This study provides evidence that levosimendan improves right ventricular performance through pulmonary vasodilator effects in septic patients with ARDS. A large multiple-center trial is needed to investigate whether levosimendan is able to improve the overall prognosis of patients with sepsis and ARDS.
Stochastic dierential equations have shown useful to describe random continuous time processes. Biomedical experiments often imply repeated measurements on a series of experimental units and dierences between units can be represented by incorporating random eects into the model. When both system noise and random eects are considered, stochastic dierential mixed-eects models ensue. This class of models enables the simultaneous representation of randomness in the dynamics of the phenomena being considered and variability between experimental units, thus providing a powerful modeling tool with immediate applications in biomedicine and pharmacokinetic/pharmacodynamic studies. In most cases the likelihood function is not available, and thus maximum likelihood estimation of the unknown parameters is not possible. Here we propose a computationally fast approximated maximum likelihood procedure for the estimation of the non-random parameters and the random eects. The method is evaluated on simulations from some famous diusion processes and on real datasets.
Stochastic differential equations (SDEs) are established tools to model physical phenomena whose dynamics are affected by random noise. By estimating parameters of an SDE intrinsic randomness of a system around its drift can be identified and separated from the drift itself. When it is of interest to model dynamics within a given population, i.e. to model simultaneously the performance of several experiments or subjects, mixed-effects modelling allows for the distinction of between and within experiment variability. A framework to model dynamics within a population using SDEs is proposed, representing simultaneously several sources of variation: variability between experiments using a mixed-effects approach and stochasticity in the individual dynamics using SDEs. These stochastic differential mixed-effects models have applications in e.g. pharmacokinetics/pharmacodynamics and biomedical modelling. A parameter estimation method is proposed and computational guidelines for an efficient implementation are given. Finally the method is evaluated using simulations from standard models like the two-dimensional Ornstein-Uhlenbeck (OU) and the square root models.
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