UVA Health researchers have developed a powerful new risk assessment tool to predict outcomes in patients with heart failure. The researchers have made the tool available to the public and free of charge to doctors.
The new tool enhances existing heart failure risk assessment tools by harnessing the power of machine learning (ML) and artificial intelligence (AI) to determine patient-specific risks of developing unfavorable outcomes with heart failure.
“Heart failure is a progressive condition that affects not only the quality of life but also the quantity. Not all patients with heart failure are the same. Each patient is on a spectrum along the continuum of risk for heart failure. adverse events,” said researcher Sula Mazimba, MD, a heart failure expert. “Identifying each patient’s level of risk promises to help doctors tailor therapies to improve outcomes.”
About heart failure
Heart failure occurs when the heart cannot pump enough blood for the body’s needs. This can lead to fatigue, weakness, swelling of the legs and feet, and ultimately death. Heart failure is a progressive condition, so it is extremely important for doctors to be able to identify patients at risk for adverse outcomes.
Additionally, heart failure is a growing problem. More than 6 million Americans already suffer from heart failure, and that number is expected to increase to more than 8 million by 2030. UVA researchers developed their new model, called CARNA, to improve care for these patients. (Finding new ways to improve patient care in Virginia and beyond is a key component of UVA Health’s first 10-year strategic plan.)
The researchers developed their model using anonymized data drawn from thousands of patients enrolled in heart failure clinical trials previously funded by the National Heart, Lung, and Blood Institute of the National Institutes of Health. When testing the model, they found that it outperformed existing predictors in determining how a broad spectrum of patients would fare in areas such as need for heart surgery or transplant, risk of rehospitalization, and risk of death.
The researchers attribute the model’s success to the use of ML/AI and the inclusion of “hemodynamic” clinical data, which describes how blood circulates through the heart, lungs, and the rest of the body.
“This model represents a breakthrough because it ingests complex sets of data and can make decisions even among missing and conflicting factors,” said researcher Josephine Lamp of the Department of Computer Science at the University of Virginia School of Engineering. “It’s really exciting because the model intelligently presents and summarizes risk factors that reduce decision burden so doctors can make treatment decisions quickly.”
By using the model, doctors will be better equipped to personalize care for each patient, helping them live longer, healthier lives, the researchers hope.
“The University of Virginia’s collaborative research environment made this work possible by bringing together experts in heart failure, informatics, data science and statistics,” said researcher Kenneth Bilchick, MD, a UVA Health cardiologist. “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 years and decades to come.”
Published findings
The researchers have made their new tool available online for free at https://github.com/jozieLamp/CARNA.
Additionally, they have published the results of their CARNA evaluation in the American Heart Journal. The research team consisted of Lamp, Yuxin Wu, Steven Lamp, Prince Afriyie, Nicholas Ashur, Bilchick, Khadijah Breathett, Younghoon Kwon, Song Li, Nishaki Mehta, Edward Rojas Pena, Lu Feng and Mazimba. The researchers have no financial interest in the work.
The project was based on one of the winning submissions from the National Heart, Lung, and Blood Institute’s Big Data Analytics Challenge: Creating New Paradigms for Heart Failure Research. The work was supported by the National Science Foundation Graduate Research Fellowship, grant 842490, and NHLBI grants R56HL159216, K01HL142848, and L30HL148881.
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