Researchers at the University of Birmingham
have developed a new way to identify which patients with heart failure will
benefit from treatment with beta-blockers.
Heart failure is one of the most common
heart conditions, with substantial impact on patient quality of life, and a
major driver of hospital admissions and healthcare cost.
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.
Published in The Lancet, the study used a
series of artificial intelligence (AI) techniques to deeply interrogate data
from clinical trials.
The research showed that the AI approach
could take account of 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 was identified
with reduced benefit from beta-blockers (combination of older age, less severe
symptoms and lower heart rate than average). Conversely in patients with AF,
the research found a cluster of patients who had a substantial reduction in
death with beta-blockers (from 15% to 9% in younger patients with lower rates
of prior heart attack but similar heart function to the average AF patient).
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. The
study uses data collated and harmonized by the Beta-blockers in Heart Failure
Collaborative Group, a global consortium dedicated to enhancing treatment for
patients with heart failure.
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.”
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 & 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.”
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 research was presented this week at the
ESC Congress 2021, hosted by the European Society of Cardiology - a non-profit
knowledge-based professional association that facilitates the improvement and
harmonisation of standards of diagnosis and treatment of cardiovascular
diseases.
Notes to Editors
• To arrange media interviews please contact the University of
Birmingham press office, via +44 (0)7789 921 165.
• Karwath et al (Aug, 2021). ‘Redefining beta-blocker response in
heart failure patients with sinus rhythm and atrial fibrillation: a machine
learning cluster analysis’. The Lancet. DOI: 10.1016/S0140-6736(21)01638-X
• The University of Birmingham is ranked amongst the world’s top 100
institutions, and its work brings people from across the world to Birmingham,
including researchers and teachers and more than 6,500 international students
from nearly 150 countries.
