Wearable Patch Driven by AI Technology Effective At Detecting Future HF Hospitalization

A new study suggests that a wearable sensor powered by artificial intelligence (AI) was associated with accurate early detection of impending rehospitalization for heart failure.

“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,” lead author Josef Stehlik, MD, MPH, co-chief of the advanced heart failure program at University of Utah Health, said in a press release. “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.”

The research team for the Multisensor Non-invasive Remote Monitoring for Prediction of Heart Failure Exacerbation (LINK-HF) study enrolled 100 subjects (98% male) and monitored subjects for up to three months using a disposable multisensor patch on the chest that captured physiological data. The study results were published in Circulation: Heart Failure.

Baseline measurements of expected physiological values were obtained after discharge using the analytical platform. The researchers estimated differences in baseline estimated vital signs and actual monitored values that triggered a clinical alert. The authors reported 35 unplanned nontrauma hospitalization events, with 24 worsening heart failure events. According to the study results, the platform was able to detect precursors of heart failure hospitalization exacerbation with 76% to 88% sensitivity and 85% specificity. The median reported time between an initial clinical alert and readmission was 6.5 days.

“There’s a high risk for readmission in the 90 days after initial discharge,”  lead author Josef Stehlik, MD, MPH, co-chief of the advanced heart failure program at University of Utah Health, said in a press release. “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.”

Out today: The LINK-HF2 study indicates that #machinelearning analytics using data from a wearable sensor could accurately predict #heartfailure exacerbation, from @JosefStehlik and colleagues #AHAJournals | https://t.co/3AYRP3AFDh pic.twitter.com/IlHGOzEhLg

— Circ: Heart Failure (@CircHF) February 25, 2020

http://twitter.com/fitmslax/statuses/1232286138817183744