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Investigators Present Study Findings of physIQ's Novel, Artificial Intelligence-Based Continuous Pat

Monday, March 12, 2018   (0 Comments)
Posted by: Sandy Thomas
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  • VA-sponsored study deployed wearable biosensors to post-acute heart failure patients and applied FDA-cleared physIQ analytics to detect vital sign anomalies
  • Analysis demonstrated promising predictive power of AI-based analytics in terms of sensitivity, specificity, and early warning lead time
  • Study results suggest compelling potential to transform to a proactive, personalized care model for at-risk patients

 

Orlando, FL – March 12, 2018  Dr. Josef Stehlik MD MPH of the University of Utah School of Medicine and VA Salt Lake City Health Care System today presented the results of the clinical study Continuous Wearable Monitoring Analytics Predict Heart Failure Decompensation: The LINK-HF Multi-Center Study.  The VA-sponsored study evaluated how Artificial Intelligence (AI) based personalized physiology analytics from physIQ, Inc. could be applied to wearable biosensor data to predict when a patient might be at risk of hospitalization or an acute care event.  The results were presented at the American College of Cardiology’s 67th Annual Scientific Session and Expo (ACC.18).

 

The groundbreaking multi-center observational study enrolled 100 heart failure patients across four VA hospitals prior to hospital discharge and provided them a 90-day supply of wearable biosensors (VitalConnect, Inc.) and a smartphone to continuously transmit patient physiologic data to the physIQ cloud.  AI-based, FDA-cleared personalized physiology analytics from physIQ then applied machine learning algorithms to the multivariate set of continuous vital sign data to “learn” the individual’s dynamic vital sign patterns and establish a personalized baseline.  From this baseline, the physIQ analytics can detect subtle changes that may be an indicator of deteriorating health or a precursor to an acute event.  Retrospective analysis of the patient records in this study evaluated the analytics’ ability to detect vital assign anomalies that corresponded with documented clinical events and re-hospitalizations.

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