Researchers at UVA Health have developed a powerful new risk assessment tool for predicting outcomes in heart failure patients.
The new tool, which has been made publicly available for free to clinicians, improves on existing risk assessment tools for heart failure by harnessing the power of machine learning (ML) and artificial intelligence (AI) to determine patient-specific risks of developing unfavourable outcomes with heart failure.
Heart failure occurs when the heart is unable to pump enough blood for the body’s needs. This can lead to fatigue, weakness, swollen legs and feet and, ultimately, death. As it is a progressive condition, it is important for clinicians to be able to identify patients at risk of adverse outcomes.
More than six million Americans already have a diagnosis of heart failure, a number that is expected to increase to more than eight million by 2030.
“Heart failure is a progressive condition that affects not only quality of life but quantity as well. All heart failure patients are not the same. Each patient is on a spectrum along the continuum of risk of suffering adverse outcomes,” said researcher Sula Mazimba, MD, a heart failure expert.
“Identifying the degree of risk for each patient promises to help clinicians tailor therapies to improve outcomes.”
The UVA researchers developed their new model, called CARNA, in a bid to improve the standard of care for these patients.
The research team used anonymised data drawn from thousands of patients enrolled in heart failure clinical trials previously funded by the National Institutes of Health’s National Heart, Lung and Blood Institute.
Putting the model to the test, they found it outperformed existing predictors for determining how a broad spectrum of patients would fare in areas such as the need for heart surgery or transplant, the risk of re-hospitalisation and the risk of death.
The researchers attribute the model’s success to the use of ML/AI and the inclusion of “hemodynamic” clinical data, which describe how blood circulates through the heart, lungs and the rest of the body.
Josephine Lamp, of the University of Virginia School of Engineering’s Department of Computer Science, explained: “This model presents a breakthrough because it ingests complex sets of data and can make decisions even among missing and conflicting factors.
“It is really exciting because the model intelligently presents and summarises risk factors reducing decision burden so clinicians can quickly make treatment decisions.”
By using the model, doctors will be better equipped to personalise care to individual patients, helping them live longer, healthier lives, the researchers hope.
“The collaborative research environment at the University of Virginia made this work possible by bringing together experts in heart failure, computer science, data science and statistics,” added researcher Kenneth Bilchick, MD, a cardiologist at UVA Health.
“Multidisciplinary biomedical research that integrates talented computer scientists like Josephine Lamp with experts in clinical medicine will be critical to helping our patients benefit from AI in the coming years and decades.”
The tool can be accessed here and the results of their evaluation are published here.
