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Evaluation and comparison of simple multiple model, richer data multiple model, and sequential interacting multiple model (IMM) bayesian analyses of gentamicin and vancomycin data collected from patients undergoing cardiothoracic surgery

机译:对从心胸外科手术患者收集的庆大霉素和万古霉素数据的简单多模型,更丰富的数据多模型以及顺序交互多模型(IMM)贝叶斯分析进行评估和比较

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摘要

This study compared the abilities of three Bayesian algorithms-simple multiple model (SMM) using a single creatinine measurement; richer data multiple model (RMM) using all creatinine measurements; and the sequential interacting multiple model (IMM)to describe gentamicin and vancomycin concentration-time data from patients within a cardiothoracic surgery unit who had variable renal function. All algorithms start with multiple sets of discrete parameter support points obtained from nonparametric population modeling. The SMM and RMM Bayesian algorithms then estimate their Bayesian posterior probabilitiis by conventionally assuming that the estimated parameter distributions are fixed and unchanging throughout the period of data analysis. In contrast, the IMM sequential Bayesian algorithm permits parameter estimates to jump from one population model support point to another, as new data are analyzed, if the probability of a different support point fitting the more recent data is more likely. Several initial IMM jump probability settings were examined-0.0001%, 0.1%, 3 %, and 10%-and a probability range of 0.0001% to 50%. The data sets comprised 550 gentamicin concentration measurements from 135 patients and 555 vancomycin concentration measurements from 139 patients. The SMM algorithm performed poorly with both antibiotics. Improved precision was obtained with the RMM algorithm. However, the IMM algorithm fitted the data with the highest precision. A 3% jump probability gave the best estimates. In contrast, the IMM 0.0001% to 50% range setting performed poorly, especially for vancomycin. In summary, the IMM algorithm described and tracked drug concentration data well in these clinically unstable patients. Further investigation of this new approach in routine clinical care and optimal dosage design is warranted.
机译:这项研究比较了使用单个肌酐测量值的三种贝叶斯算法-简单多重模型(SMM)的能力;使用所有肌酐测量值的更丰富的数据多重模型(RMM);以及依次交互多重模型(IMM)来描述来自心胸外科手术单元中具有可变肾功能的患者的庆大霉素和万古霉素浓度-时间数据。所有算法都从从非参数总体建模中获得的多组离散参数支持点开始。然后,SMM和RMM贝叶斯算法随后通过常规地假设估计的参数分布在数据分析期间是固定且不变的,来估计其贝叶斯后验概率。相比之下,IMM顺序贝叶斯算法允许参数估计值在分析新数据时从一个总体模型支持点跳到另一总体模型支持点,如果不同支持点适合最新数据的可能性更大。检查了几种初始IMM跳跃概率设置-0.0001%,0.1%,3%和10%-概率范围为0.0001%至50%。数据集包括来自135位患者的550份庆大霉素浓度测量值和来自139位患者的555份万古霉素浓度测量值。两种抗生素的SMM算法效果均不理想。使用RMM算法可获得更高的精度。但是,IMM算法以最高的精度拟合了数据。 3%的跳跃概率给出了最佳估计。相反,IMM的0.0001%至50%范围设置效果不佳,尤其是对于万古霉素。总之,IMM算法很好地描述和跟踪了这些临床不稳定患者的药物浓度数据。有必要对该常规临床护理和最佳剂量设计中的新方法进行进一步研究。

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