Erratum to: 36th International Symposium on Intensive Care and Emergency Medicine

[This corrects the article DOI: 10.1186/s13054-016-1208-6.]

Integrated Decision Support Tools for Disruption Management

During railway operations unexpected events can require railway operators and infrastructure managers to adjust their schedules. In this research we investigate the disruption management process. More specifically, we come up with an architecture and algorithmic framework which railway operators could use for decision support during disruptions. The use of this framework results in a fully feasible timetable, rolling stock plan, and crew schedule to deal with the disruption, while minimizing the number of delayed and/or (partially) cancelled trains. We demonstrate the effectiveness of our framework on a disruption case on the Dutch Railway network, which is introduced within the EU FP7 project ON-TIME.Transport & PlanningCivil Engineering and Geoscience

Search for anomaly-mediated supersymmetry breaking with the ATLAS detector based on a disappearing-track signature in pp collisions at sqrt(s) = 7TeV$

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Jussel;A. Juste Rozas;V. V. Kabachenko;S. Kabana;M. Kaci;A. Kaczmarska;P. Kadlecik;M. Kado;H. Kagan;M. Kagan;S. Kaiser;E. Kajomovitz;S. Kalinin;L. V. Kalinovskaya;S. Kama;N. Kanaya;M. Kaneda;T. Kanno;V. A. Kantserov;J. Kanzaki;B. Kaplan;A. Kapliy;J. Kaplon;D. Kar;M. Karagounis;M. Karagoz;M. Karnevskiy;K. Karr;V. Kartvelishvili;A. N. Karyukhin;L. Kashif;G. Kasieczka;R. D. Kass;A. Kastanas;M. Kataoka;Y. Kataoka;E. Katsoufis;J. Katzy;V. Kaushik;K. Kawagoe;T. Kawamoto;G. Kawamura;M. S. Kayl;V. A. Kazanin;M. Y. Kazarinov;J. R. Keates;R. Keeler;R. Kehoe;M. Keil;G. D. Kekelidze;J. Kennedy;C. J. Kenney;M. Kenyon;O. Kepka;N. Kerschen;B. P. Kerševan;S. Kersten;K. Kessoku;J. Keung;F. Khalil-zada;H. Khandanyan;A. Khanov;D. Kharchenko;A. Khodinov;A. G. Kholodenko;A. Khomich;T. J. Khoo;G. Khoriauli;A. Khoroshilov;N. Khovanskiy;V. Khovanskiy;E. Khramov;J. Khubua;H. Kim;M. S. Kim;P. C. Kim;S. H. Kim;N. Kimura;O. Kind;B. T. King;M. King;R. S. B. King;J. Kirk;L. E. Kirsch;A. E. Kiryunin;T. Kishimoto;D. 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Lee;S. C. Lee;L. Lee;M. Lefebvre;M. Legendre;A. Leger;B. C. LeGeyt;F. Legger;C. Leggett;M. Lehmacher;G. Lehmann Miotto;X. Lei;M. A. L. Leite;R. Leitner;D. Lellouch;M. Leltchouk;B. Lemmer;V. Lendermann;K. J. C. Leney;T. Lenz;G. Lenzen;B. Lenzi;K. Leonhardt;S. Leontsinis;C. Leroy;J-R. Lessard;J. Lesser;C. G. Lester;A. Leung Fook Cheong;J. Levêque;D. Levin;L. J. Levinson;M. S. Levitski;A. Lewis;G. H. Lewis;A. M. Leyko;M. Leyton;B. Li;H. Li;S. Li;X. Li;Z. Liang;H. Liao;B. Liberti;P. Lichard;M. Lichtnecker;K. Lie;W. Liebig;R. Lifshitz;C. Limbach;A. Limosani;M. Limper;S. C. Lin;F. Linde;J. T. Linnemann;E. Lipeles;L. Lipinsky;A. Lipniacka;T. M. Liss;D. Lissauer;A. Lister;A. M. Litke;C. Liu;D. Liu;H. Liu;J. B. Liu;M. Liu;S. Liu;Y. Liu;M. Livan;S. S. A. Livermore;A. Lleres;J. Llorente Merino;S. L. Lloyd;E. Lobodzinska;P. Loch;W. S. Lockman;T. Loddenkoetter;F. K. Loebinger;A. Loginov;C. W. Loh;T. Lohse;K. Lohwasser;M. Lokajicek;J. Loken;V. P. Lombardo;R. E. Long;L. Lopes;D. Lopez Mateos;J. 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Marchiori;M. Marcisovsky;A. Marin;C. P. Marino;F. Marroquim;R. Marshall;Z. Marshall;F. K. Martens;S. Marti-Garcia;A. J. Martin;B. Martin;B. Martin;F. F. Martin;J. P. Martin;Ph. Martin;T. A. Martin;V. J. Martin;B. Ma

Epidemiology of intra-abdominal infection and sepsis in critically ill patients: "AbSeS", a multinational observational cohort study and ESICM Trials Group Project

PurposeTo describe the epidemiology of intra-abdominal infection in an international cohort of ICU patients according to a new system that classifies cases according to setting of infection acquisition (community-acquired, early onset hospital-acquired, and late-onset hospital-acquired), anatomical disruption (absent or present with localized or diffuse peritonitis), and severity of disease expression (infection, sepsis, and septic shock).MethodsWe performed a multicenter (n=309), observational, epidemiological study including adult ICU patients diagnosed with intra-abdominal infection. Risk factors for mortality were assessed by logistic regression analysis.ResultsThe cohort included 2621 patients. Setting of infection acquisition was community-acquired in 31.6%, early onset hospital-acquired in 25%, and late-onset hospital-acquired in 43.4% of patients. Overall prevalence of antimicrobial resistance was 26.3% and difficult-to-treat resistant Gram-negative bacteria 4.3%, with great variation according to geographic region. No difference in prevalence of antimicrobial resistance was observed according to setting of infection acquisition. Overall mortality was 29.1%. Independent risk factors for mortality included late-onset hospital-acquired infection, diffuse peritonitis, sepsis, septic shock, older age, malnutrition, liver failure, congestive heart failure, antimicrobial resistance (either methicillin-resistant Staphylococcus aureus, vancomycin-resistant enterococci, extended-spectrum beta-lactamase-producing Gram-negative bacteria, or carbapenem-resistant Gram-negative bacteria) and source control failure evidenced by either the need for surgical revision or persistent inflammation.ConclusionThis multinational, heterogeneous cohort of ICU patients with intra-abdominal infection revealed that setting of infection acquisition, anatomical disruption, and severity of disease expression are disease-specific phenotypic characteristics associated with outcome, irrespective of the type of infection. Antimicrobial resistance is equally common in community-acquired as in hospital-acquired infection