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...

Use of an antibiotic checklist resulted in a significant increase in appropriateness of antibiotic use, but not in a reduction of LOS. Low overall checklist completion rates and discordance between checklist-answers and actual provided care might have attenuated the impact of the checklist.

Dosing of vancomycin is often guided by therapeutic drug monitoring and population pharmacokinetic models in the intensive care unit (ICU). The validity of these models is crucial, as ICU patients have marked pharmacokinetic variability. Therefore, we set out to evaluate the predictive performance of published population pharmacokinetic models of vancomycin in ICU patients. The PubMed database was used to search for population pharmacokinetic models of vancomycin in adult ICU patients. The identified models were evaluated in two independent data sets which were collected from two large hospitals in the Netherlands (Amsterdam UMC, Location VUmc, and OLVG Oost). We also tested a one-compartment model with fixed values for clearance and volume of distribution, in which a clinical standard dosage regimen (SDR) was mimicked to assess its predictive performance. Prediction error was calculated to assess the predictive performance of the models. Six models plus the SDR model were evaluated. The model of Roberts et al. (J. A. Roberts, F. S. Taccone, A. A. Udy, J.-L. Vincent, F. Jacobs, and J. Lipman, Antimicrob Agents Chemother 55:2704–2709, 2011, https://doi.org/10.1128/AAC.01708-10) performed satisfactorily, with mean and median values of prediction error of 5.1% and −7.5%, respectively, for Amsterdam UMC, Location VUmc, patients, and −12.6% and −17.2% respectively, for OLVG Oost patients. The other models, including the SDR model, yielded high mean values (−49.7% to 87.7%) and median values (−56.1% to 66.1%) for both populations. In conclusion, only the model of Roberts et al. was able to validly predict the concentrations of vancomycin for our data, whereas other models and standard dosing were largely inadequate. Extensive evaluation should precede the adoption of any model in clinical practice for ICU patients.

Background Initial appropriate anti-infective therapy is associated with improved outcomes in patients with severe infections. In critically ill patients, altered pharmacokinetic (PK) behaviour is common and known to influence the achievement of PK/pharmacodynamic targets. Objectives To describe population PK and optimized dosing regimens for flucloxacillin in critically ill patients. Methods First, we developed a population PK model, estimated between-patient variability (BPV) and identified covariates that could explain BPV through non-linear mixed-effects analysis, using total and unbound concentrations obtained from 35 adult critically ill patients treated with intermittent flucloxacillin. Second, we validated the model using external datasets from two different countries. Finally, frequently prescribed dosing regimens were evaluated using Monte Carlo simulations. Results A two-compartment model with non-linear protein binding was developed and validated. BPV of the maximum binding capacity decreased from 42.2% to 30.4% and BPV of unbound clearance decreased from 88.1% to 71.6% upon inclusion of serum albumin concentrations and estimated glomerular filtration rate (eGFR; by CKD-EPI equation), respectively. PTA (target of 100%fT>MIC) was 91% for patients with eGFR of 33 mL/min and 1 g q6h, 87% for patients with eGFR of 96 mL/min and 2 g q4h and 71% for patients with eGFR of 153 mL/min and 2 g q4h. Conclusions For patients with high creatinine clearance who are infected with moderately susceptible pathogens, therapeutic drug monitoring is advised since there is a risk of underexposure to flucloxacillin. Due to the non-linear protein binding of flucloxacillin and the high prevalence of hypoalbuminaemia in critically ill patients, dose adjustments should be based on unbound concentrations.