A machine learning algorithm is able to predict potentially dangerous low blood pressure that can occur during surgery by detecting subtle signs in routinely collected physiological data in surgical patients.
Dangerously low blood pressure—hypotension—can lead to complications such as postoperative heart attack, acute kidney injury and even death. The algorithm was able to accurately predict an intraoperative hypotensive event 15 minutes before it occurred in 84 percent of cases, 10 minutes before in 84 percent of cases, and five minutes before in 87 percent of cases.
Researchers leveraged two datasets to build and validate the predictive algorithm, based on recordings of the increase and decrease of blood pressure in the arteries during a heartbeat—including episodes of hypotension. For each heartbeat, they were able to derive 3,022 individual features from the arterial pressure waveforms, producing more than 2.6 million bits of information used to build the algorithm.
In a new study published this week in the journal Anesthesiology, machine learning was able to identify which of these individual features—when they happen together and at the same time—predict hypotension and could potentially reduce the risk of harm to patients.