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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...

Predicting time to death in controlled donation after circulatory death (cDCD) donors following withdrawal of life‐sustaining treatment (WLST) is important but poses a major challenge. The aim of this study is to determine factors predicting time to circulatory death within 60 minutes after WSLT and validate previously developed prediction models. In a single‐center retrospective study, we used the data of 92 potential cDCD donors. Multivariable regression analysis demonstrated that absent cough‐, corneal reflex, lower morphine dosage, and midazolam use were significantly associated with death within 60 minutes (area under the curve [AUC] 0.89; 95% confidenence interval [CI] 0.87‐0.91). External validation of the logistic regression models of de Groot et al (AUC 0.86; 95% CI 0.77‐0.95), Wind et al (AUC 0.62; 95% CI 0.49‐0.76), Davila et al (AUC 0.80; 95% CI 0.708‐0.901) and the Cox regression model by Suntharalingam et al (Harrell's c‐index 0.63), exhibited good discrimination and could fairly identify which patients died within 60 minutes. Previous prediction models did not incorporate the process of WLST. We believe that future studies should also include the process of WLST as an important predictor.

Background. Acceptance of organs from controlled donation after circulatory death (cDCD) donors depends on the time to circulatory death. Here we aimed to develop and externally validate prediction models for circulatory death within 1 or 2 h after withdrawal of life-sustaining treatment. Methods. In a multicenter, observational, prospective cohort study, we enrolled 409 potential cDCD donors. For model development, we applied the least absolute shrinkage and selection operator (LASSO) regression and machine learning–artificial intelligence analyses. Our LASSO models were validated using a previously published cDCD cohort. Additionally, we validated 3 existing prediction models using our data set. Results. For death within 1 and 2 h, the area under the curves (AUCs) of the LASSO models were 0.77 and 0.79, respectively, whereas for the artificial intelligence models, these were 0.79 and 0.81, respectively. We were able to identify 4% to 16% of the patients who would not die within these time frames with 100% accuracy. External validation showed that the discrimination of our models was good (AUCs 0.80 and 0.82, respectively), but they were not able to identify a subgroup with certain death after 1 to 2 h. Using our cohort to validate 3 previously published models showed AUCs ranging between 0.63 and 0.74. Calibration demonstrated that the models over- and underestimated the predicted probability of death. Conclusions. Our models showed a reasonable ability to predict circulatory death. External validation of our and 3 existing models illustrated that their predictive ability remained relatively stable. We accurately predicted a subset of patients who died after 1 to 2 h, preventing starting unnecessary donation preparations, which, however, need external validation in a prospective cohort.

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