Study develops a new system of sensors to assess heart failure in patients

This can help the doctors assess the situation easily after a patient is discharged from the hospital

A new study has found that a sensor device that works with the help of artificial intelligence could possibly help the doctors in detecting heart failure days before the actual crisis.

According to the researchers, the sensor could help in reducing the number of readmission following the week of an initial discharge from the hospital and help the patient lead a better quality of life. The system could hopefully avert up to one in three heart failure readmission.

"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 from the University of Utah in the US.

"Being able to readily detect changes in the heart sufficiently early will allow physicians to initiate prompt interventions that could prevent rehospitalization and stave off worsening heart failure," Stehlik added.

What comes after the initial discharge?

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According to the researchers, 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 inpatient status," Stehlik said.

For the findings, published in the journal Circulation: Heart Failure, the researchers followed 100 heart failure patients, average age 68, who were diagnosed and treated at four veterans administration (VA) hospitals in Utah, Texas, California, and Florida.

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 the motion of each subject.

This information was transmitted from the sensor via Bluetooth to a smartphone 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 deviated from normal, the platform generated an indication that the patient's heart failure was getting worse.

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 readmission took place (median 6.5 days), the study said

(With inputs from agency)

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