The self-adhesive bandage accurately predicts a worsening of the heart health of repeat offenders several days before the most obvious symptoms appear
Heart failure has a big impact on our health. In the United States, for example, 6.2 million people suffer from heart failure and the associated costs to the health care system. During the first three months after leaving the hospital, there is a high risk of relapse, with a probability of up to 30%. Researchers at George E. Wahlen VA Medical Center and the University of Utah School of Medicine, both in Salt Lake City, focused on a non-invasive solution to the problem. The results were published in the American Heart Association’s journal Circulation: Heart Failure.
The study was conducted on about one hundred heart failure patients, averaging 68 years of age, who were admitted to four different hospitals. Participants wore an adhesive patch on their chest 24 hours a day for a period of 30 to 90 days after discharge from the hospital. The sensor monitored heart rate, heart rate, respiratory rate and physical activities such as walking, sleeping and body posture for each participant. The data was transmitted from the sensor via Bluetooth to a smartphone and then uploaded to an analysis platform.
Thanks to artificial intelligence, the data was analyzed by a machine learning algorithm. If a patient’s data deviated from the average behavior, the algorithm generated an alarm that indicated a worsening of the heart’s state of health. The technology was able to predict the risk of worsening in 80% of cases 6 or 7 days earlier than the most severe symptoms. The study must now be expanded to include a more varied and heterogeneous sample. The tests will also cover female patients, patients with different types of insufficiencies and variations in algorithm processing.