The Intersection of Machine Learning and Medicine: New UW Medicine Study Reveals Key Predictors of Right Heart Failure Following LVAD Implantation

The Intersection of Machine Learning and Medicine: New UW Medicine Study Reveals Key Predictors of Right Heart Failure Following LVAD Implantation

If you’re a “Grey’s Anatomy” fan, chances are the word “LVAD wire” probably means something to you. Unlike the show, left ventricular assist devices (LVADs) possess capabilities far beyond the realm of hospital romance.

An LVAD is a mechanical pump designed for patients with advanced heart failure. They are implanted in the apex of the heart to help the lower left chamber (left ventricle) pump blood out of the ventricle, through the aorta and to the rest of the body. The pump is then attached to a cable coming out of the body and into an external computer, which provides alarms and messages that help operate the system. LVAD devices extend the the lives of thousands of heart failure patients each year.

However, these devices are not without risk of serious complications. According to UW Medicine, up to 20% of LVAD recipients experience right heart failure (RHF) due to the inability of the right ventricle to withstand the sudden resurgence of pump blood flow. This results in a lower chance of survival, or even immediate death, a few days after implantation.

This result, often terribly unpredictable, piqued the curiosity of researchers at UW Medicine.

Through the use of a machine learning (ML) system trained to search for 186 different factors, experts have identified the top 30 pre-implant patient factors that are strongly associated with right heart failure after implantation of the LVAD.

“Many patients, even if they survive, have a very poor quality of life and one of the main contributors to this is RHF,” Dr Song Liassistant professor of cardiology at UW Medicine and one of the authors of this study, said. “It’s hard to predict in advance, so we were interested in trying a new method to improve those predictions.”

This new method refers to the revolutionary logistics of Explainable ML. The ability to analyze hundreds of variables at the same time makes Explainable ML much better equipped for the high-dimensional interactions between the factors involved in this study.

“Many other AI machine learning models are really just black boxes, which limits its usefulness in medicine,” Li said. apply ML correctly.”

Standard ML models are notoriously limited in proving correlations without explanations, often referred to as black boxes.

Based on a sample of 20,000 patients with LVAD, the study found that the top five predictors of RHF are INTERMACS profile, model for end-stage liver disease score, the number of inotropic infusionshemoglobin and race.

Of the 186 pre-implant factors, reducing the possible predictors of RHF to five is an important finding that will help physicians assess and manage a patient’s risk even before surgery takes place.

One factor in particular heightened the interest of Li and his team: race. African Americans had a higher risk of acute RHF after LVAD implantation compared to their white counterparts.

“It’s very confusing to see why and we want to dig deeper to find out what could be causing this correlation,” Li remarked. “It’s something we’re analyzing right now, actually.”

The study also acknowledged the limitations of its data. The progression of RHF after LVAD implantation is not solely dependent on these pre-implant factors, and the circumstances of operative and postoperative care must also be considered.

Going forward, Li explained how they plan to use the ML model to simulate different optimization strategies.

“Before we even think about testing it on real patients, we can see how much of a difference it would really make,” Li said.

This unique intersection between machine learning and medicine is proving to be a successful endeavor – a collaboration that only a school like UW could discover and deliver.

Contact contributing writer Meha Singal at Twitter: @mehaha23

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