failure Collaborative Group, a global consortium dedicated to enhancing treatment for patients with heart failure. The research used individual patient data from nine landmark trials in heart failure that randomly assigned patients to either beta-blockers or a placebo. The average age of study participants was 65 years, and 24% were women. The AI-based approach combined neural network-based variational autoencoders and hierarchical clustering within an objective framework, and with detailed assessment of robustness and validation across all the trials.
The researchers say that these AI approaches could go further than this research into a specific treatment, with the potential to be applied to a range of other cardiovascular conditions and more.
Corresponding author Georgios Gkoutos, Professor of Clinical Bioinformatics at the University of Birmingham, Associate Director of Health Data Research Midlands, and co-lead for the cardAIc group, said: “Although tested in our research in trials of beta-blockers, these novel AI approaches have clear potential across the spectrum of therapies in heart failure, and across other cardiovascular and non-cardiovascular conditions.”
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 co-lead for the cardAIc group, added: “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 personalised treatment for each individual patient, taking account of their particular health circumstances to improve their well-being.”
First Author Dr Andreas Karwath, Rutherford Research Fellow at the University of Birmingham and member of the cardAIc group, added: “We hope these important research findings will be used to shape healthcare policy and improve treatment and outcomes for patients with heart failure.”
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