Recurring acute ICP elevations occur frequently and unpredictably among severe brain injury patients. ICP elevation can cause cerebral ischemia and lead to deadly brain herniation if untreated. Hence, prompt recognition and treatment of rising ICP are critical in managing severe brain injury patients. However, existing protocols in most neurocritical care units are reactive where bedside nurses, in response to simple threshold-crossing alarms, have to check numerical display of ICP on monitors to manually establish whether the alarm is a true one before initiating treatment. Acute ICP elevation is accompanied by distinctive ICP pulse morphological changes. By utilizing ICP pulse morphological metrics as input, we can accurately recognize precursors to ICP elevation to alert nurses and free them from a cognitively demanding process of establishing whether a consistent ICP elevation triggers the alarm. We therefore propose to deploy a previously developed accurate ICP elevation prediction model on an open-source model hosting platform to monitor continuous ICP signals and alert bedside nurses. Using this alerting system, we will further investigate the principal physiological abnormalities associated with acute ICP elevation showing different precursory ICP patterns prior to onset of elevation. We will pursue the following three aims: 1) To develop an alerting system for ICP elevation based on a model hosting platform;2) To investigate whether the ICP alerting system helps nurses more efficiently manage ICP. 3) To detect consistent physiological abnormalities associated with acute ICP elevation. Our long-term goal is to advance intensive care monitoring so that continuous signals from monitors are fully explored to integrate with the rest of clinical data in an electronic medical record (EMR) system to enhance clinical decision making. This project represents an effort piloting a platform-based approach towards overcoming translational barriers that impede the process of making advanced predictive analytics available at point of care. Therefore, broad impacts from this project are related to future efforts at leveraging this open model hosting platform to facilitate the translation of additional predictive models in other ICUs.
Recurring acute intracranial pressure (ICP) elevation occurs frequently, up to more than 20 in a 12-hour nursing shift, and unpredictably among severe brain injury patients. These acute ICP elevations needs prompt treatment before they impair blood flow to the brain and cause deadly brain herniation. The present work is built upon our previously developed algorithm of detecting acute ICP elevation and an open software platform for hosting predictive algorithms to further develop and evaluate a real-time ICP elevation alerting system that will provide accurate alerts of impending ICP elevation. Rigorous human factor engineering principles and techniques will be adopted in developing this system and we will further leverage this real-time alerting system to investigate the principal physiological abnormalities that are associated with accurate ICP elevation.
|Connolly, Mark; Vespa, Paul; Pouratian, Nader et al. (2015) Characterization of the relationship between intracranial pressure and electroencephalographic monitoring in burst-suppressed patients. Neurocrit Care 22:212-20|
|Connolly, Mark; He, Xing; Gonzalez, Nestor et al. (2014) Reproduction of consistent pulse-waveform changes using a computational model of the cerebral circulatory system. Med Eng Phys 36:354-63|
|Kim, Sunghan; Hamilton, Robert; Pineles, Stacy et al. (2013) Noninvasive intracranial hypertension detection utilizing semisupervised learning. IEEE Trans Biomed Eng 60:1126-33|
|Scalzo, Fabien; Liebeskind, David; Hu, Xiao (2013) Reducing false intracranial pressure alarms using morphological waveform features. IEEE Trans Biomed Eng 60:235-9|
|Asgari, Shadnaz; Vespa, Paul; Hu, Xiao (2013) Is there any association between cerebral vasoconstriction/vasodilatation and microdialysis Lactate to Pyruvate ratio increase? Neurocrit Care 19:56-64|