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 (PbtO2), 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 PbtO2 were reliably detected using user-defined thresholds and alert throttling. BSR trends computed by the platform correlated highly with manual neurologist markings (r2 0.633-0.781; p < 0.0001). The platform identified EEG electrodes with poor signal quality with 95% positive predictive value, and reduced latency of technician response by 93%. CONCLUSION: This study validates a flexible real-time platform for monitoring and interpreting ICU data and notifying caretakers of actionable results, with potential to reduce the manual burden of continuous monitoring services on care providers. SIGNIFICANCE: This work represents an important step toward facilitating translational medical data analytics to improve patient care and reduce health care costs.
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 (PbtO2), 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 PbtO2 were reliably detected using user-defined thresholds and alert throttling. BSR trends computed by the platform correlated highly with manual neurologist markings (r2 0.633-0.781; p < 0.0001). The platform identified EEG electrodes with poor signal quality with 95% positive predictive value, and reduced latency of technician response by 93%. CONCLUSION: This study validates a flexible real-time platform for monitoring and interpreting ICU data and notifying caretakers of actionable results, with potential to reduce the manual burden of continuous monitoring services on care providers. SIGNIFICANCE: This work represents an important step toward facilitating translational medical data analytics to improve patient care and reduce health care costs.
Authors: Jed A Hartings; J Adam Wilson; Jason M Hinzman; Sebastian Pollandt; Jens P Dreier; Vince DiNapoli; David M Ficker; Lori A Shutter; Norberto Andaluz Journal: Ann Neurol Date: 2014-09-17 Impact factor: 10.422
Authors: David O Okonkwo; Lori A Shutter; Carol Moore; Nancy R Temkin; Ava M Puccio; Christopher J Madden; Norberto Andaluz; Randall M Chesnut; M Ross Bullock; Gerald A Grant; John McGregor; Michael Weaver; Jack Jallo; Peter D LeRoux; Dick Moberg; Jason Barber; Christos Lazaridis; Ramon R Diaz-Arrastia Journal: Crit Care Med Date: 2017-11 Impact factor: 7.598
Authors: Alistair E W Johnson; Mohammad M Ghassemi; Shamim Nemati; Katherine E Niehaus; David A Clifton; Gari D Clifford Journal: Proc IEEE Inst Electr Electron Eng Date: 2016-01-25 Impact factor: 10.961
Authors: M Yogarajah; H W R Powell; D Heaney; S J M Smith; J S Duncan; S M Sisodiya Journal: J Neurol Neurosurg Psychiatry Date: 2008-10-17 Impact factor: 10.154
Authors: John M Bernabei; Olaoluwa Owoputi; Shyon D Small; Nathaniel T Nyema; Elom Dumenyo; Joongwon Kim; Steven N Baldassano; Christopher Painter; Erin C Conrad; Taneeta M Ganguly; Ramani Balu; Kathryn A Davis; Joshua M Levine; Jay Pathmanathan; Brian Litt Journal: Crit Care Explor Date: 2021-07-13