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Bedside monitors in cardiac intensive care units measure everything from patients’ blood pressure and blood oxygen levels to their heart rate and rhythm.
The numerical and waveform measurements provide valuable information for doctors and nurses monitoring patients for immediate signs of distress. But once that information flashes across a screen, it is gone forever.
What if the data could be captured and crunched in real time?Could big data help predict which patients might be susceptible to infections that could lead to potentially deadly sepsis?
Could it identify patients who are at heightened risk for readmission?
Raimond “Rai” Winslow, a national leader in computational medicine based at Northeastern’s Roux Institute in Portland, Maine, thinks so.
He is a principal investigator in a research project being conducted with MaineHealth that aims to take complex data sets about patients in the state’s largest cardiothoracic ICU and translate them into metrics that could better predict adverse outcomes—in time for physicians to avert them whenever possible.
The project is known as HEART for Healthcare Enabled by AI in Real-Time and is funded in large part by Northeastern University’s Impact Engine program.
“The goal of HEART is to use machine learning models to collect data from patients while they are recovering in the CT ICU, and at every moment of time a new measurement comes in to predict their risk for developing a complication,” says Winslow, director of Life Science and Medicine Research at the Roux.
Twenty percent of cardiac surgical patients develop complications, and of those patients, 20% do not survive, he says.
Physicians can use the level of risk assigned to patients by computational medicine to determine which patients need more intense treatment and which are doing fine, he says.
“The idea being that if you can make this prediction in advance before the complication has actually occurred you can intervene and help with it.”
Winslow says he started working on the HEART project about a year ago, after coming to the Roux Institute from the Johns Hopkins University School of Medicine, where he was the founding director of the Institute for Computational Medicine.
Dr. Douglas Sawyer, chief academic officer at MaineHealth and the Maine Medical Center, posed the project to him, and “it’s moved quickly,” Winslow says.
He says he expects to enroll recovering cardiac surgery patients in MaineHealth’s 12-bed ICU in clinical trials in 18 months.But first the researchers have to develop a model of the cardiovascular disease process using large patient datasets and animal models of disease.
The next step is to take streaming data from individual patients, send it to the cloud for de-identification, and analyze what the data bodes for the patients’ recovery.
“We use machine learning methods applied to population data to learn an algorithm that could reliably select patients with sepsis who are going to develop septic shock,” for instance, Winslow says.
If the estimation of developing the often-fatal syndrome is 90%, physicians likely will take a different approach than if the risk for a particular patient is 20%, he says.
The final step is to find a way to deliver the information to medical staff in a digestible, timely format, Winslow says.
“We don’t tell (physicians) what to do, what interventions to take,” he says. “We’re simply notifying them that that patient is headed toward a complication. ‘Use your knowledge and your savvy and your intuition and your experience to treat that patient as you see fit.’”
Winslow predicts the HEART project will help reduce readmissions by helping patients recover more fully in the hospital.
