PhysIQ and U.S. Veteran’s Affairs Publish Breakthrough Study Predicting Heart Failure Hospitalization up to 10 days in Advance using AI

1. PhysIQ and the U.S. VA publish results of a clinical trial that demonstrates how artificial intelligence (AI) applied to continuous wearable sensor data may predict hospitalizations.
2. When coupled with the high sensor wear compliance rates, the 7-10-day early warning timeframe suggests this approach has great promise to reduce hospitalization and improve quality of life of patients with heart failure.
3. PhysIQ’s extensive IP portfolio and several FDA cleared AI-based algorithms paved the way for the results seen in this study.

CHICAGO, IL – physIQ, Inc. and the US Department of Veteran’s Affairs (VA) today published the results of a breakthrough study aimed at validating the ability to detect the onset of heart failure exacerbation using wearable sensors and machine learning-based personalized physiology analytics.  Published in Circulation – Heart Failure, a journal of the American Heart Association, the LINK-HF study was designed to assess the ability to predict rehospitalization due to heart failure exacerbation using sophisticated analytics applied to continuous wearable sensor data.  In the study, 100 patients were enrolled upon discharge from heart failure hospitalization across four VA hospitals and monitored continuously and for up to 90 days without intervention. Post hoc data analysis indicated a mean detection lead time of as high as 10.4 days prior to the hospitalization or ER visit with as high as 88% sensitivity and 86% specificity. Such a lead time interval should permit intervention aimed at preventing hospitalization. Read more…