Investigators Present Study Findings of physIQ's Novel, Artificial Intelligence-Based Continuous Patient Monitoring Solution at ACC.18
- 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 rehospitalizations.
“The results of this study suggest a highly favorable relationship between sensitivity and specificity of event detection, as well as a sufficient warning lead time for clinicians tasked with managing patients at risk for admission for heart failure exacerbation,” said Dr. Josef Stehlik, Principal Investigator of the study. “Furthermore, the study demonstrated that patients were highly compliant with wearing the biosensors throughout the monitoring period, making this a promising solution for a patient population with significant unmet clinical needs.”
There are currently over 6 million people in the United States diagnosed with heart failure and one million annual hospitalizations.1 Among them, approximately 20% of patients are readmitted within 30 days of hospital discharge, underscoring the challenges both clinicians and patients face with trying to manage this disease.2
“Heart failure continues to be a major challenge in healthcare today,” said Dr. Stephen Ondra, Chief Medical Officer of physIQ. “Sadly, there has been far too little improvement over the past 15 years with respect to our ability to proactively manage acute deterioration. As a result, healthcare has struggled to adequately improve patient quality of life and reduce the exorbitant costs associated with this chronic disease. We at physIQ are very encouraged by the results of this study as they show tremendous promise in an approach that supports a proactive care delivery model – one where clinicians can see personalized changes early enough in the deterioration process to take action that can potentially head off an acute or even catastrophic clinical event. This could be an enormous benefit to patients, providers, health systems, and payers as they embrace value-based care.”
Clinical implementations are now underway to evaluate how the physIQ solution can help clinicians manage these post-acute patients across multiple environments including skilled nursing, home health, and home self care. In each of these, patients are provided wearable biosensors for collection of real-time, continuously streaming data that, when coupled with sophisticated personalized analytics, are intended to help clinicians better understand who among their patient population may be at risk of deterioration.
1 Heart Disease and Stroke Statistics—2017 Update: A Report from the American Heart Association
2 High Heart Failure Readmission Rates: Is it the Health System’s Fault? JACC: Heart Failure Volume 5 Issue 5 May 2017
PhysIQ is a company dedicated to enabling proactive care delivery models through pinpointIQ™, its highly scalable cloud-based platform for personalized physiology analytics. Our FDA 510k-cleared data analytics platform is designed to process multiple vital signs from wearable sensors to create a personalized dynamic baseline for each individual. By mapping vital sign relationships this way, physIQ’s analytics detect subtle deviations that may be a precursor to disease exacerbation or change in health. With applications in both healthcare and clinical trial support, physIQ is transforming continuous physiological data into insight for providers, health systems, payers, and pharmaceutical and medical device companies. For more information, please visit www.physiq.com. Follow us on Twitter and LinkedIn.
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