Table of contentsP001 - Sepsis impairs the capillary response within hypoxic capillaries and decreases erythrocyte oxygen-dependent ATP effluxR. M. Bateman, M. D. Sharpe, J. E. Jagger, C. G. EllisP002 - Lower serum immunoglobulin G2 level does not predispose to severe flu.J. Solé-Violán, M. López-Rodríguez, E. Herrera-Ramos, J. Ruíz-Hernández, L. Borderías, J. Horcajada, N. González-Quevedo, O. Rajas, M. Briones, F. Rodríguez de Castro, C. Rodríguez GallegoP003 - Brain protective effects of intravenous immunoglobulin through inhibition of complement activation and apoptosis in a rat model of sepsisF. Esen, G. Orhun, P. Ergin Ozcan, E. Senturk, C. Ugur Yilmaz, N. Orhan, N. Arican, M. Kaya, M. Kucukerden, M. Giris, U. Akcan, S. Bilgic Gazioglu, E. TuzunP004 - Adenosine a1 receptor dysfunction is associated with leukopenia: A possible mechanism for sepsis-induced leukopeniaR. Riff, O. Naamani, A. DouvdevaniP005 - Analysis of neutrophil by hyper spectral imaging - A preliminary reportR. Takegawa, H. Yoshida, T. Hirose, N. Yamamoto, H. Hagiya, M. Ojima, Y. Akeda, O. Tasaki, K. Tomono, T. ShimazuP006 - Chemiluminescent intensity assessed by eaa predicts the incidence of postoperative infectious complications following gastrointestinal surgeryS. Ono, T. Kubo, S. Suda, T. Ueno, T. IkedaP007 - Serial change of c1 inhibitor in patients with sepsis – A prospective observational studyT. Hirose, H. Ogura, H. Takahashi, M. Ojima, J. Kang, Y. Nakamura, T. Kojima, T. ShimazuP008 - Comparison of bacteremia and sepsis on sepsis related biomarkersT. Ikeda, S. Suda, Y. Izutani, T. Ueno, S. OnoP009 - The changes of procalcitonin levels in critical patients with abdominal septic shock during blood purificationT. Taniguchi, M. OP010 - Validation of a new sensitive point of care device for rapid measurement of procalcitoninC. Dinter, J. Lotz, B. Eilers, C. Wissmann, R. LottP011 - Infection biomarkers in primary care patients with acute respiratory tract infections – Comparison of procalcitonin and C-reactive proteinM. M. Meili, P. S. SchuetzP012 - Do we need a lower procalcitonin cut off?H. Hawa, M. Sharshir, M. Aburageila, N. SalahuddinP013 - The predictive role of C-reactive protein and procalcitonin biomarkers in central nervous system infections with extensively drug resistant bacteriaV. Chantziara, S. Georgiou, A. Tsimogianni, P. Alexandropoulos, A. Vassi, F. Lagiou, M. Valta, G. Micha, E. Chinou, G. MichaloudisP014 - Changes in endotoxin activity assay and procalcitonin levels after direct hemoperfusion with polymyxin-b immobilized fiberA. Kodaira, T. Ikeda, S. Ono, T. Ueno, S. Suda, Y. Izutani, H. ImaizumiP015 - Diagnostic usefullness of combination biomarkers on ICU admissionM. V. De la Torre-Prados, A. Garcia-De la Torre, A. Enguix-Armada, A. Puerto-Morlan, V. Perez-Valero, A. Garcia-AlcantaraP016 - Platelet function analysis utilising the PFA-100 does not predict infection, bacteraemia, sepsis or outcome in critically ill patientsN. Bolton, J. Dudziak, S. Bonney, A. Tridente, P. NeeP017 - Extracellular histone H3 levels are in...

Background The clinical presentation of COVID-19 in patients admitted to hospital is heterogeneous. We aimed to determine whether clinical phenotypes of patients with COVID-19 can be derived from clinical data, to assess the reproducibility of these phenotypes and correlation with prognosis, and to derive and validate a simplified probabilistic model for phenotype assignment. Phenotype identification was not primarily intended as a predictive tool for mortality. MethodsIn this study, we used data from two cohorts: the COVID-19@Spain cohort, a retrospective cohort including 4035 consecutive adult patients admitted to 127 hospitals in Spain with COVID-19 between Feb 2 and March 17, 2020, and the COVID-19@HULP cohort, including 2226 consecutive adult patients admitted to a teaching hospital in Madrid between Feb 25 and April 19, 2020. The COVID-19@Spain cohort was divided into a derivation cohort, comprising 2667 randomly selected patients, and an internal validation cohort, comprising the remaining 1368 patients. The COVID-19@HULP cohort was used as an external validation cohort. A probabilistic model for phenotype assignment was derived in the derivation cohort using multinomial logistic regression and validated in the internal validation cohort. The model was also applied to the external validation cohort. 30-day mortality and other prognostic variables were assessed in the derived phenotypes and in the phenotypes assigned by the probabilistic model. Findings Three distinct phenotypes were derived in the derivation cohort (n=2667)-phenotype A (516 [19%] patients), phenotype B (1955 [73%]) and phenotype C (196 [7%])-and reproduced in the internal validation cohort (n=1368)phenotype A (233 [17%] patients), phenotype B (1019 [74%]), and phenotype C (116 [8%]). Patients with phenotype A were younger, were less frequently male, had mild viral symptoms, and had normal inflammatory parameters. Patients with phenotype B included more patients with obesity, lymphocytopenia, and moderately elevated inflammatory parameters. Patients with phenotype C included older patients with more comorbidities and even higher inflammatory parameters than phenotype B. We developed a simplified probabilistic model (validated in the internal validation cohort) for phenotype assignment, including 16 variables. In the derivation cohort, 30-day mortality rates were 2•5% (95% CI 1•4-4•3) for patients with phenotype A, 30•5% (28•5-32•6) for patients with phenotype B, and 60•7% (53•7-67•2) for patients with phenotype C (log-rank test p<0•0001). The predicted phenotypes in the internal validation cohort and external validation cohort showed similar mortality rates to the assigned phenotypes (internal validation cohort: 5•3% [95% CI 3•4-8•1] for phenotype A, 31•3% [28•5-34•2] for phenotype B, and 59•5% [48•8-69•3] for phenotype C; external validation cohort: 3•7% [2•0-6•4] for phenotype A, 23•7% [21•8-25•7] for phenotype B, and 51•4% [41•9-60•7] for phenotype C).Interpretation Patients admitted to hospital with COVID-19 can be classified into three...

Summary We conducted a multicentre study of 1844 patients from 42 Spanish intensive care units, and analysed the clinical characteristics of brain death, the use of ancillary testing, and the clinical decisions taken after the diagnosis of brain death. The main cause of brain death was intracerebral haemorrhage (769/1844, 42%), followed by traumatic brain injury (343/1844, 19%) and subarachnoid haemorrhage (257/1844, 14%). The diagnosis of brain death was made rapidly (50% in the first 24 h). Of those patients who went on to die, the Glasgow Coma Scale on admission was ≤ 8/15 in 1146/1261 (91%) of patients with intracerebral haemorrhage, traumatic brain injury or anoxic encephalopathy; the Hunt and Hess Scale was 4–5 in 207/251 (83%) of patients following subarachnoid haemorrhage; and the National Institutes of Health Stroke Scale was ≥ 15 in 114/129 (89%) of patients with strokes. Brain death was diagnosed exclusively by clinical examination in 92/1844 (5%) of cases. Electroencephalography was the most frequently used ancillary test (1303/1752, 70.7%), followed by transcranial Doppler (652/1752, 37%). Organ donation took place in 70% of patients (1291/1844), with medical unsuitability (267/553, 48%) and family refusal (244/553, 13%) the main reasons for loss of potential donors. All life‐sustaining measures were withdrawn in 413/553 of non‐donors (75%).

OBJECTIVES: To describe hospital variation in use of “guideline-based care” for acute respiratory distress syndrome (ARDS) due to COVID-19. DESIGN: Retrospective, observational study. SETTING: The Society of Critical Care Medicine’s Discovery Viral Infection and RESPIRATORY ILLNESS UNIVERSAL STUDY COVID-19 REGISTRY. PATIENTS: Adult patients with ARDS due to COVID-19 between February 15, 2020, and April 12, 2021. INTERVENTIONS: Hospital-level use of “guideline-based care” for ARDS including low-tidal-volume ventilation, plateau pressure less than 30 cm H2O, and prone ventilation for a Pao2/Fio2 ratio less than 100. MEASUREMENTS AND MAIN RESULTS: Among 1,495 adults with COVID-19 ARDS receiving care across 42 hospitals, 50.4% ever received care consistent with ARDS clinical practice guidelines. After adjusting for patient demographics and severity of illness, hospital characteristics, and pandemic timing, hospital of admission contributed to 14% of the risk-adjusted variation in “guideline-based care.” A patient treated at a randomly selected hospital with higher use of guideline-based care had a median odds ratio of 2.0 (95% CI, 1.1–3.4) for receipt of “guideline-based care” compared with a patient receiving treatment at a randomly selected hospital with low use of recommended therapies. Median-adjusted inhospital mortality was 53% (interquartile range, 47–62%), with a nonsignificantly decreased risk of mortality for patients admitted to hospitals in the highest use “guideline-based care” quartile (49%) compared with the lowest use quartile (60%) (odds ratio, 0.7; 95% CI, 0.3–1.9; p = 0.49). CONCLUSIONS: During the first year of the COVID-19 pandemic, only half of patients received “guideline-based care” for ARDS management, with wide practice variation across hospitals. Strategies that improve adherence to recommended ARDS management strategies are needed.

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