Department of Biometry and Clinical Epidemiology, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy.
Clin Chim Acta. 2012 Mar 22;413(5-6):544-7. doi: 10.1016/j.cca.2011.11.012. Epub 2011 Dec 3.
Serial measurement of NT-proBNP is performed routinely in the monitoring and assessment of the effectiveness of therapy in patients being treated for chronic heart failure (CHF). Intra-individual changes in NT-proBNP levels over time are compared typically to a reference change value (RCV) determined using either a standard [i.e., nested analysis of variance (nANOVA)] or a lognormal approach. The RCV defines the minimum percent change in serial analyte values that exceeds the percent change expected due to biological variation alone. Currently, there is no consensus on which approach (nANOVA or lognormal) to determining RCV is better.
Based on these considerations, we aimed to illustrate the impact of data transformation on the calculation of the biological variation of NT-proBNP and discuss the utility of logarithmic transformation in monitoring patients with heart failure.
15 healthy subjects were enrolled after informed consent; 5 blood specimens were collected twice a week. Nested ANOVA from replicate analyses was applied to obtain components of biological variation, on the raw data and after data transformation.
NT-proBNP distribution being highly skewed required data transformation. Natural log transformation yielded normalization. An example demonstrates that for untransformed values the RCV was overestimated for low concentrations of NT-proBNP and underestimated for higher concentrations.
Log-transformed data are often used in the establishment of reference intervals for evaluating laboratory tests results in clinical practice, especially when the reference interval data are not Gaussian distributed. As log-normal approach is the best approach to determining RCV values we encourage its use assessing the clinical utility of NT-proBNP serial testing. We propose that the log-normal approach becomes the standard approach to determining RCV and replaces the use of nANOVA.
在监测和评估慢性心力衰竭（CHF）患者的治疗效果时，通常会常规对 NT-proBNP 进行连续测量。通常将 NT-proBNP 水平随时间的个体内变化与使用标准[即嵌套方差分析（nANOVA）]或对数正态方法确定的参考变化值（RCV）进行比较。RCV 定义了连续分析物值的最小百分比变化，该变化超过了仅由于生物学变异而预期的百分比变化。目前，对于确定 RCV 的哪种方法（nANOVA 或对数正态）更好，尚无共识。
基于这些考虑，我们旨在说明数据转换对 NT-proBNP 生物学变异计算的影响，并讨论对数转换在监测心力衰竭患者中的实用性。
在获得知情同意后，招募了 15 名健康受试者；每周采集 5 份血液标本两次。应用重复分析的嵌套 ANOVA 从原始数据和数据转换后获得生物学变异的组成部分。
由于 NT-proBNP 分布高度偏态，因此需要进行数据转换。自然对数转换产生了归一化。一个例子表明，对于未转换的值，当 NT-proBNP 浓度较低时，RCV 被高估，而当浓度较高时，RCV 被低估。
在临床实践中，对数转换后的数据通常用于建立评估实验室测试结果的参考区间，尤其是当参考区间数据不是正态分布时。由于对数正态方法是确定 RCV 值的最佳方法，因此我们鼓励其用于评估 NT-proBNP 连续测试的临床实用性。我们建议对数正态方法成为确定 RCV 的标准方法，并取代 nANOVA 的使用。
