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首页> 外文期刊>Journal of applied physiology >Exhaled NO reference limits in a large population-based sample using the Lambda-Mu-Sigma method
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Exhaled NO reference limits in a large population-based sample using the Lambda-Mu-Sigma method

机译:使用Lambda-Mu-Sigma方法呼出的基于大群样本中没有参考限制

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摘要

Absolute values are used in the interpretation of the fraction of exhaled nitric oxide (FeNO), but it has been suggested that equations to calculate reference values may be a practical and clinically useful approach. We hypothesize that the application of the Lambda-Mu-Sigma (LMS) method may improve FeNO reference equations and their interpretation. Our aims were to develop FeNO reference equations with the LMS method and to describe the difference between this method and the absolute fixed cut-offs of the current recommendations. We utilized the United States National Health and Nutrition Examination Surveys 2007-2012 and included healthy individuals with no respiratory diseases and blood eosinophils 300/mm(3) (n = 8,340). Natural log-transformed FeNO was modeled using the LMS method, imbedded in the generalized additive models for location, scale, and shape models. A set of FeNO reference equations was developed. The explanatory variables were sex, age, height, smoking habits, and race/ethnicity. A significant proportion of individuals with normal FeNO given by the equations were classified as having intermediate levels by the current recommendations. Further lower predicted FeNO compared with previous linear models was seen. In conclusion, we suggest a novel model for the prediction of reference FeNO values that can contribute to the interpretation of FeNO in clinical practice. This approach should be further validated in large samples with an objective measurement of atopy and a medical diagnosis of asthma and rhinitis.
机译:绝对值用于解释呼出的一氧化氮(FENo)的级分,但已经提出了计算参考值的方程可以是实用且临床上有用的方法。我们假设Lambda-Mu-Sigma(LMS)方法的应用可以改善FENO参考方程及其解释。我们的目标是通过LMS方法开发FENO参考方程,并描述该方法与当前建议的绝对固定截止之间的差异。我们利用美国国家健康和营养考试调查2007-2012,包括没有呼吸系统疾病和血液粒细胞的健康个体& 300 / mm(3)(n = 8,340)。使用LMS方法模拟自然对数转换的FENO,嵌入在广义添加剂模型中,用于位置,尺度和形状模型。开发了一组FENO参考方程。解释性变量是性,年龄,高度,吸烟习惯和种族/种族。由等式给出的正常FENO的个体的大量比例被当前建议归类为具有中级水平。与先前的线性模型相比,进一步降低了预测的FENo。总之,我们建议一种预测参考FENO值的新型模型,可以有助于对临床实践中FENO的解释。这种方法应在大型样品中进一步验证,具有目标测量的特性和哮喘和鼻炎的医学诊断。

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