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Caro Editor, Esta carta visa complementar os dados publicados por Silva et al., ( ) apresentando os resultados das concentracoes plasmaticas de pacientes que utilizaram piperacilina (PIP) em dose empirica (DE) ou dose ajustada (DA) pelo software Optimum Dosing Strategies (ID-ODS), alem de expor alguns pontos limitadores observados pelos autores durante a utilizacao do software na realidade de um hospital brasileiro. […] Software para ajuste de doses de antimicrobianos. Implicacoes nas concentracoes plasmaticas e limitacoes praticas

Objective To adapt an antibiotic dose adjustment software initially developed in English, to Portuguese and to the Brazilian context. Methods This was an observational, descriptive study in which the Delphi method was used to establish consensus among specialists from different health areas, with questions addressing the visual and operational aspects of the software. In a second stage, a pilot experimental study was performed with the random comparison of patients for evaluation and adaptation of the software in the real environment of an intensive care unit, where it was compared between patients who used the standardized dose of piperacillin/tazobactam, and those who used an individualized dose adjusted through the software Individually Designed and Optimized Dosing Strategies. Results Twelve professionals participated in the first round, whose suggestions were forwarded to the software developer for adjustments, and subsequently submitted to the second round. Eight specialists participated in the second round. Indexes of 80% and 90% of concordance were obtained between the judges, characterizing uniformity in the suggestions. Thus, there was modification in the layout of the software for linguistic and cultural adequacy, minimizing errors of understanding and contradictions. In the second stage, 21 patients were included, and there were no differences between doses of piperacillin in the standard dose and adjusted dose Groups. Conclusion The adapted version of the software is safe and reliable for its use in Brazil.

Population pharmacokinetic analyses can be applied to predict optimized dosages for individual patients. The aim of this study was to compare the prediction performance of the published population pharmacokinetic models for meropenem in critically ill patients. We coded the published population pharmacokinetic models with covariate relationships into dosing software to predict unbound meropenem concentrations measured in a separate cohort of critically ill patients. The agreements between the observed and predicted concentrations were evaluated with Bland-Altman plots. The absolute and relative bias and precision of the models were determined. The clinical implications of the results were evaluated according to whether dose adjustments were required from the predictions to achieve a meropenem concentration of >2 mg/liter throughout the dosing interval. A total of 157 free meropenem concentrations from 56 patients were analyzed. Eight published population pharmacokinetic models were compared. The models showed an absolute bias in predicting the unbound meropenem concentrations from a mean percent difference (95% confidence interval [CI]) of −108.5% (−119.9% to −97.3%) to 19.9% (7.3% to 32.7%), while absolute precision ranged from −249.1% (−263.4% to −234.8%) to 31.9% (17.6% to 46.2%) and −178.9% (−196.9% to −160.9%) to 175.0% (157.0% to 193.0%). A dose change was required in 44% to 64% of the concentration results. Seven of the eight equations evaluated underpredicted free meropenem concentrations. In conclusion, the overall accuracy of these models supports their inclusion in dosing software and application for individualizing meropenem doses in critically ill patients to increase the likelihood of achievement of optimal antibiotic exposures.

Background In the last century Hungary had astonishingly high suicide rates characterized by marked regional within-country inequalities, a spatial pattern which has been quite stable over time. Aims To explain the above phenomenon at the level of micro-regions ( n =175) in the period between 2005 and 2011. Methods Our dependent variable was the age and gender standardized mortality ratio (SMR) for suicide while explanatory variables were factors which are supposed to influence suicide risk, such as measures of religious and political integration, travel time accessibility of psychiatric services , alcohol consumption, unemployment and disability pensionery. When applying the ordinary least squared regression model, the residuals were found to be spatially autocorrelated, which indicates the violation of the assumption on the independence of error terms and – accordingly – the necessity of application of a spatial autoregressive (SAR) model to handle this problem. According to our calculations the SAR lag model was a better way (versus the SAR err model) of addressing the problem of spatial autocorrelation, furthermore its substantive meaning is more convenient. Results SMR was significantly associated with the “political integration” variable in a negative and with “lack of religious integration” and “disability pensionery” variables in a positive manner. Associations were not significant for the remaining explanatory variables. Limitations Several important psychiatric variables were not available at the level of micro-regions. We conducted our analysis on aggregate data. Conclusion Our results may draw attention to the relevance and abiding validity of the classic Durkheimian suicide risk factors – such as lack of social integration – apropos of the spatial pattern of Hungarian suicides.