University of Utah-led Study Reveals Powerful AI-based Technology can Predict Hospitalization for Heart Patients

University of Utah-led Study Reveals Powerful AI-based Technology can Predict Hospitalization for Heart Patients TrialsiteN

Investigators from the University of Utah Health and VA Salt Lake City Health Care System conclude from a recent study that a new wearable sensor that works in conjunction with artificial intelligence technology may help doctors remotely detect critical changes in heart failure patients—even days before a health crisis occurs—and even could prevent hospitalization. These Utah-based researchers conclude that it could be possible to eventually help avert up to one in three heart failure readmissions in the weeks following initial discharge from the hospital and support a higher quality of life for patients.


This promising recent study results were published in Circulation: Heart Failure an American Heart Association journal. 

Health Challenge

About 6.2 million Americans live with heart failure and it is the top hospital discharge diagnosis in the United States.  Around 30% of these patients will likely be readmitted to the hospital within 90 days of discharge with recurrent symptoms including shortness of breath, fatigue and fluid buildup. In many cases, hospitalization diminishes a patient’s ability to care for themselves independently. Those patients with higher repeat hospitalizations face higher probability of death.

The Study

The research team followed 100 heart failure patients who were diagnosed and treated at four VA hospitals in Salt Lake City; Houston, Texas; Palo Alto, California and Gainesville, Florida. Post discharge, participants wore an adhesive sensor patch on their chests 24 hours a day for up to three months. The sensor monitored continuous electrocardiogram (ECG) and motion of each participant.

Digital Elements

In addition to the sensors, the study involved the transmission of data from the sensor, via Bluetooth, to a smart phone and then passed on to an analytics platform, developed by PhysIQ, on a secure server, which derived heart rate, heart rhythm, respiratory rate, walking, sleep, body posture and other normal activities. Using artificial intelligence, the analytics established a normal baseline for each patient. When the data is deviated from normal, the platform generated an indication that the patient’s heart failure was getting worse.

The Results

The system predicted the impending need for hospitalization more than 80% of the time. On average, this prediction occurred 10.4 days before a readmission took place (median 6.5 days).

Study lead Josef Stehlik, MD, MPH, co-chief of the advanced heart failure program at U of U Health, commented, “This study shows that we can accurately predict the likelihood of hospitalization for heart failure deterioration well before doctors and patients know that something is wrong.”

Who is PhysIQ?

PhysIQ purports to be the only FDA-cleared personalized analytics that learns a patient’s baseline and detects subtle changes to provide unprecedented insight. Based in Chicago, IL, PhysIQ has raised over $20 million in venture capital investment since 2013, reports Crunchbase. The company employs between 25 and 50. This successful University of Utah-led study could bring them more attention.

Lead Research/Investigators

·       Josef Stehlik, M.D., M.P.H, co-chief of the advanced heart failure program at U of U Health

·       Biykem Bozkurt, M.D., Ph.D., a study co-author, director of the Winters Center for Heart Failure Research at the Baylor College of Medicine in Houston