External Validation of Aminoglycoside Models Used in Web Calculators and Clinical Decision Support Systems After Laboratory Conversion to Serum Creatinine Isotope Dilution Mass Spectrometry Assay

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• 作者：William R. Wolowich ; PharmD¹ ; ^{wwolowic@nova.edu} ; Jennifer G. Steinberg ; PharmD¹ ; ²
• 关键词：Bayes theorem ; Bland Altman analysis ; clinical decision support modules ; computerized physician order entry ; elimination rate constant ; external validation ; gentamicin ; half-life ; isotope dilution mass spectrometry ; nonlinear regression analysis ; predicti
• 刊名：Clinical Therapeutics
• 出版年：2012
• 期刊代码：65_01492918
• 类别：med
• 出版时间：April, 2012
• 卷：34
• 期：4
• 页码：803-810
• 文件大小：338 K

摘要

Background

Models to predict gentamicin t_陆 from serum creatinine (SCr) estimated creatinine clearance (CrCl) are currently being incorporated into smart-device applications and clinical decision support modules without external validation.

Objective

The aim of this study was to determine whether such models remain viable after conversion to isotope dilution mass spectrometry (IDMS) SCr assay.

Methods

This study analyzed data from retrospective reviews of the medical records of nonobese adults receiving the aminoglycoside gentamicin and having 鈮? evaluable serum gentamicin concentrations after laboratory IDMS SCr conversion, from January 2008 to August 2009, at a tertiary care hospital in Florida. A literature search found a number of cited aminoglycoside models. This group of models was classified as group 1. The World Wide Web was also searched for the term aminoglycoside dosing calculators, with 6 models found and referred to as group 2. Predictive performance measures were used to compare the model results with the t_陆 calculated from gentamicin concentrations using the Nelder-Mead algorithm.

Results

The records of 39 patients met the inclusion criteria (23 men, 16 women; age range, 18-86 years; range of estimated CrCl, 55-115 mL/min) and provided the 鈥済old standard鈥?aminoglycoside t_陆. A gentamicin t_陆 was predicted from several published models (group 1) and from other models used in online smart-device applications (group 2) and clinical decision modules. The median (interquartile range) root mean square errors were 0.48 (0.44 to 0.65) and 0.48 (0.45 to 0.70) hours from group-1 and -2 models, respectively. The median mean relative prediction errors were 9%(鈭?4%to +13%) and 11%(+1%to +21%) from groups 1 and 2. The median mean absolute prediction errors were 21%(19%to 28%) and 21%(20%to 30%) from groups 1 and 2. Adjusting SCr by +20%improved the predictive ability in 3 of 12 cited models and in 5 of 6 models used in applications.

Conclusions

Models to predict gentamicin t_陆 should be externally validated at one's institution before use. The findings from the present study provide a framework for conducting external validation.