IRIS: A Modular Platform for Continuous Monitoring and Caretaker Notification in the Intensive Care Unit

Autor: Jay Pathmanathan, Brian Litt, Steven N. Baldassano, Chloe E. Hill, Joost B. Wagenaar, Amanda Christini, Shawniqua Williams Roberson, Michael A. Gelfand, Javier Echauz, Ramani Balu, Damien Leri, Brittany H. Scheid, Brian Oommen, Paulomi Kadakia Bhalla, John M. Bernabei
Rok vydání: 2020
Předmět:
Adult
medicine.medical_specialty
Critical Care
Intracranial Pressure
02 engineering and technology
Electroencephalography
Article
law.invention
03 medical and health sciences
0302 clinical medicine
Health Information Management
law
Health care
0202 electrical engineering
electronic engineering
information engineering
medicine
Humans
Diagnosis
Computer-Assisted
Oximetry
Electrical and Electronic Engineering
Latency (engineering)
Monitoring
Physiologic
Intracranial pressure
Brain Chemistry
medicine.diagnostic_test
business.industry
Technician
020208 electrical & electronic engineering
Continuous monitoring
Signal Processing
Computer-Assisted
Intensive care unit
Computer Science Applications
Intensive Care Units
Burst suppression
Emergency medicine
business
Algorithms
030217 neurology & neurosurgery
Biotechnology
Zdroj: IEEE J Biomed Health Inform
ISSN: 2168-2208
2168-2194
DOI: 10.1109/jbhi.2020.2965858
Popis: Objective: New approaches are needed to interpret large amounts of physiologic data continuously recorded in the ICU. We developed and prospectively validated a versatile platform (IRIS) for real-time ICU physiologic monitoring, clinical decision making, and caretaker notification. Methods: IRIS was implemented in the neurointensive care unit to stream multimodal time series data, including EEG, intracranial pressure (ICP), and brain tissue oxygenation (P bt O 2 ), from ICU monitors to an analysis server. IRIS was applied for 364 patients undergoing continuous EEG, 26 patients undergoing burst suppression monitoring, and four patients undergoing intracranial pressure and brain tissue oxygen monitoring. Custom algorithms were used to identify periods of elevated ICP, compute burst suppression ratios (BSRs), and detect faulty or disconnected EEG electrodes. Hospital staff were notified of clinically relevant events using our secure API to route alerts through a password-protected smartphone application. Results: Sustained increases in ICP and concordant decreases in P bt O 2 were reliably detected using user-defined thresholds and alert throttling. BSR trends computed by the platform correlated highly with manual neurologist markings (r 2 0.633-0.781; p
Databáze: OpenAIRE
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