Machine learning can help predict the life expectancy of heart failure patients, according to new research published in the European Journal of Heart Failure.
One of the study’s authors, Avil Yagil, PhD, of the University of California San Diego, was diagnosed with heart failure in 2012 and received a heart transplant in 2016. This led Yagil to think about what he could do to help other patients going through a similar experience.
“In my day job, I use machine learning to understand a vast amount of information and measurements of particles and how they interact,” Yagil said in a prepared statement. “The human body is even more complex, but the medical profession isn't utilizing the technologies that are needed to capture the multi-dimensional correlations between the measurements, such as lab tests and vital signs, and the outcomes. We hypothesized that such methodology and techniques could contribute to improving the prognosis and treatment of heart patients with heart failure.”
Yagil and colleagues developed an algorithm using data from more than 5,000 patients treated for heart failure by UC San Diego Health. The algorithm crafted a risk score based on a number of factors, including diastolic blood pressure, creatinine, white blood cell count, red blood cell distribution and more. This new AI model was able to predict the patient’s life expectancy 88% of the time, a performance “substantially better than other popular published models.”
“This tool gives us insight, for example, on the probability that a given patient will die from heart failure in the next three months or a year,” co-author Eric Adler, MD, of the Cardiovascular Institute at UC San Diego Health, said in the prepared statement. “This is incredibly valuable. It allows us to make informed decisions based on a proven methodology and not have to look into a crystal ball.”
Now a few years removed from his heart transplant, Yagil noted that he is once again doing all of the things he loves the most.
“I am back to playing sports and enjoying life with my family after my heart transplant,” he said. “I am incredibly grateful to everyone at UC San Diego Health who saved my life and honored that my personal experience has led to a partnership and development that may help others.”