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ObjectiveIntracranial pressure (ICP) is a vital waveform used to assess the conditions of patients with intracranial pathologies, such as traumatic brain injury. The current standard for monitoring ICP requires a catheter to be inserted into the brain, which is extremely invasive and increases the risk of hemorrhage and infection. We hypothesize that ICP can be inferred from extracranial waveforms routinely measured in the Intensive Care Unit (ICU), such as invasive arterial blood pressure (ABP), photoplethysmography (PPG), and electrocardiography (ECG).MethodsWe extracted 600 hours of simultaneous ABP, EKG, PPG, and ICP waveforms (125 Hz) across 10 different patients from the MIMIC III Waveform Database. These concurrent recordings were broken into 10 second windows to train on long-short term memory (LSTM) and temporal convolutional network (TCN) models to predict ICP using ABP, EKG, and PPG as input features. These models were evaluated in both a single-patient analysis and multi-patient analysis study.ResultsIn the single-patient analysis study, TCN’s median mean average error (MAE) test loss was 1.60 mmHg in comparison to a median MAE test loss of 1.88 mmHg for LSTM. TCN was used in the multi-patient analysis study, reporting a test MAE loss of 5.44 mmHg.ConclusionsThese novel results indicate that it is possible to predict ICP waveforms from extracranial waveforms in a real-time, continuous, and non-invasive manner. This approach could potentially eliminate the risks associated with invasive monitoring and allow for more timely treatment of patients with intracranial pathologies with further follow-up studies.

This proceedings article presents actionable research targets on the basis of the presentations and discussions at the 2nd Curing Coma National Institutes of Health (NIH) symposium held from May 3 to May 5, 2021. Here, we summarize the background, research priorities, panel discussions, and deliverables discussed during the symposium across six major domains related to disorders of consciousness. The six domains include (1) Biology of Coma, (2) Coma Database, (3) Neuroprognostication, (4) Care of Comatose Patients, (5) Early Clinical Trials, and (6) Long-term Recovery. Following the 1st Curing Coma NIH virtual symposium held on September 9 to September 10, 2020, six workgroups, each consisting of field experts in respective domains, were formed and tasked with identifying gaps and developing key priorities and deliverables to advance the mission of the Curing Coma Campaign. The highly interactive and inspiring presentations and panel discussions during the 3-day virtual NIH symposium identified several action items for the Curing Coma Campaign mission, which we summarize in this article.

BackgroundGiven the clinical heterogeneity of COVID-19 infection, we hypothesize the existence of subphenotypes based on early inflammatory responses that are associated with mortality and additional complications.MethodsFor this cross-sectional study, we extracted electronic health data from adults hospitalized patients between March 1, 2020 and May 5, 2021, with confirmed primary diagnosis of COVID-19 across five Johns Hopkins Hospitals. We obtained all electronic health records from the first 24h of the patient’s hospitalization. Mortality was the primary endpoint explored while myocardial infarction (MI), pulmonary embolism (PE), deep vein thrombosis (DVT), stroke, delirium, length of stay (LOS), ICU admission and intubation status were secondary outcomes of interest. First, we employed clustering analysis to identify COVID-19 subphenotypes on admission with only biomarker data and assigned each patient to a subphenotype. We then performed Chi-Squared and Mann-Whitney-U tests to examine associations between COVID-19 subphenotype assignment and outcomes. In addition, correlations between subphenotype and pre-existing comorbidities were measured using Chi-Squared analysis.ResultsA total of 7076 patients were included. Analysis revealed three distinct subgroups by level of inflammation: hypoinflammatory, intermediate, and hyperinflammatory subphenotypes. More than 25% of patients in the hyperinflammatory subphenotype died compared to less than 3% hypoinflammatory subphenotype (pConclusionWe identify three distinct inflammatory subphenotypes that predict a range of outcomes, including mortality, MI, DVT, PE, stroke, delirium, ICU admission and LOS. The three subphenotypes are easily identifiable and may aid in clinical decision making.

Background Delirium poses significant risks to patients, but countermeasures can be taken to mitigate negative outcomes. Accurately forecasting delirium in intensive care unit (ICU) patients could guide proactive intervention. Our primary objective was to predict ICU delirium by applying machine learning to clinical and physiologic data routinely collected in electronic health records. Methods Two prediction models were trained and tested using a multicenter database (years of data collection 2014 to 2015), and externally validated on two single-center databases (2001 to 2012 and 2008 to 2019). The primary outcome variable was delirium defined as a positive Confusion Assessment Method for the ICU screen, or an Intensive Care Delirium Screening Checklist of 4 or greater. The first model, named “24-hour model,” used data from the 24 h after ICU admission to predict delirium any time afterward. The second model designated “dynamic model,” predicted the onset of delirium up to 12 h in advance. Model performance was compared with a widely cited reference model. Results For the 24-h model, delirium was identified in 2,536 of 18,305 (13.9%), 768 of 5,299 (14.5%), and 5,955 of 36,194 (11.9%) of patient stays, respectively, in the development sample and two validation samples. For the 12-h lead time dynamic model, delirium was identified in 3,791 of 22,234 (17.0%), 994 of 6,166 (16.1%), and 5,955 of 28,440 (20.9%) patient stays, respectively. Mean area under the receiver operating characteristics curve (AUC) (95% CI) for the first 24-h model was 0.785 (0.769 to 0.801), significantly higher than the modified reference model with AUC of 0.730 (0.704 to 0.757). The dynamic model had a mean AUC of 0.845 (0.831 to 0.859) when predicting delirium 12 h in advance. Calibration was similar in both models (mean Brier Score [95% CI] 0.102 [0.097 to 0.108] and 0.111 [0.106 to 0.116]). Model discrimination and calibration were maintained when tested on the validation datasets. Conclusions Machine learning models trained with routinely collected electronic health record data accurately predict ICU delirium, supporting dynamic time-sensitive forecasting. Editor’s Perspective What We Already Know about This Topic What This Manuscript Tells Us That Is New

Despite the growing contributions of critical care anesthesiologists to clinical practice, research, and administrative leadership of intensive care units (ICUs), relatively little is known about the subspecialty-specific clinical practice environment. An understanding of contemporary clinical practice is essential to recognize the opportunities and challenges facing critical care anesthesia, optimize staffing patterns, assess sustainability and satisfaction, and strategically plan for future activity, scope, and training. This study surveyed intensivists who are members of the Society of Critical Care Anesthesiologists (SOCCA) to evaluate practice patterns of critical care anesthesiologists, including compensation, types of ICUs covered, models of overnight ICU coverage, and relationships between these factors. We hypothesized that variability in compensation and practice patterns would be observed between individuals.Board-certified critical care anesthesiologists practicing in the United States were identified using the SOCCA membership distribution list and invited to take a voluntary online survey between May and June 2021. Multiple-choice questions with both single- and multiple-select options were used for answers with categorical data, and adaptive questioning was used to clarify stem-based responses. Respondents were asked to describe practice patterns at their respective institutions and provide information about their demographics, salaries, effort in ICUs, as well as other activities.A total of 490 participants were invited to take this survey, and 157 (response rate 32%) surveys were completed and analyzed. The majority of respondents were White (73%), male (69%), and younger than 50 years of age (82%). The cardiothoracic/cardiovascular ICU was the most common practice setting, with 69.5% of respondents reporting time working in this unit. Significant variability was observed in ICU practice patterns. Respondents reported spending an equal proportion of their time in clinical practice in the operating rooms and ICUs (median, 40%; interquartile range [IQR], 20%-50%), whereas a smaller proportion-primarily those who completed their training before 2009-reported administrative or research activities. Female respondents reported salaries that were $36,739 less than male respondents; however, this difference was not statistically different, and after adjusting for age and practice type, these differences were less pronounced (-$27,479.79; 95% confidence interval [CI], -$57,232.61 to $2273.03; P = .07).These survey data provide a current snapshot of anesthesiology critical care clinical practice patterns in the United States. Our findings may inform decision-making around the initiation and expansion of critical care services and optimal staffing patterns, as well as provide a basis for further work that focuses on intensivist satisfaction and burnout.

Purpose External ventricular drains (EVD) are commonly used in aneurysmal subarachnoid hemorrhage (aSAH) patients and can be life-saving by diverting cerebrospinal fluid. However, the overall relationship between EVD use and outcome is poorly understood. Methods In an exploratory analysis of an aSAH patient cohort, we examined EVD use in relation to modified Rankin Scale (mRS) at hospital discharge and at 6 months (unfavorable outcome = mRS > 2) using univariable and multivariable analyses. Results EVDs were placed in 31 of 56 (55.4%) patients and more often in women than men (66.7% vs 35.0%, p = 0.022) despite similar rates of hydrocephalus. Women had greater ICU [18 (13.5–25) vs 11.5 (6.5–18.5) days, p = 0.014] and hospital lengths of stay (LOS) [20.5 (16.5–34) vs 13.5 (10.5–27) days, p = 0.015] than men and greater mRS at discharge [4 (3–5) vs 3 (2–3.5), p = 0.011] although mRS at 6 months was similar. Patients with EVDs had longer ICU and hospital LOS and greater mRS at discharge [5 (3–6) vs 2 (2–3), p p = 0.001] than those without an EVD. In multivariable models, EVD use was associated with unfavorable 6-month outcome accounting for age, sex, and admission modified Fisher scale, but not in models adjusting for Hunt and Hess scale and World Federation of Neurological Surgeons scale. Conclusion In an aSAH cohort, the use of EVDs was associated with female sex and longer LOS, and may be linked to functional outcomes at discharge and at 6 months, although these associations warrant further investigation.

With the advent of advanced analytical methods applied to functional neuroimaging and neurophysiological data, cerebral conditions have been defined that challenge the established classification of disorders of consciousness. A subset of brain-damaged patients has been identified who cannot carry out motor commands, but who exhibit patterns of cerebral activation during mental imagery tasks that are indistinguishable from those in healthy controls. This condition, termed “cognitive motor dissociation,” has overturned many assumptions regarding the detection, diagnosis, prognosis, and care of patients with brain injury. Three factors are likely to influence efforts to improve the classification of disorders of consciousness in the near future: the types of data that will become available to characterize brain states, the modeling paradigms utilized for data analysis, and the ability to implement classification schemes in the clinical setting. Here we review past achievements, present states, and future projections for the classification of impaired consciousness and responsiveness.

Patients with traumatic brain injury (TBI) frequently require invasive mechanical ventilation and admission to an intensive care unit. Ventilation of patients with TBI poses unique clinical challenges, and careful attention is required to ensure that the ventilatory strategy (including selection of appropriate tidal volume, plateau pressure, and positive end-expiratory pressure) does not cause significant additional injury to the brain and lungs. Selection of ventilatory targets may be guided by principles of lung protection but with careful attention to relevant intracranial effects. In patients with TBI and concomitant acute respiratory distress syndrome (ARDS), adjunctive strategies include sedation optimization, neuromuscular blockade, recruitment maneuvers, prone positioning, and extracorporeal life support. However, these approaches have been largely extrapolated from studies in patients with ARDS and without brain injury, with limited data in patients with TBI. This narrative review will summarize the existing evidence for mechanical ventilation in patients with TBI. Relevant literature in patients with ARDS will be summarized, and where available, direct data in the TBI population will be reviewed. Next, practical strategies to optimize the delivery of mechanical ventilation and determine readiness for extubation will be reviewed. Finally, future directions for research in this evolving clinical domain will be presented, with considerations for the design of studies to address relevant knowledge gaps.

Statins are a class of drug widely prescribed for the prevention of cardiovascular disease, with pleiotropic cellular effects. Statins inhibit HMG-CoA reductase (HMGCR), which converts the metabolite HMG-CoA into mevalonate. Recent discoveries have shown HMG-CoA is a reactive metabolite that can non-enzymatically modify proteins and impact their activity. Therefore, we predicted that inhibition of HMGCR by statins might increase HMG-CoA levels and protein modifications. Upon statin treatment, we observe a strong increase in HMG-CoA levels and modification of only a single protein. Mass spectrometry identifies this protein as fatty acid synthase (FAS), which is modified on active site residues and, importantly, on non-lysine side-chains. The dynamic modifications occur only on a sub-pool of FAS that is located near HMGCR and alters cellular signaling around the ER and Golgi. These results uncover communication between cholesterol and lipid biosynthesis by the substrate of one pathway inhibiting another in a rapid and reversible manner.

Heterogeneity is recognized as a major barrier in efforts to improve the care and outcomes of patients with traumatic brain injury (TBI). Even within the narrower stratum of moderate and severe TBI, current management approaches do not capture the complexity of this condition characterized by manifold clinical, anatomical, and pathophysiologic features. One approach to heterogeneity may be to resolve undifferentiated TBI populations into endotypes, subclasses that are distinguished by shared biological characteristics. The endotype paradigm has been explored in a range of medical domains, including psychiatry, oncology, immunology, and pulmonology. In intensive care, endotypes are being investigated for syndromes such as sepsis and acute respiratory distress syndrome. This review provides an overview of the endotype paradigm as well as some of its methods and use cases. A conceptual framework is proposed for endotype research in moderate and severe TBI, together with a scientific road map for endotype discovery and validation in this population.

Aneurysmal subarachnoid hemorrhage (aSAH) is associated with high morbidity and mortality despite advances in management. We evaluated the prognostic significance of a qualitative score using brain magnetic resonance imaging (MRI) features obtained early after aSAH.Patients with aSAH were enrolled in a prospective observational cohort and underwent brain MRI during their acute hospitalization. MRIs were rated using a scoring system that considers the anatomical location of signal intensity changes on diffusion-weighted imaging (DWI) and fluid-attenuated inversion recovery (FLAIR) sequences. The relationship between MRI scores and functional outcome defined by modified Rankin scale (mRS) at 6 months was evaluated in uni- and multivariable models.The cohort included 45 aSAH patients (median World Federation of Neurologic Surgeons (IQR) 2 (1-4)) who underwent brain MRI a mean (SD) of 9.0 ± 8.0 days after aSAH. At 6 months after aSAH, 26 patients had achieved a favorable outcome (mRS ≤ 2) while 15 had an unfavorable outcome (mRS 2). Deep gray nuclei (DGN) score (p = 0.016), cortex + DGN score (p = 0.015), FLAIR score (p = 0.016), DWI score (p = 0.0045), and overall score (p = 0.0081) were significantly lower in patients with favorable outcome compared to those with unfavorable outcome. However, MRI scores were not independent predictors of outcome in multivariable models adjusting for admission Hunt and Hess, Glasgow Coma Scale, or World Federation of Neurologic Surgeons scales.In this pilot study, a qualitative scoring system using anatomically defined MRI FLAIR and DWI signal abnormalities identified in the acute phase of aSAH was linked to 6-month functional outcome. However, these scores did not add prognostic value to established indices of neurological severity.