A methodology of phenotyping ICU patients from EHR data: High-fidelity, personalized, and interpretable phenotypes estimation.

OBJECTIVE: Computing phenotypes that provide high-fidelity, time-dependent characterizations and yield personalized interpretations is challenging, especially given the complexity of physiological and healthcare systems and clinical data quality. This paper develops a methodological pipeline to estimate unmeasured physiological parameters and produce high-fidelity, personalized phenotypes anchored to physiological mechanics from electronic health record (EHR). METHODS: A methodological phenotyping pipeline is developed that computes new phenotypes defined with unmeasurable computational biomarkers quantifying specific physiological properties in real time. Working within the inverse problem framework, this pipeline is applied to the glucose-insulin system for ICU patients using data assimilation to estimate an established mathematical physiological model with stochastic optimization. This produces physiological model parameter vectors of clinically unmeasured endocrine properties, here insulin secretion, clearance, and resistance, estimated for individual patient. These physiological parameter vectors are used as inputs to unsupervised machine learning methods to produce phenotypic labels and discrete physiological phenotypes. These phenotypes are inherently interpretable because they are based on parametric physiological descriptors. To establish potential clinical utility, the computed phenotypes are evaluated with external EHR data for consistency and reliability and with clinician face validation. RESULTS: The phenotype computation was performed on a cohort of 109 ICU patients who received no or short-acting insulin therapy, rendering continuous and discrete physiological phenotypes as specific computational biomarkers of unmeasured insulin secretion, clearance, and resistance on time windows of three days. Six, six, and five discrete phenotypes were found in the first, middle, and last three-day periods of ICU stays, respectively. Computed phenotypic labels were predictive with an average accuracy of 89%. External validation of discrete phenotypes showed coherence and consistency in clinically observable differences based on laboratory measurements and ICD 9/10 codes and clinical concordance from face validity. A particularly clinically impactful parameter, insulin secretion, had a concordance accuracy of 83%±27%. CONCLUSION: The new physiological phenotypes computed with individual patient ICU data and defined by estimates of mechanistic model parameters have high physiological fidelity, are continuous, time-specific, personalized, interpretable, and predictive. This methodology is generalizable to other clinical and physiological settings and opens the door for discovering deeper physiological information to personalize medical care.

A methodology of phenotyping ICU patients from EHR data: high-fidelity, personalized, and interpretable phenotypes estimation.

Objective: Computing phenotypes that provide high-fidelity, time-dependent characterizations and yield personalized interpretations is challenging, especially given the complexity of physiological and healthcare systems and clinical data quality. This paper develops a methodological pipeline to estimate unmeasured physiological parameters and produce high-fidelity, personalized phenotypes anchored to physiological mechanics from electronic health record (EHR). Methods: A methodological phenotyping pipeline is developed that computes new phenotypes defined with unmeasurable computational biomarkers quantifying specific physiological properties in real time. Working within the inverse problem framework, this pipeline is applied to the glucose-insulin system for ICU patients using data assimilation to estimate an established mathematical physiological model with stochastic optimization. This produces physiological model parameter vectors of clinically unmeasured endocrine properties, here insulin secretion, clearance, and resistance, estimated for individual patient. These physiological parameter vectors are used as inputs to unsupervised machine learning methods to produce phenotypic labels and discrete physiological phenotypes. These phenotypes are inherently interpretable because they are based on parametric physiological descriptors. To establish potential clinical utility, the computed phenotypes are evaluated with external EHR data for consistency and reliability and with clinician face validation. Results: The phenotype computation was performed on a cohort of 109 ICU patients who received no or short-acting insulin therapy, rendering continuous and discrete physiological phenotypes as specific computational biomarkers of unmeasured insulin secretion, clearance, and resistance on time windows of three days. Six, six, and five discrete phenotypes were found in the first, middle, and last three-day periods of ICU stays, respectively. Computed phenotypic labels were predictive with an average accuracy of 89%. External validation of discrete phenotypes showed coherence and consistency in clinically observable differences based on laboratory measurements and ICD 9/10 codes and clinical concordance from face validity. A particularly clinically impactful parameter, insulin secretion, had a concordance accuracy of 83% ± 27%. Conclusion: The new physiological phenotypes computed with individual patient ICU data and defined by estimates of mechanistic model parameters have high physiological fidelity, are continuous, time-specific, personalized, interpretable, and predictive. This methodology is generalizable to other clinical and physiological settings and opens the door for discovering deeper physiological information to personalize medical care.

Common Data Elements for Disorders of Consciousness: Recommendations from the Electrophysiology Working Group.

BACKGROUND: Electroencephalography (EEG) has long been recognized as an important tool in the investigation of disorders of consciousness (DoC). From inspection of the raw EEG to the implementation of quantitative EEG, and more recently in the use of perturbed EEG, it is paramount to providing accurate diagnostic and prognostic information in the care of patients with DoC. However, a nomenclature for variables that establishes a convention for naming, defining, and structuring data for clinical research variables currently is lacking. As such, the Neurocritical Care Society's Curing Coma Campaign convened nine working groups composed of experts in the field to construct common data elements (CDEs) to provide recommendations for DoC, with the main goal of facilitating data collection and standardization of reporting. This article summarizes the recommendations of the electrophysiology DoC working group. METHODS: After assessing previously published pertinent CDEs, we developed new CDEs and categorized them into "disease core," "basic," "supplemental," and "exploratory." Key EEG design elements, defined as concepts that pertained to a methodological parameter relevant to the acquisition, processing, or analysis of data, were also included but were not classified as CDEs. RESULTS: After identifying existing pertinent CDEs and developing novel CDEs for electrophysiology in DoC, variables were organized into a framework based on the two primary categories of resting state EEG and perturbed EEG. Using this categorical framework, two case report forms were generated by the working group. CONCLUSIONS: Adherence to the recommendations outlined by the electrophysiology working group in the resting state EEG and perturbed EEG case report forms will facilitate data collection and sharing in DoC research on an international level. In turn, this will allow for more informed and reliable comparison of results across studies, facilitating further advancement in the realm of DoC research.

Concordance Between Active Partial Thromboplastin Time and Anti-Factor Xa Assays in Neurocritically Ill Patients Receiving Subcutaneous Heparin Prophylaxis.

Background: Laboratory monitoring is not recommended when subcutaneous unfractionated heparin (SQ-UFH) is administered at prophylactic doses. However, aPTT prolongation and associated hemorrhage has been reported in the neurocritically ill. At our institution, Neuroscience Intensive Care Unit (Neuro-ICU) patients with prolonged aPTT are further evaluated with a follow up aPTT and anti-factor Xa. Purpose: The purpose of this study was to describe concordance between aPTT and anti-factor Xa in neurocritically ill patients receiving prophylactic SQ-UFH with evidence of aPTT prolongation. Methods: A retrospective chart review of adult patients admitted to the Neuro-ICU from June 2017 to June 2019 was performed. Patients were included if they received SQ-UFH with aPTT levels and at least one anti-factor Xa level drawn within one hour of each other. Concordance between paired aPTT and anti-factor Xa was evaluated using Cohen's weighted kappa. Results: Forty two patients with 56 paired aPTT and anti-factor Xa levels were included. The most prescribed SQ-UFH regimen was 5000 units every 8 hours (60.7%) and anti-factor Xa levels were drawn a median (IQR) of 5.7 (3.1-10.7) hours after the SQ-UFH dose. Only 16 (28.6%) pairs were in concordance. The analysis showed a weighted kappa of .09; 95% CI [-.05 to .22] indicating poor agreement. Conclusions: In neurocritically ill patients receiving prophylactic SQ-UFH with aPTT prolongation, there was poor concordance between aPTT and anti-factor Xa. This suggests that aPTT prolongation may not be solely driven by heparin activity and further evaluation of mechanistic drivers for coagulopathy in this population is necessary.

Long-Term Outcomes of Randomized Controlled Trials Comparing Percutaneous Left Atrial Appendage Closure to Oral Anticoagulation for Nonvalvular Atrial Fibrillation: A Meta-Analysis.

Background: Oral anticoagulation (OAC) has been considered the standard of care for stroke prophylaxis for patients with nonvalvular atrial fibrillation; however, many individuals are unable or unwilling to take long-term OAC. The safety and efficacy of percutaneous left atrial appendage closure (LAAC) have been controversial, and new trial data have recently emerged. We therefore sought to perform an updated meta-analysis of randomized clinical trials (RCTs) comparing OAC to percutaneous LAAC, focusing on individual clinical endpoints. Methods: We performed a systematic search of the MEDLINE, EMBASE, and Cochrane Central Register of Controlled Trials from January 2000 through December 2021 for all RCTs comparing percutaneous LAAC to OAC in patients with nonvalvular atrial fibrillation. Fixed and random effects meta-analyses of hazard ratios (HRs) were performed using the longest follow-up duration available by intention-to-treat. The prespecified primary endpoint was all-cause mortality. Results: Three RCTs enrolling 1516 patients were identified. The weighted mean follow-up was 54.7 months. LAAC was associated with a reduced risk of all-cause mortality (HR 0.76; 95% confidence interval [CI], 0.59-0.96; p = 0.023), hemorrhagic stroke (HR 0.24; 95% CI, 0.09-0.61; p = 0.003), and major nonprocedural bleeding (HR 0.52; 95% CI, 0.37-0.74; p < 0.001). There was no significant difference between LAAC and OAC for any other endpoints. Conclusions: The available evidence from RCTs suggests LAAC therapy is associated with reduced long-term risk of death compared with OAC. This may be driven by reductions in hemorrhagic stroke and major nonprocedural bleeding. There were no significant differences in the risk of all stroke. Further large-scale clinical trials are needed to validate these findings.

Cognitive-motor dissociation and time to functional recovery in patients with acute brain injury in the USA: a prospective observational cohort study.

BACKGROUND: Recovery trajectories of clinically unresponsive patients with acute brain injury are largely uncertain. Brain activation in the absence of a behavioural response to spoken motor commands can be detected by EEG, also known as cognitive-motor dissociation. We aimed to explore the role of cognitive-motor dissociation in predicting time to recovery in patients with acute brain injury. METHODS: In this observational cohort study, we prospectively studied two independent cohorts of clinically unresponsive patients (aged ≥18 years) with acute brain injury. Machine learning was applied to EEG recordings to diagnose cognitive-motor dissociation by detecting brain activation in response to verbal commands. Survival statistics and shift analyses were applied to the data to identify an association between cognitive-motor dissociation and time to and magnitude of recovery. The prediction accuracy of the model that was built using the derivation cohort was assessed using the validation cohort. Functional outcomes of all patients were assessed with the Glasgow Outcome Scale-Extended (GOS-E) at hospital discharge and at 3, 6, and 12 months after injury. Patients who underwent withdrawal of life-sustaining therapies were censored, and death was treated as a competing risk. FINDINGS: Between July 1, 2014, and Sept 30, 2021, we screened 598 patients with acute brain injury and included 193 (32%) patients, of whom 100 were in the derivation cohort and 93 were in the validation cohort. At 12 months, 28 (15%) of 193 unresponsive patients had a GOS-E score of 4 or above. Cognitive-motor dissociation was seen in 27 (14%) patients and was an independent predictor of shorter time to good recovery (hazard ratio 5·6 [95% CI 2·5-12·5]), as was underlying traumatic brain injury or subdural haematoma (4·4 [1·4-14·0]), a Glasgow Coma Scale score on admission of greater than or equal to 8 (2·2 [1·0-4·7]), and younger age (1·0 [1·0-1·1]). Among patients discharged home or to a rehabilitation setting, those diagnosed with cognitive-motor dissociation consistently had higher scores on GOS-E indicating better functional recovery compared with those without cognitive-motor dissociation, which was seen as early as 3 months after the injury (odds ratio 4·5 [95% CI 2·0-33·6]). INTERPRETATION: Recovery trajectories of clinically unresponsive patients diagnosed with cognitive-motor dissociation early after brain injury are distinctly different from those without cognitive-motor dissociation. A diagnosis of cognitive-motor dissociation could inform the counselling of families of clinically unresponsive patients, and it could help clinicians to identify patients who will benefit from rehabilitation. FUNDING: US National Institutes of Health.

Prolonged Unconsciousness is Common in COVID-19 and Associated with Hypoxemia.

OBJECTIVE: The purpose of this study was to estimate the time to recovery of command-following and associations between hypoxemia with time to recovery of command-following. METHODS: In this multicenter, retrospective, cohort study during the initial surge of the United States' pandemic (March-July 2020) we estimate the time from intubation to recovery of command-following, using Kaplan Meier cumulative-incidence curves and Cox proportional hazard models. Patients were included if they were admitted to 1 of 3 hospitals because of severe coronavirus disease 2019 (COVID-19), required endotracheal intubation for at least 7 days, and experienced impairment of consciousness (Glasgow Coma Scale motor score <6). RESULTS: Five hundred seventy-one patients of the 795 patients recovered command-following. The median time to recovery of command-following was 30 days (95% confidence interval [CI] = 27-32 days). Median time to recovery of command-following increased by 16 days for patients with at least one episode of an arterial partial pressure of oxygen (PaO2 ) value ≤55 mmHg (p < 0.001), and 25% recovered ≥10 days after cessation of mechanical ventilation. The time to recovery of command-following  was associated with hypoxemia (PaO2 ≤55 mmHg hazard ratio [HR] = 0.56, 95% CI = 0.46-0.68; PaO2 ≤70 HR = 0.88, 95% CI = 0.85-0.91), and each additional day of hypoxemia decreased the likelihood of recovery, accounting for confounders including sedation. These findings were confirmed among patients without any imagining evidence of structural brain injury (n = 199), and in a non-overlapping second surge cohort (N = 427, October 2020 to April 2021). INTERPRETATION: Survivors of severe COVID-19 commonly recover consciousness weeks after cessation of mechanical ventilation. Long recovery periods are associated with more severe hypoxemia. This relationship is not explained by sedation or brain injury identified on clinical imaging and should inform decisions about life-sustaining therapies. ANN NEUROL 2022;91:740-755.

Disorders of Consciousness in Hospitalized Patients with COVID-19: The Role of the Systemic Inflammatory Response Syndrome.

BACKGROUND: Prevalence and etiology of unconsciousness are uncertain in hospitalized patients with coronavirus disease 2019 (COVID-19). We tested the hypothesis that increased inflammation in COVID-19 precedes coma, independent of medications, hypotension, and hypoxia. METHODS: We retrospectively assessed 3203 hospitalized patients with COVID-19 from March 2 through July 30, 2020, in New York City with the Glasgow Coma Scale and systemic inflammatory response syndrome (SIRS) scores. We applied hazard ratio (HR) modeling and mediation analysis to determine the risk of SIRS score elevation to precede coma, accounting for confounders. RESULTS: We obtained behavioral assessments in 3203 of 10,797 patients admitted to the hospital who tested positive for SARS-CoV-2. Of those patients, 1054 (32.9%) were comatose, which first developed on median hospital day 2 (interquartile range [IQR] 1-9). During their hospital stay, 1538 (48%) had a SIRS score of 2 or above at least once, and the median maximum SIRS score was 2 (IQR 1-2). A fivefold increased risk of coma (HR 5.05, 95% confidence interval 4.27-5.98) was seen for each day that patients with COVID-19 had elevated SIRS scores, independent of medication effects, hypotension, and hypoxia. The overall mortality in this population was 13.8% (n = 441). Coma was associated with death (odds ratio 7.77, 95% confidence interval 6.29-9.65) and increased length of stay (13 days [IQR 11.9-14.1] vs. 11 [IQR 9.6-12.4]), accounting for demographics. CONCLUSIONS: Disorders of consciousness are common in hospitalized patients with severe COVID-19 and are associated with increased mortality and length of hospitalization. The underlying etiology of disorders of consciousness in this population is uncertain but, in addition to medication effects, may in part be linked to systemic inflammation.

Perceptions Regarding the SARS-CoV-2 Pandemic's Impact on Neurocritical Care Delivery: Results From a Global Survey.

BACKGROUND: The SARS-CoV-2 (COVID-19) pandemic has impacted many facets of critical care delivery. METHODS: An electronic survey was distributed to explore the pandemic's perceived impact on neurocritical care delivery between June 2020 and March 2021. Variables were stratified by World Bank country income level, presence of a dedicated neurocritical care unit (NCCU) and experiencing a COVID-19 patient surge. RESULTS: Respondents from 253 hospitals (78.3% response rate) from 47 countries (45.5% low/middle income countries; 54.5% with a dedicated NCCU; 78.6% experienced a first surge) participated in the study. Independent of country income level, NCCU and surge status, participants reported reductions in NCCU admissions (67%), critical care drug shortages (69%), reduction in ancillary services (43%) and routine diagnostic testing (61%), and temporary cancellation of didactic teaching (44%) and clinical/basic science research (70%). Respondents from low/middle income countries were more likely to report lack of surge preparedness (odds ratio [OR], 3.2; 95% confidence interval [CI], 1.8-5.8) and struggling to return to prepandemic standards of care (OR, 12.2; 95% CI, 4.4-34) compared with respondents from high-income countries. Respondents experiencing a surge were more likely to report conversion of NCCUs and general-mixed intensive care units (ICUs) to a COVID-ICU (OR 3.7; 95% CI, 1.9-7.3), conversion of non-ICU beds to ICU beds (OR, 3.4; 95% CI, 1.8-6.5), and deviations in critical care and pharmaceutical practices (OR, 4.2; 95% CI 2.1-8.2). Respondents from hospitals with a dedicated NCCU were less likely to report conversion to a COVID-ICU (OR, 0.5; 95% CI, 0.3-0.9) or conversion of non-ICU to ICU beds (OR, 0.5; 95% CI, 0.3-0.9). CONCLUSION: This study reports the perceived impact of the COVID-19 pandemic on global neurocritical care delivery, and highlights shortcomings of health care infrastructures and the importance of pandemic preparedness.

Electrocerebral Signature of Cardiac Death.

BACKGROUND: Electroencephalography (EEG) findings following cardiovascular collapse in death are uncertain. We aimed to characterize EEG changes immediately preceding and following cardiac death. METHODS: We retrospectively analyzed EEGs of patients who died from cardiac arrest while undergoing standard EEG monitoring in an intensive care unit. Patients with brain death preceding cardiac death were excluded. Three events during fatal cardiovascular failure were investigated: (1) last recorded QRS complex on electrocardiogram (QRS0), (2) cessation of cerebral blood flow (CBF0) estimated as the time that blood pressure and heart rate dropped below set thresholds, and (3) electrocerebral silence on EEG (EEG0). We evaluated EEG spectral power, coherence, and permutation entropy at these time points. RESULTS: Among 19 patients who died while undergoing EEG monitoring, seven (37%) had a comfort-measures-only status and 18 (95%) had a do-not-resuscitate status in place at the time of death. EEG0 occurred at the time of QRS0 in five patients and after QRS0 in two patients (cohort median - 2.0, interquartile range - 8.0 to 0.0), whereas EEG0 was seen at the time of CBF0 in six patients and following CBF0 in 11 patients (cohort median 2.0 min, interquartile range - 1.5 to 6.0). After CBF0, full-spectrum log power (p < 0.001) and coherence (p < 0.001) decreased on EEG, whereas delta (p = 0.007) and theta (p < 0.001) permutation entropy increased. CONCLUSIONS: Rarely may patients have transient electrocerebral activity following the last recorded QRS (less than 5 min) and estimated cessation of cerebral blood flow. These results may have implications for discussions around cardiopulmonary resuscitation and organ donation.

Predicting early recovery of consciousness after cardiac arrest supported by quantitative electroencephalography.

OBJECTIVE: To determine the ability of quantitative electroencephalography (QEEG) to improve the accuracy of predicting recovery of consciousness by post-cardiac arrest day 10. METHODS: Unconscious survivors of cardiac arrest undergoing daily clinical and EEG assessments through post-cardiac arrest day 10 were studied in a prospective observational cohort study. Power spectral density, local coherence, and permutation entropy were calculated from daily EEG clips following a painful stimulus. Recovery of consciousness was defined as following at least simple commands by day 10. We determined the impact of EEG metrics to predict recovery when analyzed with established predictors of recovery using partial least squares regression models. Explained variance analysis identified which features contributed most to the predictive model. RESULTS: 367 EEG epochs from 98 subjects were analyzed in conjunction with clinical measures. Highest prediction accuracy was achieved when adding QEEG features from post-arrest days 4-6 to established predictors (area under the receiver operating curve improved from 0.81 ± 0.04 to 0.86 ± 0.05). Prediction accuracy decreased from 0.84 ± 0.04 to 0.79 ± 0.04 when adding QEEG features from post-arrest days 1-3. Patients with recovery of command-following by day 10 showed higher coherence across the frequency spectrum and higher centro-occipital delta-frequency spectral power by days 4-6, and globally-higher theta range permutation entropy by days 7-10. CONCLUSIONS: Adding quantitative EEG metrics to established predictors of recovery allows modest improvement of prediction accuracy for recovery of consciousness, when obtained within a week of cardiac arrest. Further research is needed to determine the best strategy for integration of QEEG data into prognostic models in this patient population.

Post-acute COVID-19 syndrome.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the pathogen responsible for the coronavirus disease 2019 (COVID-19) pandemic, which has resulted in global healthcare crises and strained health resources. As the population of patients recovering from COVID-19 grows, it is paramount to establish an understanding of the healthcare issues surrounding them. COVID-19 is now recognized as a multi-organ disease with a broad spectrum of manifestations. Similarly to post-acute viral syndromes described in survivors of other virulent coronavirus epidemics, there are increasing reports of persistent and prolonged effects after acute COVID-19. Patient advocacy groups, many members of which identify themselves as long haulers, have helped contribute to the recognition of post-acute COVID-19, a syndrome characterized by persistent symptoms and/or delayed or long-term complications beyond 4 weeks from the onset of symptoms. Here, we provide a comprehensive review of the current literature on post-acute COVID-19, its pathophysiology and its organ-specific sequelae. Finally, we discuss relevant considerations for the multidisciplinary care of COVID-19 survivors and propose a framework for the identification of those at high risk for post-acute COVID-19 and their coordinated management through dedicated COVID-19 clinics.

Antiplatelet strategies in acute coronary syndromes: design and methodology of an international collaborative network meta-analysis of randomized controlled trials.

INTRODUCTION: The optimal choice of oral P2Y12 receptor inhibitors has the potential to significantly influence outcomes. We seek to compare the safety and efficacy of the three most commonly used oral P2Y12 receptor inhibitors (clopidogrel, prasugrel, and ticagrelor) in acute coronary syndromes (ACS) via a comprehensive systematic review and network meta-analysis. EVIDENCE ACQUISITION: In line with the Preferred Reporting Items for Systematic Reviews (PRISMA) guidelines, we performed a comprehensive search for RCTs which compared cardiovascular and hemorrhagic outcomes after use of at least two of the distinct oral P2Y12 receptor inhibitors (i.e. clopidogrel, prasugrel, and ticagrelor). A search strategy has been designed to systematically search multiple databases, including MEDLINE with PubMed interface, The Cochrane Central Register of Controlled Trials, and Embase. In addition, key inclusion criteria will be trial size of at least 100 patients and at least 1 month of follow-up time. Several prespecified subgroups will be explored, including Asian patients, patients presenting with ST-elevation myocardial infarction, patients of advanced age, and others. EVIDENCE SYNTHESIS: Exploratory frequentist pairwise meta-analyses will be based primarily on a random-effects method, relying on relative risks (RR) for short-term outcomes and incidence rate ratios (IRR) for long-term outcomes. Inferential frequentist network meta-analysis will be based primarily on a random-effects method, relying on RR and IRR as specified above. Results will be reported as point summary of effect, 95% CI, and P values for effect, and graphically represented using forest plots. CONCLUSIONS: An international collaborative network meta-analysis has begun to comprehensively analyze the safety and efficacy of prasugrel, ticagrelor and clopidogrel, each on a background of aspirin, for management of patients with ACS. It is our hope that the rigor and breadth of the undertaking described herein will provide novel insights that will inform optimal patient care for patients with ACS treated conservatively, or undergoing revascularization.

Principles of Pharmacotherapy of Seizures and Status Epilepticus.

Status epilepticus is a neurological emergency with an outcome that is highly associated with the initial pharmacotherapy management that must be administered in a timely fashion. Beyond first-line therapy of status epilepticus, treatment is not guided by robust evidence. Optimal pharmacotherapy selection for individual patients is essential in the management of seizures and status epilepticus with careful evaluation of pharmacokinetic and pharmacodynamic factors. With the addition of newer antiseizure agents to the market, understanding their role in the management of status epilepticus is critical. Etiology-guided therapy should be considered in certain patients with drug-induced seizures, alcohol withdrawal, or autoimmune encephalitis. Some patient populations warrant special consideration, such as pediatric, pregnant, elderly, and the critically ill. Seizure prophylaxis is indicated in select patients with acute neurological injury and should be limited to the acute postinjury period.

Electroencephalogram Monitoring in Critical Care.

Seizures are common in critically ill patients. Electroencephalogram (EEG) is a tool that enables clinicians to provide continuous brain monitoring and to guide treatment decisions-brain telemetry. EEG monitoring has particular utility in the intensive care unit as most seizures in this setting are nonconvulsive. Despite the increased use of EEG monitoring in the critical care unit, it remains underutilized. In this review, we summarize the utility of EEG and different EEG modalities to monitor patients in the critical care setting.

Markers in Status Epilepticus Prognosis.

Status epilepticus (SE) is a neurologic emergency with high morbidity and mortality. The assessment of a patient's prognosis is crucial in making treatment decisions. In this review, we discuss various markers that have been used to prognosticate SE in terms of recurrence, mortality, and functional outcome. These markers include demographic, clinical, electrophysiological, biochemical, and structural data. The heterogeneity of SE etiology and semiology renders development of prognostic markers challenging. Currently, prognostication in SE is limited to a few clinical scores. Future research should integrate clinical, genetic and epigenetic, metabolic, inflammatory, and structural biomarkers into prognostication models to approach "personalized medicine" in prognostication of outcomes after SE.

Ketamine to treat super-refractory status epilepticus.

OBJECTIVE: To test ketamine infusion efficacy in the treatment of super-refractory status epilepticus (SRSE), we studied patients with SRSE who were treated with ketamine retrospectively. We also studied the effect of high doses of ketamine on brain physiology as reflected by invasive multimodality monitoring (MMM). METHODS: We studied a consecutive series of 68 patients with SRSE who were admitted between 2009 and 2018, treated with ketamine, and monitored with scalp EEG. Eleven of these patients underwent MMM at the time of ketamine administration. We compared patients who had seizure cessation after ketamine initiation to those who did not. RESULTS: Mean age was 53 ± 18 years and 46% of patients were female. Seizure burden decreased by at least 50% within 24 hours of starting ketamine in 55 (81%) patients, with complete cessation in 43 (63%). Average dose of ketamine infusion was 2.2 ± 1.8 mg/kg/h, with median duration of 2 (1-4) days. Average dose of midazolam was 1.0 ± 0.8 mg/kg/h at the time of ketamine initiation and was started at a median of 0.4 (0.1-1.0) days before ketamine. Using a generalized linear mixed effect model, ketamine was associated with stable mean arterial pressure (odds ratio 1.39, 95% confidence interval 1.38-1.40) and with decreased vasopressor requirements over time. We found no effect on intracranial pressure, cerebral blood flow, or cerebral perfusion pressure. CONCLUSION: Ketamine treatment was associated with a decrease in seizure burden in patients with SRSE. Our data support the notion that high-dose ketamine infusions are associated with decreased vasopressor requirements without increased intracranial pressure. CLASSIFICATION OF EVIDENCE: This study provides Class IV evidence that ketamine decreases seizures in patients with SRSE.

Rethinking the Drug Distribution and Medication Management Model: How a New York City Hospital Pharmacy Department Responded to COVID-19.

Beginning in March 2020, New York City began the fight against coronavirus disease 2019. Health care workers were faced with a disease that led to significant morbidity and mortality with no proven therapies. As hospitals became inundated with patients and underwent rapid expansion of capacity, resources such as drugs, protective and medical equipment, and hospital staff became limited. Pharmacists played a critical role in the management of clinical care and drug delivery during the pandemic. As members of the department of pharmacy within NewYork-Presbyterian Hospital, we describe our experiences and processes to overcome challenges faced during the pandemic. Strict inventory management through the use of daily usage reports, frequent communication, and minimization of waste was critical for the management of drug shortages. The creation of guidelines, protocols, and restrictions were not only used to mitigate drug shortages, but also helped educate health care providers and guided medication use. Managing technology through setting up new automatic dispensing cabinets to address hospital expansions and modifying the electronic order entry system to include new protocols and drug shortage information were also vital. Additional key pharmacist functions included provision of investigational drug service support and training of pharmacists, prescribers, nurses, and respiratory therapists to educate and standardize medication use. Through implementation of operational and clinical processes, pharmacists managed critical drug inventory and guided patient treatment. As the pandemic continues, pharmacists will remain vital members of the multidisciplinary team dedicated to the fight against the virus.

Pharmacological Agents Targeting Thromboinflammation in COVID-19: Review and Implications for Future Research.

Coronavirus disease 2019 (COVID-19), currently a worldwide pandemic, is a viral illness caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The suspected contribution of thrombotic events to morbidity and mortality in COVID-19 patients has prompted a search for novel potential options for preventing COVID-19-associated thrombotic disease. In this article by the Global COVID-19 Thrombosis Collaborative Group, we describe novel dosing approaches for commonly used antithrombotic agents (especially heparin-based regimens) and the potential use of less widely used antithrombotic drugs in the absence of confirmed thrombosis. Although these therapies may have direct antithrombotic effects, other mechanisms of action, including anti-inflammatory or antiviral effects, have been postulated. Based on survey results from this group of authors, we suggest research priorities for specific agents and subgroups of patients with COVID-19. Further, we review other agents, including immunomodulators, that may have antithrombotic properties. It is our hope that the present document will encourage and stimulate future prospective studies and randomized trials to study the safety, efficacy, and optimal use of these agents for prevention or management of thrombosis in COVID-19.