Big Data and Deep Learning for the Interictal-Ictal-Injury Continuum Brain monitoring in critical care has grown dramatically over the past 20 years with the discovery that a large proportion of ICU patients suffer from subclinical seizures and seizure-like electrical events, collectively called ?ictal-interictal-injury continuum abnormalities? (IIICAs), detectable only by electroencephalography (EEG). This growth has created a crisis in critical care: It is clear that IIICAs damage the brain and cause permanent neurologic disability. Yet detection of IIICAs by expert visual review is often delayed suggesting we need better tools for real-time monitoring, to cope with the deluge of ICU EEG data. In other cases, IIICAs appear to be harmless epiphenomena, and many worry that increased awareness of IIICAs has created an epidemic of overly-aggressive prescribing of anticonvulsant drugs leading to preventable adverse events and costs. This crisis highlights critical unmet needs for automated EEG monitoring for IIICAs, and a better understanding of which types of IIICAs cause neural injury and warrant intervention. Causes of IIICAs range widely, from primary brain injuries like hemorrhagic stroke and intracranial hemorrhage, to systemic medical illnesses like sepsis and uremia. Until recently, this massive clinical heterogeneity has been an insurmountable barrier to understanding the impact of IIICAs on neurologic outcome. However, recent advances in deep learning, coupled with the unprecedented availability of a massive dataset developed by our team over the last three years, makes it feasible for the first time to systematically study the relationship between IIICAs and neurologic outcomes. To meet the need for better monitoring tools and better models for understanding IIICAs, we will take a deep learning approach to leverage the as-yet untapped information in a massive ICU EEG dataset. We will pursue three Specific Aims: SA1: Comprehensively label all occurrences of IIICAs in a massive set of cEEG recordings, thus preparing the EEG data for training computers to detect IIICA patterns; SA2: Develop supervised DL algorithms to detect IIICAs as accurately as human experts, thus providing powerful tools for both research on IIICAs and for clinical brain monitoring; SA3: Estimate the effect of IIICAs on neurologic outcome: we will develop models to quantify effects of IIICAs on risk for disability after controlling for inciting illness and other clinical factors, and to predict effects of interventions to suppress IIICAs. This work will provide four crucial benefits to advance the field of precision critical care neurology, and by extension, our ability to provide optimal neurologic care for patients during critical illness. 1) Improved understanding of the clinical significance of seizure like IIICA states; 2) development of robust tools and algorithms for critical care brain telemetry; 3) a unique, massive, publicly available, thoroughly annotated dataset that will enable other researchers to further advance the field; and 4) a testable model that predicts which types of cEEG abnormalities warrant aggressive treatment, setting the stage for interventional trials.
Big Data and Deep Learning for the Interictal-Ictal-Injury Continuum RELEVANCE: Seizures and seizure-like brain activity, collectively called ?ictal-interictal-injury continuum abnormalities? (IIICAs), occur commonly in electroencephalogram recordings of brain activity in ICU patients, and simultaneously represent a preventable cause of brain injury and a common cause of over-treatment and iatrogenic harm to patients. Big Data and deep learning approaches have recently enabled advances in several fields of medicine, but have so far had little impact in ICU neuromedicine. This project will use Big Data and Deep Learning to advance the goal of protecting brain health in ICU patients, by 1) providing improved understanding of the clinical significance of IIICA states; 2) developing robust tools and algorithms for ICU brain telemetry; 3) creating a unique, massive, publicly available, annotated dataset to enable other researchers to further advance the field; and 4) developing a testable model that predicts which types of cEEG abnormalities warrant aggressive treatment, setting the stage for interventional trials.
|Amorim, Edilberto; Ghassemi, Mohammad M; Lee, Jong W et al. (2018) Estimating the False Positive Rate of Absent Somatosensory Evoked Potentials in Cardiac Arrest Prognostication. Crit Care Med 46:e1213-e1221|