首页> 外文期刊>European journal of clinical pharmacology >Population-based investigation of relative clearance of digoxin in Japanese neonates and infants by multiple-trough screen analysis.
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Population-based investigation of relative clearance of digoxin in Japanese neonates and infants by multiple-trough screen analysis.

机译:通过多槽筛查分析基于人口的日本新生儿和婴儿中地高辛的相对清除率调查。

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OBJECTIVE: The steady-state concentrations of digoxin at trough levels were studied to establish the role of patient characteristics in estimating doses for digoxin using routine therapeutic drug monitoring data. METHOD: The data (n = 448) showing steady state after repetitive oral administration in 172 hospitalized neonates and infants were analyzed using Nonlinear Mixed Effect Model (NONMEM), a computer program designed to analyze pharmacokinetics in study populations by allowing pooling of data. Analysis of the pharmacokinetics of digoxin was accomplished using a simple steady-state pharmacokinetic model. The effects of a variety of developmental and demographic factors on the clearance of digoxin were investigated. RESULTS: Estimates generated using NONMEM indicated that clearance of digoxin (l.h-1) was influenced by the demographic variables of age, total body weight, serum creatinine, the coadministration of spironolactone, and the presence or absence of congestive heart failure. The interindividual variability in digoxin clearance was modeled with proportional errors with an estimated coefficient of variation of 32.1%, and the residual variability was 28.9%. In the validation set of 66 patients, the performance (bias, precision) of the final population model was good (mean prediction error -0.04 ng.ml-1; mean absolute prediction error 0.20 ng.ml-1).
机译:目的:研究地高辛在低谷水平的稳态浓度,以建立患者特征在使用常规治疗药物监测数据估算地高辛剂量中的作用。方法:使用非线性混合效应模型(NONMEM)分析了172名住院新生儿和婴儿反复口服后的稳定状态数据(n = 448),该计算机程序旨在通过汇总数据来分析研究人群的药代动力学。地高辛的药代动力学分析是使用简单的稳态药代动力学模型完成的。研究了各种发育和人口因素对地高辛清除率的影响。结果:使用NONMEM产生的估计值表明,地高辛(l.h-1)的清除率受年龄,总体重,血清肌酐,螺内酯的并用以及充血性心力衰竭的人口统计学变量的影响。地高辛清除率的个体间差异采用比例误差建模,估计变异系数为32.1%,残留变异率为28.9%。在验证的66位患者中,最终总体模型的性能(偏倚,精确度)良好(平均预测误差-0.04 ng.ml-1;平均绝对预测误差0.20 ng.ml-1)。

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