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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. 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. 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]). 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. US National Institutes of Health.

Background Healthcare delivery organizations face increasing pressure to manage the use of medications in terms of safety, waste reduction, and cost containment. Objective To describe a computerized provider order entry (CPOE) system intervention to optimize use of a commonly ordered, high‐cost therapeutic: intravenous immune globulin (IVIG). Design Description of IVIG order configuration, medication use patterns, and subsequent order set configuration development in a CPOE system. Measurements IVIG orders were extracted from the CPOE system before and after the implementation of a specialty orderset to determine the indications for use, dosing, and duration of therapy. Orders were compared to a theoretical dosing schedule created from published evidence and data from a prior medication use evaluation. Results During 36 months before the implementation of the IVIG order set, 1965 IVIG orders were reviewed. The prescribed IVIG dose varied considerably from the expected dose (mean = −1.8, range = −4.9‐1.5). In the 27 months after order set implementation, 848 IVIG orders were reviewed. The prescribed IVIG dose was closer to the expected dose (mean = −1.2, range = −3.9‐2.6, P < .0001). Conclusions Order configuration processes are cumbersome and time‐consuming, but can be streamlined to enhance a medication’s usage in the healthcare system. A better understanding of institution‐specific ordering patterns may facilitate more efficient and effective order configuration and optimize drug use.

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. A comprehensive review of the current literature on post-acute COVID-19, also referred to as long COVID, its pathophysiology and its organ-specific sequelae highlights the need for multidisciplinary follow-up and care of COVID-19 survivors.

Background The recent publication of practice guidelines for management of patients with disorders of consciousness (DoC) in the United States and Europe was a major step forward in improving the accuracy and consistency of terminology, diagnostic criteria, and prognostication in this population. There remains a pressing need for a more precise brain injury classification system that combines clinical semiology with neuroimaging, electrophysiologic, and other biomarker data. To address this need, the National Institute of Neurological Disorders and Stroke launched the Common Data Elements (CDEs) initiative to facilitate systematic collection of high-quality research data in studies involving patients with neurological disease. The Neurocritical Care Society’s Curing Coma Campaign expanded this effort in 2018 to develop CDEs for DoC. Herein, we present CDE recommendations for behavioral phenotyping of patients with DoC. Methods The Behavioral Phenotyping Workgroup used a preestablished, five-step process to identify and select candidate CDEs that included review of existing National Institute of Neurological Disorders and Stroke CDEs, nomination and systematic vetting of new CDEs, CDE classification, iterative review, and approval of panel recommendations and development of corresponding case review forms. Results We identified a slate of existing and newly proposed basic, supplemental, and exploratory CDEs that can be used for behavioral phenotyping of adult and pediatric patients with DoC. Conclusions The proposed behavioral phenotyping CDEs will assist with international harmonization of DoC studies and allow for more precise characterization of study cohorts, favorably impacting observational studies and clinical trials aimed at improving outcome in this population.

Background The implementation of multimodality monitoring in the clinical management of patients with disorders of consciousness (DoC) results in physiological measurements that can be collected in a continuous and regular fashion or even at waveform resolution. Such data are considered part of the “Big Data” available in intensive care units and are potentially suitable for health care-focused artificial intelligence research. Despite the richness in content of the physiological measurements, and the clinical implications shown by derived metrics based on those measurements, they have been largely neglected from previous attempts in harmonizing data collection and standardizing reporting of results as part of common data elements (CDEs) efforts. CDEs aim to provide a framework for unifying data in clinical research and help in implementing a systematic approach that can facilitate reliable comparison of results from clinical studies in DoC as well in international research collaborations. Methods To address this need, the Neurocritical Care Society’s Curing Coma Campaign convened a multidisciplinary panel of DoC “Physiology and Big Data” experts to propose CDEs for data collection and reporting in this field. Results: We report the recommendations of this CDE development panel and disseminate CDEs to be used in physiologic and big data studies of patients with DoC. Conclusions These CDEs will support progress in the field of DoC physiologic and big data and facilitate international collaboration.

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.

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 (QRS 0 ), (2) cessation of cerebral blood flow (CBF 0 ) estimated as the time that blood pressure and heart rate dropped below set thresholds, and (3) electrocerebral silence on EEG (EEG 0 ). 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. EEG 0 occurred at the time of QRS 0 in five patients and after QRS 0 in two patients (cohort median − 2.0, interquartile range − 8.0 to 0.0), whereas EEG 0 was seen at the time of CBF 0 in six patients and following CBF 0 in 11 patients (cohort median 2.0 min, interquartile range − 1.5 to 6.0). After CBF 0 , 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.

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.