8:30am - 9:30am Wednesday, March 11

Location & Room

Orlando - Orange County Convention Center
W304A
Continuous monitoring of blood pressure to avoid the onset of arterial hypotension is crucial during surgery and critical care. Current technologies are not very effective, and there is a need to improve prediction precision and timing relative to hypotensive event onset. Recent studies suggest that the prodromal stage of hemodynamic instability is characterized by subtle, complex changes in different physiologic variables. These changes can result in unique dynamic arterial waveform “signatures" that require machine learning and complex feature extraction techniques to be utilized. In this study, machine learning approaches were applied to arterial waveforms to develop an algorithm that observes subtle signs to predict hypotension episodes. Further, for the first time, real-world evidence and advanced data analytics were leveraged to quantify the association between hypotension exposure duration for various thresholds and critically ill sepsis patient morbidity and mortality outcomes.
Speakers: 
Director R&D, Algorithms & Signal Processing,
Edwards Lifesciences
Principal,
Boston Strategic Partners, Inc.
Continuing Education Credits: 
ABPM
1.00
CAHIMS
1.00
CME
1.00
CNE
1.00
CPHIMS
1.00