Critical Care Medicine

Quantitative Electroencephalogram Trends Predict Recovery in Hypoxic-Ischemic Encephalopathy Objectives: Electroencephalogram features predict neurologic recovery following cardiac arrest. Recent work has shown that prognostic implications of some key electroencephalogram features change over time. We explore whether time dependence exists for an expanded selection of quantitative electroencephalogram features and whether accounting for this time dependence enables better prognostic predictions. Design: Retrospective. Setting: ICUs at four academic medical centers in the United States. Patients: Comatose patients with acute hypoxic-ischemic encephalopathy. Interventions: None. Measurements and Main Results: We analyzed 12,397 hours of electroencephalogram from 438 subjects. From the electroencephalogram, we extracted 52 features that quantify signal complexity, category, and connectivity. We modeled associations between dichotomized neurologic outcome (good vs poor) and quantitative electroencephalogram features in 12-hour intervals using sequential logistic regression with Elastic Net regularization. We compared a predictive model using time-varying features to a model using time-invariant features and to models based on two prior published approaches. Models were evaluated for their ability to predict binary outcomes using area under the receiver operator curve, model calibration (how closely the predicted probability of good outcomes matches the observed proportion of good outcomes), and sensitivity at several common specificity thresholds of interest. A model using time-dependent features outperformed (area under the receiver operator curve, 0.83 ± 0.08) one trained with time-invariant features (0.79 ± 0.07; p < 0.05) and a random forest approach (0.74 ± 0.13; p < 0.05). The time-sensitive model was also the best-calibrated. Conclusions: The statistical association between quantitative electroencephalogram features and neurologic outcome changed over time, and accounting for these changes improved prognostication performance. Drs. Ghassemi and Amorim contributed equally as co-first authors of this work. The Critical Care Electroencephalogram Monitoring Research Consortium Board consists of: Chair: Brandon M. Westover, MD, PhD; Vice-Chair: Emily Gilmore, MD; Secretary: Aaron Struck, MD; Member-at-Large: Nicholas Gaspard, MD, PhD; Immediate Past Chair: Jong Woo Lee, MD, PhD; and Past Chair: Nicholas S. Abend, MD, MSCE. Drs. Ghassemi, Amorim, Lee, Cash, Brown, Mark, and Westover contributed to conception and design of the study. Drs. Ghassemi, Amorim, and Westover contributed to analysis of data. Drs. Ghassemi, Amorim, and Westover contributed to preparing the figures. Drs. Ghassemi and Amorim, Mr. Al Hanai, Drs. Lee, Herman, Sivaraju, and Gaspard, Mr. Biswal, Mr. Moura Junior, and Dr. Westover contributed to data acquisition. Drs. Ghassemi and Amorim, Mr. Al Hanai, Drs. Lee, Herman, Sivaraju, and Gaspard, Mr. Biswal, Mr. Moura Junior, and Drs. Cash, Brown, Mark, and Westover contributed to drafting the text. Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal's website (http://journals.lww.com/ccmjournal). Supported, in part, by grants from National Institutes of Health (NIH) 1R01NS102190, 1R01NS102574, and 1R01NS107291 (to Dr. Westover); R01GM104987 (to Dr. Mark); T32HL007901, T90DA22759, and T32EB001680 (to Dr. Ghassemi); National Institute of Neurological Disorders and Stroke 1K23NS090900 (to Dr. Westover); Salerno foundation (M.G.M.); Neurocritical Care Society research training fellowship and American Heart Association postdoctoral fellowship (to Dr. Amorim); and Andrew David Heitman Neuroendovascular Research Fund and the Rappaport Foundation (to Dr. Westover). Preliminary findings of this study were presented at the 14th Annual Neurocritical Care Society Meeting, National Harbor, MD, September 15–18, 2016. Dr. Amorim's institution received funding from the National Institutes of Health (NIH), Neurocritical Care Society, and American Heart Association. Drs. Amorim, Mark, and Westover received support for article research from the NIH. Dr. Lee received funding from SleepMed/DigiTrace, Advance Medical, and United Diagnostics. Drs. Lee's and Mark's institutions received funding from the NIH. Dr. Herman's institution received funding from UCB Pharma, Sage Therapeutics, Neurospace, Epilepsy Therapy Development Project, Acorda Therapeutics, Pfizer, and Philips. Dr. Hirsch's institution received funding from Upsher-Smith and Monteris. He received funding from Adamas; consultation fees for advising from Aquestive, Ceribell, Eisai, and Medtronic; honoraria for speaking from Neuropace; and royalties for authoring chapters for UpToDate-Neurology and from Wiley for coauthoring a book on electroencephalograms in critical care. Dr. Scirica's institution received funding from Merck, Eisai, and Novartis, and he received consulting fees from AbbVie, Allergan, AstraZeneca, Boehringer Ingelheim, Covance, Eisai, Elsevier Practice Update Cardiology, GlaxoSmithKline, Lexicon, Merck, NovoNordisk, Sanofi, and equity in Health [at] Scale. Dr. Brown's institution received funding from Massachusetts General Hospital and Massachusetts Institute of Technology. The remaining authors have disclosed that they do not have any potential conflicts of interest. For information regarding this article, E-mail: mwestover@mgh.harvard.edu; edilbertoamorim@gmail.com. Copyright © by 2019 by the Society of Critical Care Medicine and Wolters Kluwer Health, Inc. All Rights Reserved.

Enablers and Barriers to Implementing ICU Follow-Up Clinics and Peer Support Groups Following Critical Illness: The Thrive Collaboratives Objectives: Data are lacking regarding implementation of novel strategies such as follow-up clinics and peer support groups, to reduce the burden of postintensive care syndrome. We sought to discover enablers that helped hospital-based clinicians establish post-ICU clinics and peer support programs, and identify barriers that challenged them. Design: Qualitative inquiry. The Consolidated Framework for Implementation Research was used to organize and analyze data. Setting: Two learning collaboratives (ICU follow-up clinics and peer support groups), representing 21 sites, across three continents. Subjects: Clinicians from 21 sites. Measurement and Main Results: Ten enablers and nine barriers to implementation of "ICU follow-up clinics" were described. A key enabler to generate support for clinics was providing insight into the human experience of survivorship, to obtain interest from hospital administrators. Significant barriers included patient and family lack of access to clinics and clinic funding. Nine enablers and five barriers to the implementation of "peer support groups" were identified. Key enablers included developing infrastructure to support successful operationalization of this complex intervention, flexibility about when peer support should be offered, belonging to the international learning collaborative. Significant barriers related to limited attendance by patients and families due to challenges in creating awareness, and uncertainty about who might be appropriate to attend and target in advertising. Conclusions: Several enablers and barriers to implementing ICU follow-up clinics and peer support groups should be taken into account and leveraged to improve ICU recovery. Among the most important enablers are motivated clinician leaders who persist to find a path forward despite obstacles. This does not necessarily represent the views of the U.S. government or Department of Veterans Affairs. Drs. Haines, McPeake, Boehm, and Sevin had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. All other authors contributed substantially to the study design, data analysis and interpretation, and the writing of the article. Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal's website (http://journals.lww.com/ccmjournal). Drs. Haines's, McPeake's, Hibbert's, Boehm's, Aparanji's, Bastin's, Drumright's, Holdsworth's, Johnson's, Kloos's, Meyer's, Quasim's, Saft's, Stollings's, and Sevin's institutions received funding from the Society of Critical Care Medicine (SCCM). Dr. Haines, McPeake, Boehm, and Sevin are currently receiving funding from SCCM to undertake this work, although the supporting source had no input into the design, data collection and analysis, although approved the final article for submission for publication. Dr. Boehm's institution received funding from the National Institutes of Health (NIH)/National Heart, Lung, and Blood Institute (NHLBI) (1K12HL137943-01) and Vanderbilt Clinical and Translational Science Award. The funding source reviewed and approved the article for submission. Drs. Boehm and Iwashyna received support for article research from the NIH. Dr. Hope's institution received funding from NHLBI K01-HL140279, and he received funding from American Association of Critical Care Nurses. Dr. Khan's institution received funding from the NIH. Dr. Kross's institution received funding from the NIH and the American Lung Association. Dr. Quasim's institution received funding from the Health Foundation. Dr. Saft received funding from Medtronic. Dr. Stollings received funding from Intermountain Health. Dr. Weinhouse received funding from UptoDate. Dr. Hopkins's institution received funding from Intermountain Research and Medical Foundation. Dr. Iwashyna's institution received funding from NIH K12, and he disclosed government work. The remaining authors have disclosed that they do not have any potential conflicts of interest. For information regarding this article, E-mail: Kimberley.haines@wh.org.au Copyright © by 2019 by the Society of Critical Care Medicine and Wolters Kluwer Health, Inc. All Rights Reserved.

Hand Hygiene Compliance in the ICU: A Systematic Review Objectives: To synthesize the literature describing compliance with World Health Organization hand hygiene guidelines in ICUs, to evaluate the quality of extant research, and to examine differences in compliance levels across geographical regions, ICU types, and healthcare worker groups, observation methods, and moments (indications) of hand hygiene. Data Sources: Electronic searches were conducted in August 2018 using Medline, CINAHL, PsycInfo, Embase, and Web of Science. Reference lists of included studies and related review articles were also screened. Study Selection: English-language, peer-reviewed studies measuring hand hygiene compliance by healthcare workers in an ICU setting using direct observation guided by the World Health Organization's "Five Moments for Hand Hygiene," published since 2009, were included. Data Extraction: Information was extracted on study location, research design, type of ICU, healthcare workers, measurement procedures, and compliance levels. Data Synthesis: Sixty-one studies were included. Most were conducted in high-income countries (60.7%) and in adult ICUs (85.2%). Mean hand hygiene compliance was 59.6%. Compliance levels appeared to differ by geographic region (high-income countries 64.5%, low-income countries 9.1%), type of ICU (neonatal 67.0%, pediatric 41.2%, adult 58.2%), and type of healthcare worker (nursing staff 43.4%, physicians 32.6%, other staff 53.8%). Conclusions: Mean hand hygiene compliance appears notably lower than international targets. The data collated may offer useful indicators for those evaluating, and seeking to improve, hand hygiene compliance in ICUs internationally. Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal's website (http://journals.lww.com/ccmjournal). Supported, in part, by grant from the Health Research Board. Drs. Lambe and Lydon, Ms. Hehir, Ms. Walsh, and Dr. O'Connor's institutions received funding from Irish Health Research Board. Dr. Lydon also received funding from National Doctors Training and Planning, Health Service Executive, and Trinity College Dublin (for role as adjunct assistant professor). Dr. O'Connor's institution received funding from Health Services Executive, and he received funding from Naval Postgraduate School and National University of Ireland Galway. The remaining authors have disclosed that they do not have any potential conflicts of interest. For information regarding this article, E-mail: kathryn.lambe@nuigalway.ie Copyright © by 2019 by the Society of Critical Care Medicine and Wolters Kluwer Health, Inc. All Rights Reserved.

Management of Peripheral Venoarterial Extracorporeal Membrane Oxygenation in Cardiogenic Shock Objectives: Cardiogenic shock is a highly morbid condition in which inadequate end-organ perfusion leads to death if untreated. Peripheral venoarterial extracorporeal membrane oxygenation is increasingly used to restore systemic perfusion despite limited understanding of how to optimally titrate support. This review provides insights into the physiologic basis of extracorporeal membrane oxygenation support and presents an approach to extracorporeal membrane oxygenation management in the cardiogenic shock patient. Data Sources, Study Selection, and Data Extraction: Data were obtained from a PubMed search of the most recent medical literature identified from MeSH terms: extracorporeal membrane oxygenation, cardiogenic shock, percutaneous mechanical circulatory support, and heart failure. Articles included original articles, case reports, and review articles. Data Synthesis: Current evidence detailing the use of extracorporeal membrane oxygenation to support patients in cardiogenic shock is limited to isolated case reports and single institution case series focused on patient outcomes but lacking in detailed approaches to extracorporeal membrane oxygenation management. Unlike medical therapy, in which dosages are either prescribed or carefully titrated to specific variables, extracorporeal membrane oxygenation is a mechanical support therapy requiring ongoing titration but without widely accepted variables to guide treatment. Similar to mechanical ventilation, extracorporeal membrane oxygenation can provide substantial benefit or induce significant harm. The widespread use and present lack of data to guide extracorporeal membrane oxygenation support demands that intensivists adopt a physiologically-based approach to management of the cardiogenic shock patient on extracorporeal membrane oxygenation. Conclusions: Extracorporeal membrane oxygenation is a powerful mechanical circulatory support modality capable of rapidly restoring systemic perfusion yet lacking in defined approaches to management. Adopting a management approach based physiologic principles provides a basis for care. Dr. Keller received support for article research from the National Institutes of Health (1K08HL143342-01). For information regarding this article, E-mail: spkeller@mit.edu Copyright © by 2019 by the Society of Critical Care Medicine and Wolters Kluwer Health, Inc. All Rights Reserved.

Causes of Death in Status Epilepticus Objectives: To determine the causes of death in patients with status epilepticus. To analyze the relative contributions of seizure etiology, seizure refractoriness, use of mechanical ventilation, anesthetic drugs for seizure control, and medical complications to in-hospital and 90-day mortality, hospital length of stay, and discharge disposition. Design: Retrospective cohort. Setting: Single-center neuroscience ICU. Participants: Patients with status epilepticus were identified by retrospective search of electronic database from January 1, 2011, to December 31, 2016. Interventions: Review of electronic medical records. Measurements and Main Results: Demographics, clinical characteristics, treatments, and outcomes were collected. Univariable and multivariable logistic regression analysis were used to determine whether the use of anesthetic drugs, mechanical ventilation, Status Epilepticus Severity Score, refractoriness of seizures, etiology of seizures, or medical complications were associated with in-hospital, 90-day mortality or discharge disposition. Among 244 patients with status epilepticus (mean age was 64 yr [interquartile range, 42–76], 55% male, median Status Epilepticus Severity Score 3 [interquartile range, 2–4]), 24 received anesthetic drug infusions for seizure control. In-hospital and 90-day mortality rates were 9.2% and 19.2%, respectively. Death was preceded by withdrawal of life-sustaining treatment in 19 patients (86.3%) and cardiac arrest in three (13.7%). Only Status Epilepticus Severity Score was associated with in-hospital and 90-day mortality, whereas the use of anesthetic drugs for seizure control, mechanical ventilation, medical complications, etiology, and refractoriness of seizures were not. Hospital length of stay was longer in patients with medical complications (p = 0.0091), refractory seizures (p = 0.0077), and in those who required anesthetic drugs for seizure control (p = 0.0035). Patients who had refractory seizures were less likely to be discharged home (odds ratio, 0.295; CI, 0.143–0.608; p = 0.0009). Conclusions: In this cohort, death primarily resulted from the underlying neurologic disease and withdrawal of life-sustaining treatment and not from our treatment choices. Use of anesthetic drugs, medical complications, and mechanical ventilation were not associated with in-hospital and 90-day mortality. Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal's website (http://journals.lww.com/ccmjournal). The authors have disclosed that they do not have any potential conflicts of interest. For information regarding this article, E-mail: maximilianohawkes@gmail.com Copyright © by 2019 by the Society of Critical Care Medicine and Wolters Kluwer Health, Inc. All Rights Reserved.

Association of Elevated Plasma Interleukin 18 Level With Increased Mortality in a Clinical Trial of Statin Treatment for Acute Respiratory Distress Syndrome Objective: A high plasma level of inflammasome mediator interleukin-18 was associated with mortality in observational acute respiratory distress syndrome cohorts. Statin exposure increases both inflammasome activation and lung injury in mouse models. We tested whether randomization to statin therapy correlated with increased interleukin-18 in the ARDS Network Statins for Acutely Injured Lungs from Sepsis trial. Design: Retrospective analysis of randomized controlled clinical trial. Setting: Multicenter North American clinical trial, the ARDS Network Statins for Acutely Injured Lungs from Sepsis. Patients: Six hundred eighty-three subjects with infection-related acute respiratory distress syndrome, representing 92% of the original trial population. Interventions: Random assignment of rosuvastatin or placebo for up to 28 days or 3 days after ICU discharge. Measurements and Main Results: We measured plasma interleukin-18 levels in all Statins for Acutely Injured Lungs from Sepsis patients with sample available at day 0 (baseline, n = 683) and day 3 (after randomization, n = 588). We tested the association among interleukin-18 level at baseline, rising interleukin-18, and the impact of statin therapy on 60-day mortality, adjusting for severity of illness. Baseline plasma interleukin-18 level greater than or equal to 800 pg/mL was highly associated with 60-day mortality, with a hazard of death of 2.3 (95% CI, 1.7–3.1). Rising plasma interleukin-18 was also associated with increased mortality. For each unit increase in log2 (interleukin-18) at day 3 compared with baseline, the hazard of death increased by 2.3 (95% CI, 1.5–3.5). Subjects randomized to statin were significantly more likely to experience a rise in plasma interleukin-18 levels. Subjects with acute kidney injury, shock, low baseline interleukin-18, and those not receiving systemic corticosteroids were more likely to experience rising interleukin-18. Randomization to statin therapy was associated with rising in interleukin-18 in all of those subsets, however. Conclusions: Elevated baseline plasma interleukin-18 was associated with higher mortality in sepsis-induced acute respiratory distress syndrome. A rise in plasma interleukin-18 was also associated with increased mortality and was more common in subjects randomized to statin therapy in this clinical trial. Drs. Rogers, Hunninghake, Matthay, Steingrub, Wheeler, and Baron helped with conception and design. Dr. Guan, Dr. Trtchounian, Ms. Kozikowski, Ms. DeSouza, Ms. Mogan, Dr. Liu, and Dr. Nakahira helped with experimental procedures. Drs. Rogers, Hunninghake, Kaimal, Desai, and Baron helped with analysis and interpretation. Drs. Rogers, Hunninghake, Kaimal, Desai, Liu, Matthay, Steingrub, Yoon, Nakahira, Choi, and Baron helped with manuscript preparation and revision. Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal's website (http://journals.lww.com/ccmjournal). Supported, in part, by grants from National Institutes of Health (NIH)/National Heart, Lung, and Blood Institute (NHLBI) R01 HL112747, HL111024, HL51856, HL55330, Global Research Laboratory grant number 2016K1A1A2910779, K23 HL125663, NIH/National Center for Advancing Translational Sciences KL2-TR-002385, and NHLBI ARDS Network investigators. Drs. Rogers and Hunninghake, Ms. Kozikowski, Ms. DeSouza, and Drs. Liu, Matthay, Steingrub, Nakahira, Choi, and Baron received support for article research from National Institutes of Health (NIH). Dr. Hunninghake received funding from consulting for Genentech, Boehringer-Ingelheim, the Gerson Lehrman Group, and Mistubishi Chemical for work unrelated to this submission. Ms. Kozikowski's institution received funding from Brigham and Womens Hospital. Ms. DeSouza disclosed work for hire. Dr. Liu's institution received funding from NHLBI, National Institute of Diabetes and Digestive and Kidney Disease, and she received funding from National Policy Forum on Critical Care and Acute Renal Failure, Achaogen (consultant), Durect (consultant), Theravance (consultant), Quark (consultant), Potrero Med (consultant), Amgen (stockholder), and Baxter (presenter at sponsored meeting). Dr. Matthay's institution received funding from Bayer Pharmaceuticals, Department of Defense, GlaxoSmithKline, and he received other support from CSL Behring, Roche-Genentec, Quark Pharmaceuticals, Boerhinger-Ingelheim, Cerus Therapeutics, and NHLBI. Dr. Choi's institution received funding from NIH; he received funding from Teva Pharmaceuticals; and he disclosed that he is a cofounder, stock holder, and serves on the Scientific Advisory Board for Proterris, which develops therapeutic uses for carbon monoxide, and he has a use patent on carbon monoxide. Dr. Baron's institution received funding from the NIH. The remaining authors have disclosed that they do not have any potential conflicts of interest. For information regarding this article, E-mail: ajrogers@stanford.edu; rbaron@partners.org Copyright © by 2019 by the Society of Critical Care Medicine and Wolters Kluwer Health, Inc. All Rights Reserved.

Multi-Compartment Profiling of Bacterial and Host Metabolites Identifies Intestinal Dysbiosis and Its Functional Consequences in the Critically Ill Child Objectives: Adverse physiology and antibiotic exposure devastate the intestinal microbiome in critical illness. Time and cost implications limit the immediate clinical potential of microbial sequencing to identify or treat intestinal dysbiosis. Here, we examined whether metabolic profiling is a feasible method of monitoring intestinal dysbiosis in critically ill children. Design: Prospective multicenter cohort study. Setting: Three U.K.-based PICUs. Patients: Mechanically ventilated critically ill (n = 60) and age-matched healthy children (n = 55). Interventions: Collection of urine and fecal samples in children admitted to the PICU. A single fecal and urine sample was collected in healthy controls. Measurements and Main Results: Untargeted and targeted metabolic profiling using 1H-nuclear magnetic resonance spectroscopy and liquid chromatography-mass spectrometry or urine and fecal samples. This was integrated with analysis of fecal bacterial 16S ribosomal RNA profiles and clinical disease severity indicators. We observed separation of global urinary and fecal metabolic profiles in critically ill compared with healthy children. Urinary excretion of mammalian-microbial co-metabolites hippurate, 4-cresol sulphate, and formate were reduced in critical illness compared with healthy children. Reduced fecal excretion of short-chain fatty acids (including butyrate, propionate, and acetate) were observed in the patient cohort, demonstrating that these metabolites also distinguished between critical illness and health. Dysregulation of intestinal bile metabolism was evidenced by increased primary and reduced secondary fecal bile acid excretion. Fecal butyrate correlated with days free of intensive care at 30 days (r = 0.38; p = 0.03), while urinary formate correlated inversely with vasopressor requirement (r = –0.2; p = 0.037). Conclusions: Disruption to the functional activity of the intestinal microbiome may result in worsening organ failure in the critically ill child. Profiling of bacterial metabolites in fecal and urine samples may support identification and treatment of intestinal dysbiosis in critical illness. This is an open access article distributed under the Creative Commons Attribution License 4.0 (CCBY), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Drs. Wijeyesekera and Wagner contributed equally. Dr. Wijeyesekera developed and supervised the metabolic profiling strategy, undertook data analysis, and wrote the article. Dr. Wagner developed and supervised the microbial profiling strategy, undertook data analysis, and wrote the article. Dr. De Goffau analyzed the microbial data and co-wrote the article. Ms. Thurston undertook sample processing and data analysis. Drs. Rodrigues Sabino and Zaher, Ms. White, Ms. Ridout, and Dr. Valla undertook sample processing, data collection, and analysis. Dr. Meyer undertook data analysis. Drs. Peters, Branco, Torok, Meyer, and Klein contributed to protocol development, supervised data analysis, and co-wrote the article. Dr. Parkhill developed the microbial profiling protocol, supervised all aspects of the microbial data analysis, and co-wrote the article. Drs. Frost and Holmes developed the metabolic profiling protocol, supervised all aspects of the metabolic data analysis, and co-wrote the article. Dr. Pathan conceived and supervised the study and wrote the article. Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal's website (http://journals.lww.com/ccmjournal). Aspects of the work were funded by an Imperial College Biomedical Research Centre award (to Drs. Holmes and Pathan), the Evelyn Trust (to Drs. Parkhill and Pathan), a Wellcome Trust Core Informatics Award (to Dr. Parkhill), Great Ormond Street Hospital Children's Charity (to Drs. Peters and Ramnarayan), and a Levi-Montalcini award from the European Society of Intensive Care Medicine (to Dr. Pathan). The research was supported by the National Institute for Health Research Biomedical Research Centres based at Cambridge University Hospitals National Health Service (NHS) Foundation Trust, Great Ormond Street Hospital NHS Foundation Trust, Imperial College Healthcare NHS Trust, and Imperial College London. Dr. Rodrigues Sabino's institution received funding from National Institute for Health Research Imperial Biomedical Research Centre Institute of Translational Medicine and Therapeutics Call for Experimental Medicine Proposals. Dr. Valla received funding from Baxter and Nutricia. Dr. Meyer received funding from academic lectures for Danone, Nestle and Mead Johnson, and from the Mead Johnson Allergy Advisory Board. Dr. Frost's institution received funding from Nestle and Heptares, and he received support for article research from Research Councils UK and Bill & Melinda Gates Foundation. Drs. Frost, Parkhill, and Pathan received support for article research from Wellcome Trust/Charity Open Access Fund. Dr. Parkhill's institution received funding from Wellcome Trust, and he received funding from Next Gen Diagnostics Llc. Dr. Pathan's institution received funding from European Society of Intensive Care Medicine, Evelyn Trust, and Wellcome Trust. The remaining authors have disclosed that they do not have any potential conflicts of interest. Address requests for reprints to: Nazima Pathan, FRCPCH, PhD, Department of Paediatrics, University of Cambridge, Level 8, Addenbrookes Hospital, Hills Road, Cambridge CB2 0QQ, United Kingdom. E-mail: np409@cam.ac.uk Copyright © by 2019 by the Society of Critical Care Medicine and Wolters Kluwer Health, Inc. All Rights Reserved.

Interprofessional Shared Decision-Making in the ICU: A Systematic Review and Recommendations From an Expert Panel Objectives: There is growing recognition that high-quality care for patients and families in the ICU requires exemplary interprofessional collaboration and communication. One important aspect is how the ICU team makes complex decisions. However, no recommendations have been published on interprofessional shared decision-making. The aim of this project is to use systematic review and normative analysis by experts to examine existing evidence regarding interprofessional shared decision-making, describe its principles and provide ICU clinicians with recommendations regarding its implementation. Data Sources: We conducted a systematic review using MEDLINE, Cumulative Index to Nursing and Allied Health Literature, and Cochrane databases and used normative analyses to formulate recommendations regarding interprofessional shared decision-making. Study Selection: Three authors screened titles and abstracts in duplicate. Data Synthesis: Four papers assessing the effect of interprofessional shared decision-making on quality of care were identified, suggesting that interprofessional shared decision-making is associated with improved processes and outcomes. Five recommendations, largely based on expert opinion, were developed: 1) interprofessional shared decision-making is a collaborative process among clinicians that allows for shared decisions regarding important treatment questions; 2) clinicians should consider engaging in interprofessional shared decision-making to promote the most appropriate and balanced decisions; 3) clinicians and hospitals should implement strategies to foster an ICU climate oriented toward interprofessional shared decision-making; 4) clinicians implementing interprofessional shared decision-making should consider incorporating a structured approach; and 5) further studies are needed to evaluate and improve the quality of interprofessional shared decision-making in ICUs. Conclusions: Clinicians should consider an interprofessional shared decision-making model that allows for the exchange of information, deliberation, and joint attainment of important treatment decisions. Drs. Michalsen, Ganz, White, Jensen, Metaxa, Latour, Truog, and Curtis conceptualized the article. Drs. Michalsen, Long, and Ganz reviewed the literature. All authors drafted and revised the article for important intellectual content as well as final approval for the version submitted. Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal's website (http://journals.lww.com/ccmjournal). Dr. DeKeyser Ganz's institution received funding from the Israel Institute of Health Policy Research, and she received funding from the Israel Higher Education Commission. Dr. Metaxa received funding from European Society of Intensive Care Medicine. Dr. Truog received funding from Covance (Data Safety Monitoring Committee) and Sanofi (Data Safety Monitoring Committees). Dr. Kesecioglu reports receiving honorarium from Xenios A.G. The remaining authors have disclosed that they do not have any potential conflicts of interest. Ethical standards: This research does not involve human participants or animals. For information regarding this article, E-mail: jrc@u.washington.edu Copyright © by 2019 by the Society of Critical Care Medicine and Wolters Kluwer Health, Inc. All Rights Reserved.

Outcome Prediction in Postanoxic Coma With Deep Learning Objectives: Visual assessment of the electroencephalogram by experienced clinical neurophysiologists allows reliable outcome prediction of approximately half of all comatose patients after cardiac arrest. Deep neural networks hold promise to achieve similar or even better performance, being more objective and consistent. Design: Prospective cohort study. Setting: Medical ICU of five teaching hospitals in the Netherlands. Patients: Eight-hundred ninety-five consecutive comatose patients after cardiac arrest. Interventions: None. Measurements and Main Results: Continuous electroencephalogram was recorded during the first 3 days after cardiac arrest. Functional outcome at 6 months was classified as good (Cerebral Performance Category 1–2) or poor (Cerebral Performance Category 3–5). We trained a convolutional neural network, with a VGG architecture (introduced by the Oxford Visual Geometry Group), to predict neurologic outcome at 12 and 24 hours after cardiac arrest using electroencephalogram epochs and outcome labels as inputs. Output of the network was the probability of good outcome. Data from two hospitals were used for training and internal validation (n = 661). Eighty percent of these data was used for training and cross-validation, the remaining 20% for independent internal validation. Data from the other three hospitals were used for external validation (n = 234). Prediction of poor outcome was most accurate at 12 hours, with a sensitivity in the external validation set of 58% (95% CI, 51–65%) at false positive rate of 0% (CI, 0–7%). Good outcome could be predicted at 12 hours with a sensitivity of 48% (CI, 45–51%) at a false positive rate of 5% (CI, 0–15%) in the external validation set. Conclusions: Deep learning of electroencephalogram signals outperforms any previously reported outcome predictor of coma after cardiac arrest, including visual electroencephalogram assessment by trained electroencephalogram experts. Our approach offers the potential for objective and real time, bedside insight in the neurologic prognosis of comatose patients after cardiac arrest. Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal's website (http:/journals.lww.com/ccmjournal). Dr. van Putten is co-founder of Clinical Science Systems, a supplier of electroencephalogram systems for Medisch Spectrum Twente. The remaining authors have disclosed that they do not have any conflicts of interest. This work was performed in Medisch Spectrum Twente, Rijnstate Hospital, St. Antonius Hospital, University Medical Center Groningen and VieCuri Medical Center, The Netherlands. For information regarding this article, E-mail: m.tjepkema-cloostermans@mst.nl Copyright © by 2019 by the Society of Critical Care Medicine and Wolters Kluwer Health, Inc. All Rights Reserved.

XueBiJing Injection Versus Placebo for Critically Ill Patients With Severe Community-Acquired Pneumonia: A Randomized Controlled Trial Objectives: To investigate whether XueBiJing injection improves clinical outcomes in critically ill patients with severe community-acquired pneumonia. Design: Prospective, randomized, controlled study. Setting: Thirty-three hospitals in China. Patients: A total of 710 adults 18–75 years old with severe community-acquired pneumonia. Interventions: Participants in the XueBiJing group received XueBiJing, 100 mL, q12 hours, and the control group received a visually indistinguishable placebo. Measurements and Main Results: The primary outcome was 8-day improvement in the pneumonia severity index risk rating. Secondary outcomes were 28-day mortality rate, duration of mechanical ventilation and total duration of ICU stay. Improvement in the pneumonia severity index risk rating, from a previously defined endpoint, occurred in 203 (60.78%) participants receiving XueBiJing and in 158 (46.33%) participants receiving placebo (between-group difference [95% CI], 14.4% [6.9–21.8%]; p < 0.001). Fifty-three (15.87%) XueBiJing recipients and 84 (24.63%) placebo recipients (8.8% [2.4–15.2%]; p = 0.006) died within 28 days. XueBiJing administration also decreased the mechanical ventilation time and the total ICU stay duration. The median mechanical ventilation time was 11.0 versus 16.5 days for the XueBiJing and placebo groups, respectively (p = 0.012). The total duration of ICU stay was 12 days for XueBiJing recipients versus 16 days for placebo recipients (p = 0.004). A total of 256 patients experienced adverse events (119 [35.63%] vs 137 [40.18%] in the XueBiJing and placebo groups, respectively [p = 0.235]). Conclusions: In critically ill patients with severe community-acquired pneumonia, XueBiJing injection led to a statistically significant improvement in the primary endpoint of the pneumonia severity index as well a significant improvement in the secondary clinical outcomes of mortality, duration of mechanical ventilation and duration of ICU stay. This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal. Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal's website (http://journals.lww.com/ccmjournal). Supported, in part, by a Tianjin Science and Technology committee grant (14ZXLJSY00230) and National Natural Science Foundation of China (81630001,81490533). Drs. X. Yu and Zhi Liu disclosed work for hire. Dr. B. Zhang disclosed government work. The remaining authors have disclosed that they do not have any potential conflicts of interest. Clinical Trial Registration: http://www.chictr.org.cn/index.aspx. Unique identifier: ChiCTR-TRC-13003534. For information regarding this article, E-mail: bai.chunxue@zs-hospital.sh.cn; shanghongcai@126.com Copyright © by 2019 by the Society of Critical Care Medicine and Wolters Kluwer Health, Inc. All Rights Reserved.

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