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.

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.

Heart Rate Variability as a Biomarker of Neurocardiogenic Injury After Subarachnoid Hemorrhage.

BACKGROUND: The objective of this study was to examine whether heart rate variability (HRV) measures can be used to detect neurocardiogenic injury (NCI). METHODS: Three hundred and twenty-six consecutive admissions with aneurysmal subarachnoid hemorrhage (SAH) met criteria for the study. Of 326 subjects, 56 (17.2%) developed NCI which we defined by wall motion abnormality with ventricular dysfunction on transthoracic echocardiogram or cardiac troponin-I > 0.3 ng/mL without electrocardiogram evidence of coronary artery insufficiency. HRV measures (in time and frequency domains, as well as nonlinear technique of detrended fluctuation analysis) were calculated over the first 48 h. We applied longitudinal multilevel linear regression to characterize the relationship of HRV measures with NCI and examine between-group differences at baseline and over time. RESULTS: There was decreased vagal activity in NCI subjects with a between-group difference in low/high frequency ratio (ß 3.42, SE 0.92, p = 0.0002), with sympathovagal balance in favor of sympathetic nervous activity. All time-domain measures were decreased in SAH subjects with NCI. An ensemble machine learning approach translated these measures into a classification tool that demonstrated good discrimination using the area under the receiver operating characteristic curve (AUROC 0.82), the area under precision recall curve (AUPRC 0.75), and a correct classification rate of 0.81. CONCLUSIONS: HRV measures are significantly associated with our label of NCI and a machine learning approach using features derived from HRV measures can classify SAH patients that develop NCI.

Incorporating High-Frequency Physiologic Data Using Computational Dictionary Learning Improves Prediction of Delayed Cerebral Ischemia Compared to Existing Methods.

PURPOSE: Accurate prediction of delayed cerebral ischemia (DCI) after subarachnoid hemorrhage (SAH) can be critical for planning interventions to prevent poor neurological outcome. This paper presents a model using convolution dictionary learning to extract features from physiological data available from bedside monitors. We develop and validate a prediction model for DCI after SAH, demonstrating improved precision over standard methods alone. METHODS: 488 consecutive SAH admissions from 2006 to 2014 to a tertiary care hospital were included. Models were trained on 80%, while 20% were set aside for validation testing. Modified Fisher Scale was considered the standard grading scale in clinical use; baseline features also analyzed included age, sex, Hunt-Hess, and Glasgow Coma Scales. An unsupervised approach using convolution dictionary learning was used to extract features from physiological time series (systolic blood pressure and diastolic blood pressure, heart rate, respiratory rate, and oxygen saturation). Classifiers (partial least squares and linear and kernel support vector machines) were trained on feature subsets of the derivation dataset. Models were applied to the validation dataset. RESULTS: The performances of the best classifiers on the validation dataset are reported by feature subset. Standard grading scale (mFS): AUC 0.54. Combined demographics and grading scales (baseline features): AUC 0.63. Kernel derived physiologic features: AUC 0.66. Combined baseline and physiologic features with redundant feature reduction: AUC 0.71 on derivation dataset and 0.78 on validation dataset. CONCLUSION: Current DCI prediction tools rely on admission imaging and are advantageously simple to employ. However, using an agnostic and computationally inexpensive learning approach for high-frequency physiologic time series data, we demonstrated that we could incorporate individual physiologic data to achieve higher classification accuracy.

Metabolic crisis occurs with seizures and periodic discharges after brain trauma.

OBJECTIVE: Traumatic brain injury (TBI) results in persistent disruption of brain metabolism that has yet to be mechanistically defined. Early post-traumatic seizures are one potential mechanism for metabolic crisis and hence could be a therapeutic target. We hypothesized that seizures and pseudoperiodic discharges (PDs) may be mechanistically linked to metabolic crisis as measured by cerebral microdialysis. METHODS: A prospective multicenter study of surface and intracortical depth electroencephalography (EEG) was performed in conjunction with cerebral microdialysis in a cohort of severe TBI patients with time-locked analysis of the neurochemical response to seizures and pseudoperiodic discharges. RESULTS: Seizures or PDs occurred in 61% of 34 subjects, with 42.9% of these seizures noted only on intracortical depth EEG and in some cases lasting for many hours. Metabolic crisis as measured by elevated cerebral microdialysis lactate/pyruvate ratio occurred during seizures or PDs but not during electrically nonepileptic epochs. INTERPRETATION: In TBI patients, seizures and periodic discharges are one mechanism for metabolic crisis, and hence represent a therapeutic target for future study.