2015 Annual Update in Intensive Care and Emergency Medicine 2015 Edited by J.-L.Vincent 123 Annual Update in Intensive Care and Emergency Medicine 2015 The series Annual Update in Intensive Care and Emergency Medicine is the continuation of the series entitled Yearbook of Intensive Care Medicine in Europe and Intensive Care Medicine: Annual Update in the United States Jean-Louis Vincent Editor Annual Update in Intensive Care and Emergency Medicine 2015 Editor Prof Jean-Louis Vincent Université libre de Bruxelles Dept of Intensive Care Erasme Hospital Brussels, Belgium jlvincen@ulb.ac.be ISSN 2191-5709 ISBN 978-3-319-13760-5 DOI 10.1007/978-3-319-13761-2 ISBN 978-3-319-13761-2 (eBook) Springer Cham Heidelberg New York Dordrecht London © Springer International Publishing Switzerland 2015 This work is subject to copyright All rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilm or in any other way, and storage in data banks Duplication of this publication or parts thereof is permitted only under the provisions of the German Copyright Law of September 9, 1965, in its current version, and permission for use must always be obtained from Springer Violations are liable to prosecution under the German Copyright Law The use of general descriptive names, registered names, trademarks, etc in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made Cover design: WMXDesign GmbH, Heidelberg Printed on acid-free paper Springer International Publishing AG Switzerland is part of Springer Science+Business Media (www.springer.com) Contents Common Abbreviations Part I xi Infections Early Identification of Ventilator-associated Pneumonia Causative Pathogens: Focus on the Value of Gram-stain Examination C Chiurazzi, A Motos-Galera, and A Torres Central Line-associated Bloodstream Infections: A Critical Look at the Role and Research of Quality Improvement Interventions and Strategies 15 K Blot, D Vogelaers, and S Blot Clostridium difficile Infection 25 M H Wilcox, M J G T Vehreschild, and C E Nord Viral Sepsis 37 P Amin and V Amin Part II Antimicrobials and Resistance Light and Shade of New Antibiotics 63 M Bassetti, P Della Siega, and D Pecori Optimizing Antimicrobial Efficacy at Minimal Toxicity: A Novel Indication for Continuous Renal Replacement Therapy? 85 P M Honoré, R Jacobs, and H D Spapen v vi Contents Combatting Resistance in Intensive Care: The Multimodal Approach of the Spanish ICU “Zero Resistance” Program 91 The Scientific Expert Committee for the “Zero Resistance” Project Immune System Dysfunction and Multidrug-resistant Bacteria in Critically Ill Patients: Inflammasones and Future Perspectives 105 M Girardis, S Busani, and S De Biasi Part III Sepsis Tachycardia in Septic Shock: Pathophysiological Implications and Pharmacological Treatment 115 A Morelli, A D’Egidio, and M Passariello Angiotensin II in Septic Shock 129 T D Corrêa, J Takala, and S M Jakob ˇ-Blockers in Critically Ill Patients: From Physiology to Clinical Evidence 139 S Coppola, S Froio, and D Chiumello Part IV Oxygenation and Respiratory Failure Prehospital Endotracheal Intubation: Elemental or Detrimental? 155 P E Pepe, L P Roppolo, and R L Fowler Hyperoxia in Intensive Care and Emergency Medicine: Dr Jekyll or Mr Hyde? An Update 167 S Hafner, P Radermacher, and P Asfar Extracorporeal Gas Exchange for Acute Respiratory Failure in Adult Patients: A Systematic Review 179 M Schmidt, C Hodgson, and A Combes Update on the Role of Extracorporeal CO2 Removal as an Adjunct to Mechanical Ventilation in ARDS 207 P Morimont, A Batchinsky, and B Lambermont Fundamentals and Timing of Tracheostomy: ICU Team and Patient Perspectives 219 V Pandian and M Mirski Shared Decision-making to Pursue, Withhold or Withdraw Invasive Mechanical Ventilation in Acute Respiratory Failure 233 M E Wilson, P R Bauer, and O Gajic Contents Part V vii Monitoring New Fully Non-invasive Hemodynamic Monitoring Technologies: Groovy or Paltry Tools 249 J Benes and E Kasal Assessing Global Perfusion During Sepsis: SvO2 , Venoarterial PCO2 Gap or Both? 259 J.-L Teboul and X Monnet An Update on Cerebral Oxygenation Monitoring, an Innovative Application in Cardiac Arrest and Neurological Emergencies 273 B Schneider, T J Abramo, and G Albert Part VI Cardiac Arrest Out-of-hospital Cardiac Arrest and Survival to Hospital Discharge: A Series of Systemic Reviews and Meta-analyses 289 M Vargas, Y Sutherasan, and P Pelosi Cooling Techniques for Targeted Temperature Management Post-cardiac Arrest 315 C Vaity, N Al-Subaie, and M Cecconi Part VII Fluids How Does Volume Make the Blood Go Around? 327 S Magder Clinical Implications from Dynamic Modeling of Crystalloid Fluids 339 R G Hahn Part VIII Renal Injury Urinary Electrolyte Monitoring in the Critically Ill: Revisiting Renal Physiology 351 P Caironi, T Langer, and M Ferrari Management of AKI: The Role of Biomarkers 365 Z Ricci, G Villa, and C Ronco Bone Morphogenetic Protein 7: An Emerging Therapeutic Target for Sepsis-associated Acute Kidney Injury 379 X Chen, X Wen, and J A Kellum viii Contents Long-term Sequelae from Acute Kidney Injury: Potential Mechanisms for the Observed Poor Renal Outcomes 391 M Varrier, L G Forni, and M Ostermann Part IX Hepatic and Abdominal Issues Application of the Acute Kidney Injury Network Criteria in Patients with Cirrhosis and Ascites: Benefits and Limitations 405 P Angeli, M Tonon, and S Piano Intensive Care Management of Severe Acute Liver Failure 415 S Warrillow and R Bellomo Human Albumin: An Important Bullet Against Bacterial Infection in Patients with Liver Cirrhosis? 431 M Bernardi, M Domenicali, and P Caraceni Open Abdomen Management: Challenges and Solutions for the ICU Team 447 J J De Waele and M L N G Malbrain Part X Nutrition Protein Intake in Critical Illness 459 O Rooyackers and J Wernerman Part XI Trauma and Massive Bleeding Rational and Timely Use of Coagulation Factor Concentrates in Massive Bleeding Without Point-of-Care Coagulation Monitoring 471 O Grottke, D R Spahn, and R Rossaint Optimal Temperature Management in Trauma: Warm, Cool or In-between? 481 M C Reade and M Lumsden-Steel Detection of Consciousness in the Severely Injured Brain 495 J Stender, A Gjedde, and S Laureys Contents Part XII ix Neuromuscular Considerations The Role of Local and Systemic Inflammation in the Pathogenesis of Intensive Care Unit-acquired Weakness 509 E Witteveen, M J Schultz, and J Horn Critical Illness is Top Sport 519 M Suker, C Ince, and C van Eijck Part XIII Rapid Response Teams Vital Signs: From Monitoring to Prevention of Deterioration in General Wards 533 M Cardona-Morrell, M Nicholson, and K Hillman Rapid Response Systems: Are they Really Effective? 547 C Sandroni, S D’Arrigo, and M Antonelli Severe Sepsis Beyond the Emergency Department and ICU: Targeting Early Identification and Treatment on the Hospital Floor 557 C A Schorr, J Sebastien, and R P Dellinger Part XIV Data Management State of the Art Review: The Data Revolution in Critical Care 573 M Ghassemi, L A Celi, and D J Stone Creating a Learning Healthcare System in the ICU 587 J Yu and J M Kahn Index 597 588 J Yu and J M Kahn ICU, review some of the key barriers to creating a learning healthcare system in the ICU, and outline a roadmap through we which we can move towards this goal What Is Meant by a “Learning Healthcare System”? The concept of the learning healthcare system is a response to many different problems in healthcare, but chief among these is the notion that providers lack timely and actionable evidence to inform clinical decisions Despite enormous investments in clinical research over the last decades, few clinical decisions are informed by robust evidence [4] Moreover, due to the slow pace of and high expense research, innovations in healthcare, both in terms of therapeutics and healthcare delivery strategies, go untested for years after their introduction, if at all [5] The learning healthcare system addresses this problem by integrating clinical care and research through a mature electronic health record (Fig 1) [3] Rather than research occurring in parallel to clinical care, knowledge generation is the natural byproduct of clinical care, all patients actively and continuously contribute data for research, which can then be used to rapidly generate clinical evidence In turn, that new evidence is directly applied in real time Such a system would have several key elements, including a robust, interoperable electronic medical record (EMR) that can be quickly and easily mined; a culture of shared responsibility for healthcare improvement on the part of clinicians, researchers and the public, such that patients willingly participate in the generation of new knowledge as part of their role in the larger healthcare system; and alignment of incentives to rapidly generate and apply new knowledge, eliminating the disconnect between research and clinical care [3] The Rationale for the Learning Healthcare System in Critical Care There are several problems with current critical care systems that would directly be impacted by a learning healthcare system (Box 1) Box Rationale for the Learning Healthcare System Research is not routinely implemented in practice Research is not informed by clinical practice Research is costly and time-consuming Research paradigms not answer the right questions Research is not routinely implemented in practice The gap between clinical evidence and clinical practice is well documented in critical care [6] For example, several studies convincingly demonstrate that patients with acute respiratory Creating a Learning Healthcare System in the ICU Traditional healthcare system Patient care • Inefficient Learning healthcare system Patient care and Research • Rare participation translation of evidence into practice 589 in research • Many questions not answered • All patients participate in research Electronic medical record Engaged patients • Key questions consistently answered • Efficient Research translation of evidence into practice Supportive regulatory agencies Fig A model of the current healthcare system and a learning healthcare system In the current healthcare system, research runs in parallel to clinical care In the learning healthcare system, research is integrated into clinical care supported by a mature electronic medical record distress syndrome (ARDS) not routinely receive a lung protective ventilation strategy proven to save lives [7, 8] Moreover, patients receiving mechanical ventilation frequently not receive evidence-based preventive care, daily interruption of continuous sedation or daily spontaneous breathing trials [9] As a consequence there is preventable mortality among critically ill patients, with wide variation in outcomes across hospitals not accounted for by variation in case-mix [10] Research is not informed by clinical practice New evidence itself is frequently not directly applicable to clinical practice Because of the disconnect between clinicians and researchers, clinical trials and observational studies not always answer the most important and timely research questions For example, pharmaceutical companies generally fund only research into proprietary therapeutics with potential to generate revenue [11] Generic medicines that lack a profit potential but have clinical potential, such as heparin in sepsis [12] and antidepressants for post-ICU depression [13], are rarely subjected to large trials Additionally, the vast majority of patient care is wasted from a learning standpoint Every day patients are subjection to variations in treatments, yet we have no capacity to learn from our experiences in a systematic way We may ‘learn by doing’ as individual clinicians, but these experiences generate no collective knowledge that can be used to advance the field Meanwhile, front-line clinicians with important questions about how to best deliver care have no mechanism to drive knowledge discovery, instead relying on the academic research enterprise Research is costly and time-consuming Even a small clinical trial in the ICU costs upwards of $5 million, and will ultimately answer only a relatively narrow set of research questions Moreover, most ICU-based clinical trials take years 590 J Yu and J M Kahn to complete, furthering the disconnect between evidence generation and clinical practice [14] For example, a recent study of weaning strategies in prolonged mechanical ventilation took over 10 years to enroll 316 patients [15], and a research question that was timely over a decade ago remained unanswered until just recently Another example can be found in the rise and fall of drotrecogin alfa for severe sepsis It took three clinical trials conducted over nearly 15 years to assess the value of this drug, which was ultimately withdrawn from the market due to lack of efficacy [16] Despite all these trials, reasonable doubt still exists as to whether drotrecogin alfa may still be life saving in some groups of patients, such as those with meningococcemia [17] Many years and untold millions of dollars later, we still not fully understand whether and when drotrecogin alfa may work Research paradigms not answer the right questions An analogous problem is that the research enterprise itself is not set up to answer the right questions Randomized controlled trials (RCTs), the mainstay of causal inference in biomedical research, are extremely limited in many regards RCTs can only answer questions in highly selected patient populations that are frequently not representative of the population of interest Thus, although RCTs often yield internally valid results, these results may be just as biased, if not more so, than the results of carefully conducted observational trials [18] RCTs also only give results on the average treatment effects for the selected population They cannot give other important results, such as the proportion of patients in a population who might benefit from a treatment, or whether a treatment will benefit a specific individual patient [18] This information is arguably more important to decision makers such as clinicians, who are treating individual patients, and regulatory agencies, who are making determinations about drug approval populations rather than means [19] How the Learning Healthcare System Would Help A learning healthcare system in the ICU would overcome these problems in several ways To correct the problem of research failing to inform practice, the learning healthcare system would incorporate clinical decision support and protocols at the point of care, overcoming some of the frequently cited barriers to evidence-based practice [20] These decision support systems would not be static but would rapidly adapt to new data informing outcome probabilities with recent treatments They could also be used to rapidly test different pragmatic treatment strategies, randomly assigning clinicians or ICUs to different permutations of the decisions support system To correct the problem of research failing to be informed by practice, every patient would directly contribute data to an ICU registry that could be queried to not only determine current treatment patterns but also assess treatment variation, ensuring that clinical studies answer questions of relevance to patients and clinicians Creating a Learning Healthcare System in the ICU 591 Clinicians themselves could also query these databases, asking questions like “for patients like mine, which treatment did they most often receive, X or Y?” To correct the problem of costly and time consuming trials, the learning healthcare system would be able to rapidly and facilely enroll large numbers of patients in clinical studies Both patient enrollment and clinical data collection would be facilitated by an EMR that is at once complete and easily queried [21] The recurring problem of clinical trials being underpowered because the outcome incidence is lower than anticipated would be reduced or eliminated, since researchers would always have access to recent and accurate patient-level data on disease incidence and outcomes [22] To correct the problem that RCTs are not necessarily the gold standard for clinical decision making, the learning healthcare system would facilitate rapid observational studies as well as large-scale, effectiveness trials designed to answer questions of immediate importance to clinicians [23] Exploiting natural variation in treatment processes, we can learn about how treatments work in the real-world instead of only relying on experimental evidence Experiments are important, but all types of evidence will be essential for understanding treatment effects and guiding decision making Examples Under a learning healthcare system in the ICU, the daily practice of critical care would be directly and tightly tied to research, and vice versa Here we offer several examples of how this might work in practice Understanding Disease Burden Investigators wishing to design a clinical trial need accurate, up-to-date information on the burden of disease This information is necessary to assess the potential clinical value of a novel treatment, to understand the possible number of eligible patients at each center, and to perform accurate power calculations based on the incidence of the outcome of interest Rather than look to the published literature, which is often out of date and subject to publication bias, the investigators could query the ICUs in the learning healthcare system Using their EMRs, the ICUs could quickly return accurate and actionable data for use in planning the trial or deciding whether to go ahead with it in the first place 592 J Yu and J M Kahn Conducting a Randomized Controlled Trial Next, the investigators could conduct the clinical trial directly in the participating ICUs making use of the EMR The EMR could automatically screen each admitted patient for eligibility When the EMR discovers an eligible patient, an automated notice could be sent to the physician asking for permission to enroll the patient After enrollment, the EMR could be used to collect outcome data Such a system would enable researchers to conduct trials of much larger scope than is currently possible, at much reduced cost The trial could be even more powerful by directly tying randomization to real-time treatment decisions For example, if the EMR notices that an ICU physician is prescribing a certain therapy, perhaps through computerized physician order entry (CPOE), it could embed an enrollment alert directly into the CPOE system, inquiring: “I notice that you are prescribing drug X In similar scenarios, physicians have prescribed both X and Y Would you consider randomizing your patient to receive either X or Y?”, then acting on the physician’s response In this way, research can be directly embedded in clinical care, greatly increasing the efficiency of the research enterprise Assessing the Effectiveness of a Novel Therapeutic The learning healthcare system could also be used for post-marketing surveillance of novel therapeutics based on the EMR If unexpected side effects are encountered, these could be tracked using the EMR and fed back to a central repository quickly As experience with the drug grows, investigators could quickly assemble a large, continually updated patient registry that could be used to perform observational studies of effectiveness, controlling for observed covariates using traditional methods like regression, and controlling for unobserved covariates using instrumental variables and other techniques for causal inference in observational data [24] Most powerfully, with a large enough dataset the analyses could be customized for individual patients, not just populations In this way, physicians could determine how patients like theirs responded to the drug, better informing individualized decisions at the bedside Creating a Learning Healthcare System in the ICU 593 Barriers Achieving a fully realized learning healthcare system faces numerous barriers (Box 2) Box Barriers to the Learning Healthcare System Poorly functional electronic health records Data privacy The need for informed consent in prospective studies Competition among providers Poorly Functional EMRs Most existing EMRs are poorly equipped to handle the demands of a true learning healthcare system [25] The data exist in complex databases that are difficult to query, often requiring customized programming languages and other poorly interoperable tools to abstract data for research Different systems use different data architectures and naming conventions, making data sharing and collaboration across health systems difficult Alternatively, these data must be cleaned and archived prior to use, making them ineffective for real-time decision making The human interfaces for these systems are also lacking, with alarms that are easily ignored or, perhaps more frequently, non-specific and thus more annoying than helpful [26] Privacy and Regulatory Barriers Data privacy and confidentiality are important concerns, particularly in the modern era of hackers and security breaches, which can occur in even the most secure systems Yet they also pose a substantial barrier to the learning healthcare system Many regulations, including the US Health Insurance Portability and Accountability Act, require permission to use protected health data for research if it is at all possible This well-meaning regulation works on a small scale, but becomes infeasible and impractical on a large scale Similarly, the learning healthcare system will still rely on randomized study designs, which, when the studies involve hundreds of thousands patients, can make informed consent a prohibitive obstacle to success [27] Yet decades of work in the ethics of research appropriately prohibits patient-level randomization without informed consent in most biomedical research 594 J Yu and J M Kahn Competition Among Providers A successful learning healthcare system requires collaboration across numerous healthcare providers, both within provider types (e g., across multiple hospitals) and across provider types (e g., across hospitals and insurers) [28] Yet many of these stakeholders compete for patients and revenue, making large scale collaboration a challenge Researchers also can be more competitive than collaborative For example, when the learning healthcare system requires coordination across multiple academic medical centers, issues such as grant revenue and academic credit must be dealt with, posing a potential challenge Future Directions These challenges, although large, are not insurmountable, and even at present we can take several steps toward the learning healthcare system in the ICU First, we can work to build more useful EMRs that are designed not just as data repositories but as tools for data analysis and interpretation The information technology already exists and is widely applied in other industries – we simply need to bring it to healthcare EMRs can be made more interoperable by building off novel datasharing tools such as I2B2, which facilitates collaborative data repositories [29], and SHRINE, which allows for multiple EMRs to be queried simultaneously without moving data across firewalls [30] Search functions and natural language processing can also be incorporated into EMRs For example, Google anticipates what users are searching for and offers suggestions based on the prior searches of the individual user and other users Similarly, EMRs could see what other clinicians have done for similar patients and make similar suggestions Second, we can convene a large-scale effort to deal with the regulatory and privacy issues that go part and parcel with the learning healthcare system [31] A first step is better engaging patients as partners in the research process We have already accepted the notion that patients need not consent to give their records for quality improvement, since the overall goal of quality improvement is to improve individual patient health Yet this goal is not in principle different from that of the learning healthcare system Perhaps by working more closely with patients and informing them of the potential uses of their data we can move towards a world where personal health information can be more rapidly used to generate new knowledge Ultimately we can create novel ‘integrated consent’ paradigms that give new flexibility to the learning healthcare system while offering patients important protection [32] Creating a Learning Healthcare System in the ICU 595 Conclusion A learning healthcare system for the ICU is a laudable goal In many ways, the ICU is a ripe area for the development of such a system, given its history as a data-rich clinical environment [33] The idea is also catching on Since first advanced by the IoM, the learning healthcare system has been endorsed by the US Patient Centered Outcomes Research Institute, which is working to develop a learning healthcare system in the US through its Clinical Data Research Networks [34] Moreover, much of what we describe as the learning healthcare system is already gaining traction through other names, such as ‘big data’ and ‘analytics’ [35] Together, these concepts underlie an essential effort to better use data to improve healthcare outcomes, not just through quality improvement but also through research and the creation of generalizable knowledge The continuing gap between evidence and practice, and the lack of evidence for the bulk of clinical decisions, demands no less References Riley WT, Glasgow RE, Etheredge L, Abernethy AP (2013) Rapid, responsive, relevant (R3) research: a call for a rapid learning health research enterprise Clin Transl Med 2:10 Friedman CP, Wong AK, Blumenthal D (2010) Achieving a nationwide learning health system Sci Transl Med 2:57–29 Olsen L, Aisner D, McGinnis JM (2007) Roundtable on Evidence-Based Medicine: The Learning Healthcare System: Workshop Summary National Academies Press, Washington Eddy DM (2005) Evidence-based medicine: a unified approach Health Aff (Millwood) 24:9– 17 Berwick DM (2003) Disseminating innovations in health care JAMA 289:1969–1975 Kahn JM (2009) Disseminating clinical trial results in critical care Crit Care Med 37:S147– S153 Cooke CR, Watkins TR, Kahn JM et al (2008) The effect of an intensive care unit staffing model on tidal volume in patients with acute lung injury Crit Care 12:R134 Esteban A, Ferguson ND, Meade MO et al (2008) Evolution of mechanical ventilation in response to clinical research Am J Respir Crit Care Med 177:170–177 Kahn JM, Brake H, Steinberg KP (2007) Intensivist physician staffing and the process of care in academic medical centres Qual Saf Health Care 16:329–333 10 Chen LM, Render M, Sales A, Kennedy EH, Wiitala W, Hofer TP (2012) Intensive care unit admitting patterns in the veterans affairs health care system Arch Intern Med 172:1220–1226 11 Angell M (2008) Industry-sponsored clinical research: a broken system JAMA 300:1069– 1071 12 Davidson BL, Geerts WH, Lensing AWA (2002) Low-dose heparin for severe sepsis N Engl J Med 347:1036–1037 13 Weinert C, Meller W (2006) Epidemiology of depression and antidepressant therapy after acute respiratory failure Psychosomatics 47:399–407 14 Ioannidis JP (1998) Effect of the statistical significance of results on the time to completion and publication of randomized efficacy trials JAMA 279:281–286 15 Jubran A, Grant BJB, Duffner LA et al (2013) Effect of pressure support vs unassisted breathing through a tracheostomy collar on weaning duration in patients requiring prolonged mechanical ventilation: A randomized trial JAMA 309:671–677 596 J Yu and J M Kahn 16 Wenzel RP, Edmond MB (2012) Septic shock – evaluating another failed treatment N Engl J Med 366:2122–2124 17 Faust SN, Levin M, Harrison OB et al (2001) Dysfunction of endothelial protein C activation in severe meningococcal sepsis N Engl J Med 345:408–416 18 Deaton A (2010) Instruments, Randomization, and Learning about Development Journal of Economic Literature 48:424–455 19 Harris KM, Remler DK (1998) Who is the marginal patient? Understanding instrumental variables estimates of treatment effects Health Serv Res 33:1337–1360 20 Garg AX, Adhikari NKJ, McDonald H et al (2005) Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review JAMA 293:1223–1238 21 D’Avolio L, Ferguson R, Goryachev S et al (2012) Implementation of the Department of Veterans Affairs’ first point-of-care clinical trial J Am Med Inform Assoc 19:e170–e176 22 Halpern SD, Karlawish JHT, Berlin JA (2002) The continuing unethical conduct of underpowered clinical trials JAMA 288:358–362 23 Dahabreh IJ, Kent DM (2014) Can the learning health care system be educated with observational data? JAMA 312:129–130 24 Iwashyna TJ, Kennedy EH (2013) Instrumental variable analyses Exploiting natural randomness to understand causal mechanisms Ann Am Thorac Soc 10:255–260 25 Blumenthal D, Tavenner M (2010) The “meaningful use” regulation for electronic health records N Engl J Med 363:501–504 26 Graham KC, Cvach M (2010) Monitor alarm fatigue: standardizing use of physiological monitors and decreasing nuisance alarms Am J Crit Care 19:28–34 (quiz 35) 27 Faden R, Kass N, Whicher D, Stewart W, Tunis S (2013) Ethics and informed consent for comparative effectiveness research with prospective electronic clinical data Med Care 51:S53– S57 28 Fleurence RL, Beal AC, Sheridan SE, Johnson LB, Selby JV (2014) Patient-powered research networks aim to improve patient care and health research Health Affairs 33:1212–1219 29 Murphy SN, Weber G, Mendis M et al (2010) Serving the enterprise and beyond with informatics for integrating biology and the bedside (i2b2) J Am Med Inform Assoc 17:124–130 30 McMurry AJ, Murphy SN, MacFadden D et al (2013) SHRINE: enabling nationally scalable multi-site disease studies PLoS ONE 8:e55811 31 Faden RR, Beauchamp TL, Kass NE (2014) Informed consent, comparative effectiveness, and learning health care N Engl J Med 370:766–768 32 Kim SYH, Miller FG (2014) Informed consent for pragmatic trials – the integrated consent model N Engl J Med 370:769–772 33 Zimmerman JE, Kramer AA (2014) A history of outcome prediction in the ICU Curr Opin Crit Care 20:550–556 34 Amin W, Tsui FR, Borromeo C et al (2014) PaTH: towards a learning health system in the Mid-Atlantic region J Am Med Inform Assoc 21:633–636 35 Pinsky MR, Dubrawski A (2014) Gleaning knowledge from data in the intensive care unit Am J Respir Crit Care Med 190:606–610 Index A abdominal aortic aneurysm (AAA) 522 – compartment syndrome (ACS) 447 Acanthamoeba polyphaga 56 acid-base equilibrium 353, 358 acidosis 211, 482 Acinetobacter 80 activated partial thromboplastin time (aPTT) 485 active limb mobilization (AML) 526 activin-like kinase 386 acute brain injury 148 – coronary syndrome 171 – kidney injury (AKI) 85, 365, 379, 391, 409 – kidney syndrome 366 – liver failure (ALF) 415, 425 – respiratory distress syndrome (ARDS) 4, 47, 147, 179, 207, 424 – respiratory failure 147, 179, 233 – on-chronic liver failure (ACLF) 411, 432 adenosine diphosphate 485 – triphosphate (ATP) 167, 316, 395, 489 adenovirus 42, 48 advanced life support (ALS) 155, 160 aerobic exercise 526 AKI see acute kidney injury albumin 408, 431, 436, 439 albuminuria 371 Alport’s syndrome 382 amikacin 87 amino acid solution 467 aminoglycoside 74, 87 ammonia 357, 417, 422, 423 angiotensin II 129 angiotensinogen 129 antibiotics 63 antifibrinolytic therapy 472 antigen-presenting molecules 514 APACHE Outcomes database 575 apical ballooning syndrome 149 applanation tonometry 255 arboviral encephalitis 50 ARDS see acute respiratory distress syndrome arenavirus 43 arginine vasopressin (AVP) 410 arterial pressure monitoring 255 asphyxia 315 astrovirus 44 avibactam 72 B bacterial infection 431, 440 bag-valve-mask (BVM) 156 baroreceptor reflex 334 beta-adrenoceptor antagonist 143 – blocker 123, 139, 145 – lactamase inhibitor 73 biaryl oxazolidinone 76 Big Data 573, 574, 579, 581, 582, 595 bilevel positive airway pressure (BiPAP) bioimpedance 251 biomarker 365, 367, 370, 375 bioreactance 251 blood gas analysis 419 – transfusion 481 bloodstream infection (BSI) 15 blunt trauma 475 B lymphocytes 40 body protein status 462 © Springer International Publishing Switzerland 2015 J.-L Vincent (ed.), Annual Update in Intensive Care and Emergency Medicine 2015, DOI 10.1007/978-3-319-13761-2 235 597 598 bone morphogenetic protein 379, 382 brainstem herniation 417 Bunyaviridae 43, 53 burden of disease 591 Index (BMP-7) C cadazolid 77 calcium 333 caliciviridae 44 calpain 316 cannula thrombosis 215 capillary refill 344 carbapenem 73 carbavance 73 carbon dioxide (CO2 ) pressure 259 – dioxide removal 210 carboxypeptidase 130 cardiac arrest 174, 289, 290, 316, 550 – cycle 333 – output 334 cardiopulmonary bypass (CPB) 474 – exercise testing (CPET) 521 – resuscitation (CPR) 157, 281, 289, 316, 539 catecholamine 117, 119, 143, 520 catheter 15 – related bloodstream infection (CRBI) 92 CDI see Clostridium difficile infection cefotaxime 440 cell culture 45 central line-associated bloodstream infections (CLABSI) 15, 20 – nervous system (CNS) 273, 417 – venous catheter (CVC) 15 – venous pressure (CVP) 360, 424 cephalosporin 71 cerebral blood flow 308 – blood volume index (CBVI) 275 – edema 417, 420 – glucose metabolism 497 – oximetry 273, 274, 281 – perfusion pressure (CPP) 284 – tissue oxygen saturation 274 Chikungunya 50 chronic kidney disease (CKD) 371, 379, 384, 391 – obstructive pulmonary disease (COPD) 4, 123, 146, 235, 523 cirrhosis see also liver cirrhosis 405, 434 Clinical Pulmonary Infection Score (CPIS) Clostridium difficile infection (CDI) 25, 32, 560 clot formation 472, 473 CO2 see carbon dioxide coagulation factors 474 coagulopathy 418, 482, 484 Code Blue team 538 colectomy 32 colistin 85 colloid fluid 339 Coma Recovery Scale-Revised (CRS-R) 496 communication 563 community-acquired infection 46 compensatory anti-inflammatory response syndrome (CARS) 435 computerized physician order entry (CPOE) 592 consciousness 496, 502 continuous positive airway pressure (CPAP) 235 – renal replacement therapy (CRRT) 85, 320, 422 – venovenous hemofiltration (CVVH) 86 cooling techniques 317 COPD see chronic obstructive pulmonary disease coronary artery bypass graft (CABG) 523 coronaviridae 44 cor pulmonale 207, 209 corticosteroids 315 Creutzfeldt-Jakob disease 39 Crimean-Congo hemorrhagic fever 53 critical illness myopathy 510 crowdsourcing 580 CRRT see continuous renal replacement therapy crystalloid fluid 339 CVVH see continuous venovenous hemofiltration cystatin-C (Cys-C) 367, 368 cytokine 435, 513 cytomegalovirus (CMV) 39, 55 cytopathic effect (CPE) 45 D dalbavancin 78 damage associated molecular pattern (DAMP) 106 data mining 577 deep brain stimulation 496 delafloxacin 75 Dengue hemorrhagic fever 52 dermatomyositis 512 diarrhea 26 diuretics 366 Index dobutamine 117, 262 drug-induced nephropathy 599 386 E Ebola virus 38, 50 ECMO see extracorporeal membrane oxygenation effective circulating blood volume (ECBV) 360 electrical muscle stimulation (EMS) 525 electroencephalography (EEG) 498 electrolyte management 418 electronic medical record (EMR) 561, 573, 588, 592 electrons 330 Emergency Medical Services (EMS) 155, 282 encephalitis 49 encephalopathy 417, 426 endothelial injury 395 endothelin-1 (ET-1) 396 endotracheal aspirate (ETA) – intubation 155, 158, 162 – tube (ETT) 156 enterobacteriaceae 74 epidural hematoma 278 epilepsy 503 epinephrine 290, 294, 310 ESKAPE pathogens 63 esmolol 115, 123, 126, 145 estimated glomerular filtration rate (eGFR) 392 ethanol extended spectrum beta-lactamase (ESBL) 71 extracorporeal CO2 removal (ECCO2 R) 208, 213 – gas exchange 179, 182 – membrane oxygenation (ECMO) 4, 179, 190, 195, 210 F feeding 464 fibrinogen 473, 474 fibrosis 379 Fick equation 265, 267 fidaxomicin 29 filoviridae 43 fistula 454 flaviviridae 44 fresh frozen plasma (FFP) 473 frontoparietal cortex 498 functional magnetic resonance imaging (fMRI) 497 G galectin-3 397 ganciclovir 55 gelatinase 367 gene transcription 380 gentamicin 88 Glasgow Coma Scale (GCS) score 148 glomerular filtration rate (GFR) 354, 365, 405 glottic stenosis 219 glutamine 357 glycocalyx 342 glycopeptide 77 Gram-stain 3, H hantavirus 38, 48 Hawthorne phenomenon 19 hemodiafiltration 422 hemodynamic monitoring 249 – optimization 256 hemoglobin 168, 274, 275, 473 hemorrhage 226, 343 hemostatic treatment 472 Henle’s loop 356 hepadna virus 42 hepatic encephalopathy 411 hepatitis B virus (HBV) 38 hepatorenal syndrome (HRS) 408, 432 heptapeptide 130 herpes simplex encephalitis (HSE) 48 herpesvirus 38, 42 homeostasis 351 human albumin see albumin – immunodeficiency virus (HIV) 38, 71 hydrocephalus 277 hyperammonemia 417 hyperbaric oxygenation 172 hypercapnia 172, 208, 209, 211 hypercarbia 421 hypercoagulability 485 hyperfibrinolysis 472 hyperglycemia 107 hyperlactatemia 265 hypernatremia 422 hyperoxia 167, 174, 306, 308 hyperventilation 162, 421 hypoalbuminemia 27, 438 hypocarbia 308 hypofibrinogenemia 426, 472 hypoglycemia 419, 427 hypoperfusion 511 hypothermia 303, 315, 423, 482 hypovolemia 120, 366, 417, 519 600 Index hypoxemia 174, 519 hypoxia 306, 308, 398, 511 hypoxia-inducible transcription factor (HIF) 398 I ICU-acquired weakness 509 ileostomy 32 immunodepression 109 immunoglobulin 110 immunoparalysis 109 inclusion bodies 45 infection 25, 39, 46 inflammasome 107 influenza 37, 47 insulin-like growth factor binding-protein (IGFBP-7) 370 intercellular adhesion molecules (ICAMs) 132, 514 interleukin 18 (IL-18) 367, 370 International Normalized Ratio (INR) 475 intraabdominal pressure (IAP) 449 intracranial hypertension 417, 420 intravascular cooling systems 319 intravenous immunoglobulin (IVIG) 515 ischemic stroke 173 isoflurane anesthesia 345 ivabradine 123, 125 J Japanese encephalitis virus 50 jugular venous oxygen saturation (SjvO2 ) 276 juxtaglomerular cell 130 K kidney damage 366, 373 – injury molecule-1 (KIM-1) Klebsiella 80 367, 368 L lactic acidosis 415 Lean Six Sigma 21, 567 lidoflazine 315 lipoglycopeptides 77 liver cirrhosis 405 – transplantation 415, 427 – type fatty acid binding protein (L-FABP) 367 M mannitol 422 Marburg virus 39, 50 massive bleeding 471 matrix-assisted laser desorption ionization time-of-fly (MALDI-TOF) mean arterial pressure (MAP) 342, 407 – circulatory filling pressure (MCFP) 328 – systemic filling pressure (MSFP) 328 mechanical ventilation 4, 179, 207, 211, 233 medical emergency team (MET) 533, 537, 547 metabolic acidosis 346, 359 metronidazole 26, 29 minimally conscious state (MCS) 495 mitochondrial dysregulation 395 mixed venous O2 saturation (SvO2 ) 259 mobilization 526 model for end-stage liver disease (MELD) 405 MODS see multiple organ dysfunction syndrome monocyte chemoattractant protein-1 (MCP-1) 382 Muckle–Wells syndrome 109 multidrug-resistant (MDR) bacteria 63, 105 multiple organ dysfunction syndrome (MODS) 125, 447, 510 myokines 512 N N-acetyl-ˇ-D-glucosaminidase (NAG) 367, 370 N-acetylcysteine (NAC) 420 natural killer (NK) lymphocytes 39 near-infrared spectroscopy (NIRS) 273 negative pressure wound therapy (NPWT) 450 neuroimaging 497, 501 neuroprotection 315 neutrophil gelatinase-associated lipocalin (NGAL) 367 nimodipine 315 nitric oxide (NO) 519 nitrogen balance 459 non-steroidal anti-inflammatory drugs (NSAIDs) 408 norepinephrine 116, 126, 134, 146 normobaric hyperoxia 172 normocapnia 309 normoxemia 174 nosocomial infection 3, 39 Index 601 nucleic acid 46 nutrition 459 O obstructive sleep apnea 219 octapeptide 130 open abdomen management 447 oritavancin 78 orthomyxoviridae 47 out-of-hospital cardiac arrest (OHCA) oxazolidinone 76 oxygenation 306 oxygen radicals 316 – toxicity 167 289 P papovavirus 42 paracetamol 416 parainfluenza virus 47 Paramyxoviridae 47 parenteral nutrition 462 parvoviridae 43 pathogen-associated molecular pattern (PAMP) 110 PCO2 gap 265 peripheral perfusion index (PPI) 273 – vascular resistance (PVR) 435 peritonitis 515 phenylalanine 466 photoplethysmography 252 picornavirus 43 plan-do-study-act (PDSA) 22 PlasmaLyte 346 platelet dysfunction 473 – transfusion 474 plazomicin 74 plethysmography 252 – variability index (PVI) 252 pneumonectomy 199 pneumonia 46 point-of-care coagulation monitoring 471 polymerase chain reaction (PCR) – PCR/electrospray ionization MS (PCR/ESI-MS) polymyositis 512 positive end-expiratory pressure (PEEP) 169, 189, 261 positron emission tomography with fluorodeoxyglucose (FDG-PET) 497 post-traumatic stress syndrome (PTSD) 195 poxvirus 41 pre-renal azotemia 373 propranolol 146 protected specimen brush (PSB) protein intake 459, 465 proteinuria 393, 398 prothrombin complex concentrate (PCC) 474 proton pump inhibitors (PPI) 27 pseudomembranous colitis 25 Pseudomonas 80 pulmonary artery catheter (PAC) 249, 259 – occlusion pressure (PAOP) 360 – edema 330 – vasoconstriction 209 – wave transit time 251 pulse oximetry 275, 276, 535 pump-assisted lung protection (PALP) 211 Q quinolone 75 R radezolid (RX-1741) 76 rapid response systems 537, 540, 553 – response team 564 reactive oxygen species (ROS) 117, 395, 435 red blood cells (RBCs) 476 reduced capillary density 395 refractory hypercapnia 215 – hypoxemia 200 regional cerebral tissue oxygen saturation (rcSO2 ) 274 renal ammonium 357 – cell apoptosis 383 – dysfunction 410 – failure 27, 418 – fibrosis 384 – potassium handling 356 – sodium 353 – support 426 renin-angiotensin system (RAS) 129, 133 – aldosterone system 130, 360 reoviridae 43 respiratory acidosis 208 – alkalosis 358 – monitoring 251 – syncytial virus 47 retroviridae 44 return of spontaneous circulation (ROSC) 281 rewarming 487 rhabdoviridae 43 Ringer‘s solution 346 602 S sepsis 37, 106, 145, 418, 565 septic myocardial dysfunction 118 – shock 115, 118, 120, 145, 424, 557 sequential organ failure assessment (SOFA) 28, 411 serum amyloid A 513 – creatinine 366, 393, 405, 409 SIRS see systemic inflammatory response syndrome spectrin 316 spontaneous bacterial peritonitis 440 Staphylococcus aureus 4, 12 stoma 222 stomal cellulitis 226 stroke 172, 317 – volume 334 subarachnoid bleeding 278 surface cooling systems 318 surgical stress 520, 526 Surviving Sepsis Campaign (SSC) 557 sympathetic nervous system (SNS) 410 sympathomimetics 143 systemic inflammatory response syndrome (SIRS) 510 – ischemic-reperfusion syndrome 306 T tachyarrhythmia 117 tachycardia 115, 119, 123 Takotsubo syndrome 149 targeted temperature management (TTM) 303, 315, 317 (see therapeutic hypothermia) tazobactam 71 telavancin 77 temporary abdominal closure (TAC) 447, 449 terlipressin 408 tetracycline 79 therapeutic hypothermia 308 thiopental 315 thrombocytopenia 426, 473 thromboelastography 472, 485 thromboelastometry 472 tigecycline 31 Index tissue inhibitor of metalloproteinase-2 (TIMP-2) 370 T lymphocytes 40 togaviridae 44 torezolid 76 tracheostomy 219, 224 tranexamic acid 472 transcranial magnetic stimulation (TMS) 501 transforming growth factor-beta (TGF-ˇ) 379, 396 trauma 106, 483, 490 – induced coagulopathy 474 traumatic brain injury (TBI) 148, 162, 172, 282, 283, 317, 495 tubulo-interstitial fibrosis 395, 398 U unresponsive wakefulness syndrome (UWS) 495 urinary electrolytes 352 urine albumin-to-creatinine ratio 394 V vancomycin 26, 29 – resistant enterococci (VRE) 98 VAP see ventilator-associated pneumonia vascular cell adhesion molecules (VCAMs) 132, 514 – endothelial growth factor (VEGF) 132 vasodilatory shock 417, 424 vasopressin 294, 298, 310 ventilation 335 ventilator-associated pneumonia (VAP) 3, 10, 54, 219, 227 viral hemorrhagic fever 50 vocal cord paralysis 219 volume kinetics 340, 341 voriconazole 88 W warming blanket 487 Z Zero Resistance program 91 ... Intensive Care Medicine in Europe and Intensive Care Medicine: Annual Update in the United States Jean-Louis Vincent Editor Annual Update in Intensive Care and Emergency Medicine 2015 Editor.. .Annual Update in Intensive Care and Emergency Medicine 2015 The series Annual Update in Intensive Care and Emergency Medicine is the continuation of the series entitled Yearbook of Intensive. .. Pulmonary and Critical Care Medicine, Thorax Institute, Hospital Clinic, Barcelona, Spain e-mail: atorres@clinic.ub.es © Springer International Publishing Switzerland 2015 J.-L Vincent (ed.), Annual Update