Researchers from Salt Lake City-based University of Utah Health and VA Salt Lake City Health Care System led the study, which used the AI sensor for 24-hour monitoring of 100 heart failure patients’ continuous ECG and motion for up to three months.
The sensor used Bluetooth to pass the collected data to a smartphone app and analytics platform. The AI-equipped platform calculated heart rate, heart rhythm, respiratory rate, body posture and other measurements for each participant, then alerted care teams when those measurements deviated from a participant’s individual baseline, indicating worsening heart failure.
According to the study, the AI system correctly predicted the need for hospitalization more than 80 percent of the time, an average of about 10 days before the readmission occurred.
“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,” Josef Stehlik, MD, lead study author and co-chief of the advanced heart failure program at University of Utah Health, said in a statement. “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.”
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