As well as spotting acute heart failure more accurately than heart protein blood tests on their own, CoDE-HF was especially precise in difficult to diagnose patient groups – such as older people and those with pre-existing medical conditions.
The team are currently conducting further studies to understand how this decision-support tool will work in the hospital environment and influence patient outcomes.
Dr Ken Lee, cardiology specialist registrar and clinical lecturer at the University of Edinburgh, said: “Heart failure can be a very challenging diagnosis to make in practice. We have shown that CoDE-HF, our decision-support tool, can substantially improve the accuracy of diagnosing heart failure compared to current blood tests.”
Mr Dimitrios Doudesis, research fellow and data scientist at the University of Edinburgh, said: “Our study demonstrates that the application of artificial intelligence in healthcare has major potential to help doctors deliver more personalized patient care.”
Professor Nicholas Mills, British Heart Foundation Professor of Cardiology at the University of Edinburgh and Consultant Cardiologist, said: “The application of artificial intelligence in decision-support tools as CoDE-HF to deliver more personalized care is particularly important given our ageing patient population who are living longer with more pre-existing medical conditions. We are currently conducting further studies to identify ways to implement CoDE-HF effectively in routine care.”
Professor Sir Nilesh Samani, Medical Director at the British Heart Foundation, said: “Early detection of acute heart failure is vital to improve outcomes, but in practice accurate diagnosis is challenging, because the main symptom – breathlessness – can be caused by other conditions. The new tool developed in this study identifies people with acute heart failure more accurately, allowing patients to get the lifesaving treatment they need sooner.”
Source: Healthcare in Europe
