Clusions The developed software is able to simulate different combinations of submodels at variable complexity. Simulation results are consistent with experimental data. Interaction between submodels can be seen in the simulation output. References 1. Lozano S, Moller K, Brendle A, et al.: AUTOPILOT-BT: a system for knowledge and model based mechanical ventilation. Technol Health Care 2008, 16:1-11. 2. Jensen MC, Lozano S, Gottlieb D, et al.: An evaluation of end-tidal CO2 change following alteration in ventilation frequency [abstract]. In MBEC Antwerpen Conference; 2008.P152 Intraaortic balloon pumping: why should we hurry up?A Macas, A Mundinaite, G Baksyte Kaunas University of Medicine, Kaunas, Lithuania Critical Care 2009, 13(Suppl 1):P152 (doi: 10.1186/cc7316) Introduction Application of the intraaortic balloon pump (IABP) for patients with acute myocardial infarction complicated by cardiogenic shock is undoubted. But the time of IABC initiation is questionable. The goal of the study was to evaluate the influence of the IABP initiation time and dose of dopamine for patient hemodynamic data and inhospital mortality. Methods Sixty-two consecutive acute myocardial infarction patients managed with IABP were included in the study. The initiation time of IABP and administered dopamine doses were compared. Two subgroups of patients were separated: those receiving less than 10 g/kg/minute dopamine, and others receiving 10 g/kg/minute dopamine or more. Standard hemodynamic indices of cardiac output (CO), cardiac index (CI), stroke volume (SV), stroke index (SI), pulmonary capillary wedge pressure (PCWP), mean pulmonary artery pressure (PAmean), cardiacConclusions There were more significant negative correlations between plasmatic Se and parameters of inflammation in the group of patients supplemented with high doses of Se. In accordance was a finding of more frequent transition PubMed ID:https://www.ncbi.nlm.nih.gov/pubmed/8476056 from septic shock to a less severe stage of sepsis. Even if a decrease of mortality was again not found, the presented results may perhaps indicate a positive metabolic influence in critical illness.P151 Dynamic generation of physiological model systemsJ Kretschmer1, A Wahl2, J Guttmann2, K M ler1 1Furtwangen University, Villingen-Schwenningen, Germany; 2University Hospital Freiburg, Germany Critical Care 2009, 13(Suppl 1):P151 (doi: 10.1186/cc7315) Introduction Mathematical models are widely used to simulate physiological processes in the human body and can be exploited for diagnostic purpose or the automation of therapeutic measuresSAvailable online http://ccforum.com/supplements/13/Spower (CP = CO x MAP / 451, where MAP is mean arterial pressure) and cardiac power index SD-169 (CPI = CI x MAP / 451) were measured. Results CO in the first group was 3.58 ?1.12 l/minute, in comparison with 2.54 ?1.08 l/minute (P <0.0001) in the second group. CI correspondingly was 1.9 ?0.6 l/minute/m2 and 1.38 ?0.61 l/minute/m2 (P = 0.001), SV was 43.8 ?26.14 ml and 30.01 ?13.75 ml (P = 0.012), SI was 23.93 ?15.58 ml/m2 and 16.28 ?7.75 ml/m2 (P = 0.017), CP was 0.68 ?0.25 W and 0.4 ?0.23 W (P <0.0001), CPI was 0.36 ?0.14 W/m2 and 0.22 ?0.13 W/m2 (P <0.0001), PCWP was 21.32 ?6.04 mmHg and 24.16 ?7.42 mmHg (P = 0,104), PAmean was 28.74 ?8.53 mmHg and 30.23 ?7.05 mmHg (P = 0.47). In the second subgroup, hemodynamic indices (CO, CI, SV, SI, CP and CPI) were statistically significantly lower (P <0.05) in comparison with the data of patients receiving less than 10 g/kg/minute, while PCWP and CVP.