Optimal loading dose of meropenem before continuous infusion in critically ill patients: a simulation study

The aim of this study was to investigate optimal loading doses prior to continuous infusion of meropenem in critically ill patients. A previously published and successfully evaluated pharmacokinetic model of critically ill patients was used for stochastic simulations of virtual patients. Maintenance doses administered as continuous infusion of 1.5–6 g/24 h with preceding loading doses (administered as 30 min infusion) of 0.15–2 g were investigated. In addition to the examination of the influence of individual covariates, a best-case and worst-case scenario were simulated. Dosing regimens were considered adequate if the 5th percentile of the concentration–time profile did not drop at any time below four times the S/I breakpoint (= 2 mg/L) of Pseudomonas aeruginosa according to the EUCAST definition. Low albumin concentrations, high body weight and high creatinine clearances increased the required loading dose. A maximum loading dose of 0.33 g resulted in sufficient plasma concentrations when only one covariate showed extreme values. If all three covariates showed extreme values (= worst-case scenario), a loading dose of 0.5 g was necessary. Higher loading doses did not lead to further improvements of target attainment. We recommend the administration of a loading dose of 0.5 g meropenem over 30 min immediately followed by continuous infusion.

Model Pedukung Keputusan Dengan Metode Analitycal Hierarchy Process: Studi Kasus Proses Bongkar Muatan Barang Kapal

As a center for leaning ships, the Port is a center that plays an important role as a facility that can connect one island to another in trading activities. In this study emphasizes the final goal of (1) determining the factors used as criteria for determining the right and optimal loading decisions, (2) calculating the priority weights of the decision criteria, (3) determining priority weights, (4) obtaining a design that best in priority ship unloading. In this study we use hierarchical based analysis methods or commonly called Analytical hierarchy process (AHP). This method performs pairwise comparisons between criteria and sub criteria by producing a criteria value matrix and produces consistent information each factor from the sub-criteria assessed where the CR <0.1. The results of the scores for each 3 respondents are where the arrival time of the ship scores a total of 17.46 with the highest score, the type of load total score of 11.89, the type of ship the total score is 6.61 and the total loading score of 3.05.

Dynamic programming in applied tasks which are allowing to reduce the options selection

The article discusses the dynamic programming algorithm developed by R. Bellman, based on the search for the optimal trajectory connecting the nodes of a predefined regular grid of states. Possibilities are analyzed for a sharp increase in the effectiveness of using dynamic programming in solving applied problems with specific features, which allows us to refuse to split a regular grid of states and implement an algorithm for finding the optimal trajectory when rejecting not only unpromising options for paths leading to each of the states, and all of them continuations, as in R. Bellmanʼs algorithm, but also actually hopeless states and all variants of paths emanating from them. The conditions are formulated and justified under which the rejection of hopeless states is possible. It has been established that many applied problems satisfy these conditions. To solve such problems, a new dynamic programming algorithm described in the article is proposed and implemented. Concrete examples of such applied problems are given: the optimal distribution of a homogeneous resource between several consumers, the optimal loading of vehicles, the optimal distribution of finances when choosing investment projects. To solve these problems, dynamic programming algorithms with rejecting unpromising paths, but without rejecting states, were previously proposed. The number of hopeless states that appear at various stages of dynamic programming and, accordingly, the effectiveness of the new algorithm depends on the specific numerical values of the source data. For the two-parameter problem of optimal loading of vehicles with weight and volume constraints, the results of comparative calculations by the R. Bellman algorithm and the new dynamic programming algorithm are presented. As a source of data for a series of calculations, pseudorandom numbers were used. As a result of the analysis, it was shown that the comparative efficiency of the algorithm with rejection of states increases with increasing dimension of the problem. So, in the problem of the optimal choice of items for loading a vehicle of a given carrying capacity with a number of items of 150, the number of memorized states and the counting time are reduced by 50 and 57 times, respectively, when using the new algorithm compared to the classical algorithm of R. Bellman. And for 15 items, the corresponding numbers are 13 and 4.