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Purpose The heterogeneity in sepsis is held responsible, in part, for the lack of precision treatment. Many attempts to identify subtypes of sepsis patients identify those with shared underlying biology or outcomes. To date, though, there has been limited effort to determine overlap across these previously identified subtypes. We aimed to determine the concordance of critically ill patients with sepsis classified by four previously described subtype strategies. Methods This secondary analysis of a multicenter prospective observational study included 522 critically ill patients with sepsis assigned to four previously established subtype strategies, primarily based on: (i) clinical data in the electronic health record ([alpha], [beta], [gamma], and [delta]), (ii) biomarker data (hyper- and hypoinflammatory), and (iii-iv) transcriptomic data (Mars1-Mars4 and SRS1-SRS2). Concordance was studied between different subtype labels, clinical characteristics, biological host response aberrations, as well as combinations of subtypes by sepsis ensembles. Results All four subtype labels could be adjudicated in this cohort, with the distribution of the clinical subtype varying most from the original cohort. The most common subtypes in each of the four strategies were [gamma] (61%), which is higher compared to the original classification, hypoinflammatory (60%), Mars2 (35%), and SRS2 (54%). There was no clear relationship between any of the subtyping approaches (Cramer's V = 0.086-0.456). Mars2 and SRS1 were most alike in terms of host response biomarkers (p = 0.079-0.424), while other subtype strategies showed no clear relationship. Patients enriched for multiple subtypes revealed that characteristics and outcomes differ dependent on the combination of subtypes made. Conclusion Among critically ill patients with sepsis, subtype strategies using clinical, biomarker, and transcriptomic data do not identify comparable patient populations and are likely to reflect disparate clinical characteristics and underlying biology., Author(s): Rombout B. E. van Amstel [sup.1] [sup.2], Jason N. Kennedy [sup.3], Brendon P. Scicluna [sup.4] [sup.5], Lieuwe D. J. Bos [sup.1] [sup.2], Hessel Peters-Sengers [sup.6] [sup.7], Joe M. Butler [...]

Purpose Severe community-acquired pneumonia (sCAP) is associated with high morbidity and mortality, and whilst European and non-European guidelines are available for community-acquired pneumonia, there are no specific guidelines for sCAP. Methods The European Respiratory Society (ERS), European Society of Intensive Care Medicine (ESICM), European Society of Clinical Microbiology and Infectious Diseases (ESCMID), and Latin American Thoracic Association (ALAT) launched a task force to develop the first international guidelines for sCAP. The panel comprised a total of 18 European and four non-European experts, as well as two methodologists. Eight clinical questions for sCAP diagnosis and treatment were chosen to be addressed. Systematic literature searches were performed in several databases. Meta-analyses were performed for evidence synthesis, whenever possible. The quality of evidence was assessed with GRADE (Grading of Recommendations, Assessment, Development and Evaluation). Evidence to Decision frameworks were used to decide on the direction and strength of recommendations. Results Recommendations issued were related to diagnosis, antibiotics, organ support, biomarkers and co-adjuvant therapy. After considering the confidence in effect estimates, the importance of outcomes studied, desirable and undesirable consequences of treatment, cost, feasibility, acceptability of the intervention and implications to health equity, recommendations were made for or against specific treatment interventions. Conclusions In these international guidelines, ERS, ESICM, ESCMID, and ALAT provide evidence-based clinical practice recommendations for diagnosis, empirical treatment, and antibiotic therapy for sCAP, following the GRADE approach. Furthermore, current knowledge gaps have been highlighted and recommendations for future research have been made., Author(s): Ignacio Martin-Loeches [sup.1] [sup.2] [sup.3] [sup.4], Antoni Torres [sup.3] [sup.4], Blin Nagavci [sup.5], Stefano Aliberti [sup.6] [sup.7], Massimo Antonelli [sup.8], Matteo Bassetti [sup.9], Lieuwe D. Bos [sup.10], James D. [...]

Author(s): Malou Janssen [sup.1] [sup.2], Henrik Endeman [sup.2], Lieuwe D. J. Bos [sup.3] [sup.4] Author Affiliations: (1) grid.415868.6, 0000 0004 0624 5690, Internal Medicine, Reinier de Graaf Gasthuis, , Delft, [...]

Author(s): Andrew Conway Morris [sup.1] [sup.2] [sup.3], Lieuwe D. J. Bos [sup.4], Saad Nseir [sup.5] [sup.6] Author Affiliations: (1) grid.5335.0, 0000000121885934, Division of Immunology, Department of Pathology, University of Cambridge, [...]

Although the acute respiratory distress syndrome (ARDS) is well defined by the development of acute hypoxemia, bilateral infiltrates and non-cardiogenic pulmonary edema, ARDS is heterogeneous in terms of clinical risk factors, physiology of lung injury, microbiology, and biology, potentially explaining why pharmacologic therapies have been mostly unsuccessful in treating ARDS. Identifying phenotypes of ARDS and integrating this information into patient selection for clinical trials may increase the chance for efficacy with new treatments. In this review, we focus on classifying ARDS by the associated clinical disorders, physiological data, and radiographic imaging. We consider biologic phenotypes, including plasma protein biomarkers, gene expression, and common causative microbiologic pathogens. We will also discuss the issue of focusing clinical trials on the patient's phase of lung injury, including prevention, administration of therapy during early acute lung injury, and treatment of established ARDS. A more in depth understanding of the interplay of these variables in ARDS should provide more success in designing and conducting clinical trials and achieving the goal of personalized medicine., Author(s): Michael A. Matthay [sup.1] [sup.2] [sup.3], Yaseen M. Arabi [sup.4], Emily R. Siegel [sup.2], Lorraine B. Ware [sup.5], Lieuwe D. J. Bos [sup.6], Pratik Sinha [sup.7], Jeremy R. Beitler [...]

Author(s): Ignacio Martin-Loeches [sup.1] [sup.2] [sup.3] [sup.4], Antoni Torres [sup.3] [sup.4], Blin Nagavci [sup.5], Stefano Aliberti [sup.6] [sup.7], Massimo Antonelli [sup.8], Matteo Bassetti [sup.9], Lieuwe D. Bos [sup.10], James D. [...]

Purpose Mechanical power (MP) may unify variables known to be related to development of ventilator-induced lung injury. The aim of this study is to examine the association between MP and mortality in critically ill patients receiving invasive ventilation for at least 48 h. Methods This is an analysis of data stored in the databases of the MIMIC-III and eICU. Critically ill patients receiving invasive ventilation for at least 48 h were included. The exposure of interest was MP. The primary outcome was in-hospital mortality. Results Data from 8207 patients were analyzed. Median MP during the second 24 h was 21.4 (16.2-28.1) J/min in MIMIC-III and 16.0 (11.7-22.1) J/min in eICU. MP was independently associated with in-hospital mortality [odds ratio per 5 J/min increase (OR) 1.06 (95% confidence interval (CI) 1.01-1.11); p = 0.021 in MIMIC-III, and 1.10 (1.02-1.18); p = 0.010 in eICU]. MP was also associated with ICU mortality, 30-day mortality, and with ventilator-free days, ICU and hospital length of stay. Even at low tidal volume, high MP was associated with in-hospital mortality [OR 1.70 (1.32-2.18); p < 0.001] and other secondary outcomes. Finally, there is a consistent increase in the risk of death with MP higher than 17.0 J/min. Conclusion High MP of ventilation is independently associated with higher in-hospital mortality and several other outcomes in ICU patients receiving invasive ventilation for at least 48 h., Author(s): Ary Serpa Neto [sup.1] [sup.2] [sup.3], Rodrigo Octavio Deliberato [sup.2] [sup.3] [sup.4], Alistair E. W. Johnson [sup.5], Lieuwe D. Bos [sup.1], Pedro Amorim [sup.6], Silvio Moreto Pereira [sup.6], Denise [...]

Purpose To evaluate patients with resolved versus confirmed ARDS, identify subgroups with substantial mortality risk, and to determine the utility of day 2 ARDS reclassification. Methods Our primary objective, in this secondary LUNG SAFE analysis, was to compare outcome in patients with resolved versus confirmed ARDS after 24 h. Secondary objectives included identifying factors associated with ARDS persistence and mortality, and the utility of day 2 ARDS reclassification. Results Of 2377 patients fulfilling the ARDS definition on the first day of ARDS (day 1) and receiving invasive mechanical ventilation, 503 (24%) no longer fulfilled the ARDS definition the next day, 52% of whom initially had moderate or severe ARDS. Higher tidal volume on day 1 of ARDS was associated with confirmed ARDS [OR 1.07 (CI 1.01-1.13), P = 0.035]. Hospital mortality was 38% overall, ranging from 31% in resolved ARDS to 41% in confirmed ARDS, and 57% in confirmed severe ARDS at day 2. In both resolved and confirmed ARDS, age, non-respiratory SOFA score, lower PEEP and P/F ratio, higher peak pressure and respiratory rate were each associated with mortality. In confirmed ARDS, pH and the presence of immunosuppression or neoplasm were also associated with mortality. The increase in area under the receiver operating curve for ARDS reclassification on day 2 was marginal. Conclusions ARDS, whether resolved or confirmed at day 2, has a high mortality rate. ARDS reclassification at day 2 has limited predictive value for mortality. The substantial mortality risk in severe confirmed ARDS suggests that complex interventions might best be tested in this population. Trial Registration ClinicalTrials.gov NCT02010073., Author(s): Fabiana Madotto [sup.1] [sup.2], Tài Pham [sup.2] [sup.3] [sup.4], Giacomo Bellani [sup.5] [sup.6], Lieuwe D. Bos [sup.7], Fabienne D. Simonis [sup.7], Eddy Fan [sup.8] [sup.9], Antonio Artigas [sup.10], Laurent [...]

Author(s): Bastiaan V. C. Kraaijenbrink [sup.1], Amne Mousa [sup.1] [sup.2], Lieuwe D. Bos [sup.3], Frederique Paulus [sup.3], Pieter R. Tuinman [sup.1] [sup.2] Author Affiliations: (1) grid.509540.d, 0000 0004 6880 3010, [...]

Author(s): Lieuwe D. J. Bos [sup.1] [sup.2], Andre C. Kalil [sup.3] Author Affiliations: (1) Intensive Care, Infection and Immunity, Amsterdam UMC -Location AMC, , Amsterdam, The Netherlands (2) Department of [...]

Author(s): Lieuwe D. J. Bos [sup.1], Elie Azoulay [sup.2], Ignacio Martin-Loeches [sup.3] [sup.4] Author Affiliations: (1) 0000 0004 0617 8280, grid.416409.e, St James's Hospital, , James's St N, Ushers, D03 [...]

Background Co-infection is frequently seen in critically ill patients with influenza, although the exact rate is unknown. We determined the rate of co-infection, the risk factors and the outcomes associated with co-infection in critically ill patients with influenza over a 7-year period in 148 Spanish intensive care units (ICUs). Methods This was a prospective, observational, multicentre study. Influenza was diagnosed using the polymerase chain reaction. Co-infection had to be confirmed using standard bacteriological tests. The primary endpoint of this analysis was the presence of community-acquired co-infection, with secondary endpoints including ICU, 28-day and hospital mortality. Results Of 2901 ICU patients diagnosed with influenza, 482 (16.6 %) had a co-infection. The proportion of cases of co-infection increased from 11.4 % (110/968) in 2009 to 23.4 % (80/342) in 2015 (P < 0.001). Compared with patients without co-infection, patients with co-infection were older [adjusted odds ratio (aOR) 1.1, 95 % confidence interval 1.1-1.2; P < 0.001] and were more frequently immunosuppressed due to existing HIV infection (aOR 2.6 [1.5-4.5]; P < 0.001) or preceding medication (aOR 1.4 [1.1-1.9]; P = 0.03). Co-infection was an independent risk factor for ICU mortality (aOR 1.4 [1.1-1.8]; P < 0.02), 28-day mortality (aOR 1.3 [1.1-1.7]; P = 0.04) and hospital mortality (aOR 1.9 [1.5-2.5]; P < 0.001). Conclusions Co-infection in critically ill patients with influenza has increased in recent years. In this Spanish cohort, age and immunosuppression were risk factors for co-infection, and co-infection was an independent risk factor for ICU, 28-day and hospital mortality., Author(s): Ignacio Martin-Loeches [sup.1] [sup.2], Marcus J Schultz [sup.3], Jean-Louis Vincent [sup.4], Francisco Alvarez-Lerma [sup.5], Lieuwe D. Bos [sup.3], Jordi Solé-Violán [sup.6], Antoni Torres [sup.7], Alejandro Rodriguez [sup.8] [sup.9] Author [...]

Author(s): Lieuwe D. Bos [sup.1] [sup.2] [sup.3], Cheryl Stips [sup.1], Laura R. Schouten [sup.1], Lonneke A. van Vught [sup.5], Maryse A. Wiewel [sup.4] [sup.5], Luuk Wieske [sup.1], Roosmarijn T. van [...]

Author(s): Lorenzo Ball [sup.1], Lieuwe D. Bos [sup.2], Paolo Pelosi [sup.1] Author Affiliations: (1) grid.5606.5, 0000000121513065, IRCCS AOU San Martino-IST, Department of Surgical Sciences and Integrated Diagnostics, University of Genoa, [...]

Author(s): Lieuwe D. J. Bos [sup.1] [sup.2] [sup.3] [sup.4], Ignacio Martin-Loeches [sup.4], Antonio Artigas [sup.4] Author Affiliations: (1) grid.7177.6, 0000000084992262, Department of Intensive Care, Academic Medical Center, University of Amsterdam, [...]

Author(s): Ignacio Martin-Loeches [sup.1] [sup.2] [sup.5], Lieuwe D. Bos [sup.3], Ewan C. Goligher [sup.4] Author Affiliations: (1) Multidisciplinary Intensive Care, St. James's University Hospital, , 8, Dublin, Ireland (2) 0000 [...]

Author(s): Lieuwe D. Bos [sup.1], Olaf L. Cremer [sup.2], David S. Y. Ong [sup.2] [sup.3], Eliana B. Caser [sup.8], Carmen S. V. Barbas [sup.9] [sup.10], Jesus Villar [sup.4] [sup.5], Robert [...]

Author(s): Lieuwe D. J. Bos [sup.1] [sup.2] [sup.3] [sup.4] [sup.5], Ignacio Martin-Loeches [sup.5], Janine B. Kastelijn [sup.1] [sup.5], Gisela Gili [sup.5], Mateu Espasa [sup.6], Pedro Povoa [sup.7], Arend H. J. [...]