Nonconvulsive seizures after subarachnoid hemorrhage: Multimodal detection and outcomes

Abstract

Objective Seizures have been implicated as a cause of secondary brain injury, but the systemic and cerebral physiologic effects of seizures after acute brain injury are poorly understood. Methods We analyzed intracortical EEG and multimodality physiological recordings in 48 comatose subarachnoid hemorrhage patients to better characterize the physiological response to seizures after acute brain injury. Results Intracortical seizures were seen in 38% of patients and 8% had surface seizures. Intracortical seizures were accompanied by elevated heart rate (P=0.001), blood pressure (P<0.001), and respiratory rate (P<0.001). There were trends for rising cerebral perfusion pressure (P=0.03) and intracranial pressure (P =0.06) seen after seizure onset. Intracortical seizure associated increases in global brain metabolism, partial brain tissue oxygenation, and regional cerebral blood flow (rCBF) did not reach significance, but a trend for a pronounced delayed rCBF rise was seen for surface seizures (P=0.08). Functional outcome was very poor for patients with severe background attenuation without seizures and best for those without severe attenuation or seizures (77% vs. 0% dead or severely disabled, respectively). Outcome was intermediate for those with seizures independent of the background EEG and worse for those with intracortical only seizures when compared to those with intracortical and scalp seizures (50% and 25% death or severe disability, respectively). Interpretation We replicated in humans complex physiologic processes associated with seizures after acute brain injury previously described in laboratory experiments and illustrated differences such as the delayed increase in regional cerebral blood flow. These real-world physiologic observations may permit more successful translation of laboratory research to the bedside.

Unraveling the complexities of invasive multimodality neuromonitoring

“…10,11,69 Early studies evaluating the utility of continuous scalp electroencephalography (EEG) monitoring noted that seizures occur in approximately 25% of patients with TBI and intracerebral hemorrhage and that the majority of seizures are nonconvulsive, with no clinical manifestations. 10,69 Nonconvulsive seizures are associated with ICP increases and cerebral metabolic distress after TBI, 68 independently predict hematoma expansion, 10 and are associated with increased risk of hippocampal and cortical atrophy.…”

“…Intracranial recordings detect seizures with much higher sensitivity than scalp EEG; in patients being monitored with both types of recordings, conventional scalp EEG misses up to 60% of seizures that are subsequently picked up with invasive methods. 11,71 Seizures identified with invasive recordings produce brain hypoxia 72 and metabolic dysregulation 66 (e.g., cerebral glycopenia and elevated lactate/pyruvate ratio), thus providing a rationale for using intracranial EEG monitoring to guide patient care.…”

Continuous EEG Monitoring in Critical Care

“…7 More recently, a study of 48 comatose subarachnoid hemorrhage patients undergoing multimodality monitoring including intracranial EEG recordings, showed a seizure rate of 38% for intracranial and 8% for surface seizures; intracranial seizures were associated with increases in heart rate, mean arterial pressure and respiratory rate reflecting a sympathetic response and with trends for increased intracranial and cerebral perfusion pressure. 8 Seizures, and more specifically seizure burden, has recently been shown to independently contribute to neurological decline in a large prospective study in a pediatric critical care population. 9 These associations suggest the potential impact of seizures in worsening the already fragile clinical state of the critical care patient.…”

Estimating summary statistics for electronic health record laboratory data for use in high-throughput phenotyping algorithms

“…Our motivation for devising a method for automatically summarizing laboratory data to be used in computational tasks such as phenotyping evolved from four directions: (i) our work on health care process and phenotyping where we observed and documented how the health care influences, confounds, and highlights features that are observable from EHR data [4,1,20,2,21,5,22]; (ii) our Bayesian approach to estimating personalized, time dependent hazard functions that predict the onset of chronic kidney disease—the functions used to model and represent the data were chosen to be Weibull rather than the more standard Gaussian distributions because of the properties of EHR data [18]; (iii) our intuition that the processes generating health care data are relatively sparse [23] and may be summarized and modeled by large contributions from a few dominant features rather than a small contributions from all possible features; and (iv) our work translating phenotypic information to clinical settings where it became clear to us that more simple representations of data, e.g., via single, parameterized families, are more understandable and hence more useful for clinicians than black box prediction [24,25]. In essence, we wanted to find a way to minimize garbage in for machine learning methods, to translate laboratory data to a summary that was simple, faithful, interpretable all while minimizing the amount of human effort necessary to clean and summarize the data and therefore minimizing the resources needed to use EHR data in a high throughput setting.…”