Researchers have found a way to predict hypotension (low blood pressure) in surgical patients as early as 15 minutes before it sets in.
The potential applications of machine learning in healthcare are limitless -- but the problem is that everything needs to be fine-tuned and error-proof. There's no margin for error, there's no room for mistakes or miscalculations. In this case, researchers drew data from 550,000 minutes of surgical arterial waveform recordings from 1,334 patients’ records, using high-fidelity recordings that revealed more than 3,000 unique features per heartbeat. All in all, they had millions of data points with unprecedented accuracy to calibrate their algorithm. They reached sensitivity and specificity levels of 88% and 87% respectively at 15 minutes before a hypotensive event. Those levels went up to 92% each at 5 minutes before onset.
"We are using machine learning to identify which of these individual features, when they happen together and at the same time, predict hypotension," lead researcher Maxime Cannesson, MD, PhD, said in a statement. Cannesson is a professor of anesthesiology and vice chair for perioperative medicine at UCLA Medical Center.
This study is particularly important because medics haven't had a way to predict hypotension during surgery, an event that can cause a very dangerous crisis, and thus forces doctors to adapt quickly to these threatening situations. This could allow physicians to avoid potentially-fatal postoperative complications like heart attacks or kidney injuries researchers say.
"Physicians haven't had a way to predict hypotension during surgery, so they have to be reactive, and treat it immediately without any prior warning. Being able to predict hypotension would allow physicians to be proactive instead of reactive," Cannesson said.
Furthermore, unlike other applications of machine learning in healthcare, this may become a reality in the near future. A piece of software (Acumen Hypotension Prediction Index) containing the underlying algorithm has already been submitted to the FDA, and it's already been approved for commercial usage in Europe.
This is also impressive because it represents a significant breakthrough, Cannesson says.,
"It is the first time machine learning and computer science techniques have been applied to complex physiological signals obtained during surgery," Dr. Cannesson said. "Although future studies are needed to evaluate the real-time value of such algorithms in a broader set of clinical conditions and patients, our research opens the door to the application of these techniques to many other physiological signals, such as EKG for cardiac arrhythmia prediction or EEG for brain function. It could lead to a whole new field of investigation in clinical and physiological sciences and reshape our understanding of human physiology."
Results have been presented at the American Society of Anesthesiologists
