Clinical Services

Facilitated Roundtable Discussion

CS2-05 - LINKHF-2 Study: Using Continuous Biosensor Data and Machine Learning Analytics to Predict Acute Care Events

Sunday, April 29
3:45 PM - 4:10 PM
Location: Education Zone, Booth 2416, Zone 3

Acute-on-chronic conditions like heart failure and COPD represent a disproportionate share of the costs associated with hospitalization and post-acute care. A majority of patients who deal with these diseases suffer from a complex set of co-morbidities that exacerbate the complexity of care and increase risk of unplanned acute care events. Recent advancements in wearable technology and advanced, machine learning-based analytics represent a potentially powerful new tool to manage ambulatory at-risk patient populations. The VA LINKHF-2 Study sought to validate how sophisticated, personalized, machine-learning, FDA-cleared analytics, when applied to continuous multivariate physiological data from wearable sensors, can detect vital sign anomalies that may be a precursor to an acute-care episode.

Within the LINKHF-2 study, 100 patients from across four different U.S. hospitals (Palo Alto, CA, Gainesville, FL, Houston, TX, and Salt Lake City, UT) were enrolled upon discharge related to heart failure decompensation. Patients were provided with a 90-day supply of wearable, disposable, bluetooth-enabled biosensors and a mobile phone with pre-paid data plan for data transmission. Data were collected via cloud-based IT platform across the 90 days and analyzed with multivariate, machine-learning analytics to detect changes from a personalized model of vital sign relationships.

The FDA-cleared analytics being evaluated build a personalized model of vital sign dynamics based on biosensor data captured the first 36 hours after hospital discharge. During that model training period, the system "learns" an individual patient's unique vital sign relationships (HR, RR, and activity) across the full spectrum of activities one would expect in an ambulatory environment. With this, the system analytics develop a personalized baseline by which to measure changes that may be a precursor to an acute care event. After the 36-hour model training period, the system automatically transitions from "learning" mode into "monitoring" mode where it indicates subtle changes that may indicate compensatory behavior within the cardiopulmonary control loop. Personalized anomalies are indicated via time-series plotted index of change from baseline. The higher the index, across a longer period of time, the greater the change from baseline.

This was an observational study. Current standard of care was delivered while data were being collected in the background. A retrospective analysis compared results of the personalized analytics relative to the medical record. The intent of the study was to evaluate 1) how well the personalized, machine learning-based physiology analytics detected acute care events in an ambulatory environment and 2) how early the analytics indicated health deterioration relative to the acute care event. The ultimate goal of the solution is to provide clinicians with a scalable tool that will allow them to manage a post-acute population using passive data collection and sophisticated analytics. The intended benefit is that large, at-risk patient populations can be managed by exception, as indicated by each individual's changes in cardiopulmonary physiology. A simplified ROC analysis yielded an AUC ~0.81, enabling very high specificity to alleviate false alert burden, while still delivering a potential cost-saving sensitivity.

Learning Objectives:

Matt Pipke

Chief Technology Officer
physIQ

As CTO and cofounder at PhysIQ, Matt steers the product roadmap, focusing on the future of medicine at the intersection of machine learning and clinical applications. Originally trained in physics, Matt switched careers looking to bridge the gap between pure science and real-world applications. He became a patent attorney and spent most of a decade helping clients convert their scientific breakthroughs into valuable business assets. As the internet exploded, Matt left private practice to pursue his passion for artificial intelligence and machine learning systems in the start-up world. Prior to PhysIQ, he managed the R&D team as Chief Technology Officer and IP counsel at SmartSignal, which delivered a machine learning-based platform for predictive monitoring of equipment health to the power, aviation and transportation industries. He also served as legal counsel for all licensing deals, intellectual property, transactions and partnerships of the company. In addition to his role as CTO at PhysIQ, Matt has run the company’s clinical trials and was the regulatory lead for the company’s foundational FDA clearance. Matt has a degree in physics from the University of Chicago and a Juris Doctor from Loyola University. He enjoys coding in MATLAB and Python, is known amongst colleagues for an esoteric knowledge of world history, and also speaks Mandarin Chinese and German.

Presentation(s):

Send Email for Matt Pipke

Josef Stehlik

Professor of Medicine
University of Utah

Josef Stehlik, MD, MPH is a Christi T Smith Professor of Medicine at the University of Utah School of Medicine. Dr. Stehlik has received his medical degree from Charles University in Prague and Masters in Public Health degree from Harvard School of Public Health. He completed his training in Internal Medicine and in Cardiovascular Diseases at Allegheny General Hospital, MCP*Hahnemann University and advanced training in Heart Failure and Transplantation at the Cleveland Clinic.

Dr. Stehlik has been on faculty at the University of Utah since 2004 and serves as Medical Director of the Heart Transplant Program and Co-chief of the Advance Heart Failure Program at the University of Utah Hospital and the Salt Lake City Veterans Affairs Medical Center. He has been active in clinical work, education and research in the areas of advanced heart failure, heart transplantation and mechanical circulatory support.

Presentation(s):

Send Email for Josef Stehlik


Assets

CS2-05 - LINKHF-2 Study: Using Continuous Biosensor Data and Machine Learning Analytics to Predict Acute Care Events



Attendees who have favorited this

Please enter your access key

The asset you are trying to access is locked. Please enter your access key to unlock.

Send Email for LINKHF-2 Study: Using Continuous Biosensor Data and Machine Learning Analytics to Predict Acute Care Events