AI-powered sensor can predict heart failure days before it occurs

This wearable AI-enabled sensor technology can be used by doctors to remotely monitor heart patients and provide early warning of potential heart failure

A study led by the University of Utah Health in the US found that a new wearable sensor, working in conjunction with artificial intelligence, was able to predict worsening heart failure in the days leading up to hospitalization. The researchers say the system could eventually help avert up to one in three heart failure readmissions in the weeks following initial discharge from the hospital and help patients sustain a better quality of life.

"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," says the study's lead author, Josef Stehlik, M.D., M.P.H, co-chief of the advanced heart failure program at U of U Health.

Setting of the study

The researchers followed 100 heart failure patients, average age 68, who were diagnosed and treated at four hospitals in America. After 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 subject.

 

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

 

The information gathered was transmitted from the sensor to a smartphone via Bluetooth technology and later passed to an analytics platform, which was developed by PhysIQ, on a secure server. It derived heart rate, heart rhythm, respiratory rate, walking, sleep, body posture and other normal activities. With the use of artificial intelligence the analytics establishes a normal baseline for the participants. This data generated an indication that the patient’s heart failure was getting worse each time it deviated from normal.

Overall, the system accurately predicted the impending need for hospitalization more than 80 percent of the time. On average, this prediction occurred 10.4 days before a readmission took place.

"There's a high risk for readmission in the 90 days after initial discharge", Stehlik says. "If we can decrease this readmission rate through monitoring and early intervention, that's a big advance.

"We're hoping even in patients who might be readmitted that their stays are shorter, and the overall quality of their lives will be better with the help of this technology."

Heart Failure as a global pandemic

Heart failure is a global pandemic affecting at least 26 million people worldwide and is increasing in prevalence. Up to 30% of patients experiencing heart failure, will likely be readmitted to the hospital after 90 days of being discharged with recurrent symptoms including shortness of breath, fatigue and fluid buildup.

"Those individuals who have repeated hospitalizations for heart failure have significantly higher mortality" says 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. "Even if patients survive, they have poor functional capacity, poor exercise tolerance and low quality of life after hospitalizations. This patch, this new diagnostic tool, could potentially help us prevent hospitalizations and decline in patient status."

Follow-up study

Next, the researchers plan to conduct a large clinical trial that will not only use the system to alert doctors of changes in a patient's condition but also track if early intervention based on these alerts lead to fewer rehospitalizations for heart failure.

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