When Avi Yagil, PhD, Distinguished Professor of Physics at University of California San Diego flew home from Europe in 2012, he thought he had caught a cold from his travels. When a “collection of pills” did not improve his symptoms, his wife encouraged him to see a doctor.
Further tests revealed something far more life-threatening to Yagil than the common cold. “A chest X-Ray showed my lungs were flooded with fluid, and a subsequent echocardiogram found I had damage to my heart.”
Yagil was diagnosed with heart failure. “UC San Diego Health cardiologists tried to manage my condition with medication, but all systems were failing as my heart struggled to keep me alive.”
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In June 2016, Yagil received a heart transplant. “I consider June 17 my second birthday.”
While Yagil recovered from surgery, he began thinking about how he could improve the process for patients like him.
“In my day job, I use machine learning to understand a vast amount of information and measurements of particles and how they interact,” he said. “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.”
So Yagil teamed up with his doctors, Eric Adler, MD, cardiologist and director of cardiac transplant and mechanical circulatory support and Barry Greenberg, MD, Distinguished Professor of Medicine at UC San Diego School of Medicine and director of the advanced heart failure treatment program, both at the Cardiovascular Institute at UC San Diego Health.
“We wanted to develop a tool that predicted life expectancy in heart failure patients,” Adler said. “There are apps where algorithms are finding out all kinds of things, like products you want to purchase. We needed a similar tool to make medical decisions. Predicting mortality is important in patients with heart failure. Current strategies for predicting risk, however, are only modestly successful and can be subjective.”
Alder, Yagil and Greenberg, as well as a diverse team of cardiologists and physicists, developed a machine learning algorithm based on de-identified electronic health records data of 5,822 hospitalized or ambulatory patients with heart failure at UC San Diego Health.
From this model, a risk score was derived that determined low- and high-risk of death by identifying eight readily available variables collected for the majority of patients with heart failure: