Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, School of Medicine, Emory University, Atlanta VA Sleep Medicine Center, 250 N Arcadia Ave, Decatur, GA, 30030, USA.
Jean Wall Bennett Professor of Medicine, Chair-Education Institute, Cleveland Clinic, 9500 Euclid Ave, Cleveland, OH, USA.
Sci Rep. 2020 Oct 6;10(1):16624. doi: 10.1038/s41598-020-73925-0.
Area under expiratory flow-volume curve (AEX) has been proposed recently to be a useful spirometric tool for assessing ventilatory patterns and impairment severity. We derive here normative reference values for AEX, based on age, gender, race, height and weight, and by using artificial neural network (ANN) algorithms. We analyzed 3567 normal spirometry tests with available AEX values, performed on subjects from two countries (United States and Spain). Regular linear or optimized regression and ANN models were built using traditional predictors of lung function. The ANN-based models outperformed the de novo regression-based equations for AEX and AEX z scores using race, gender, age, height and weight as predictor factors. We compared these reference values with previously developed equations for AEX (by gender and race), and found that the ANN models led to the most accurate predictions. When we compared the performance of ANN-based models in derivation/training, internal validation/testing, and external validation random groups, we found that the models based on pooling samples from various geographic areas outperformed the other models (in both central tendency and dispersion of the residuals, ameliorating any cohort effects). In a geographically diverse cohort of subjects with normal spirometry, we computed by both regression and ANN models several predicted equations and z scores for AEX, an alternative measurement of respiratory function. We found that the dynamic nature of the ANN allows for continuous improvement of the predictive models' performance, thus promising that the AEX could become an essential tool in assessing respiratory impairment.
呼气流量-容积曲线下面积（AEX）最近被提出作为一种评估通气模式和损害严重程度的有用肺量计工具。我们基于年龄、性别、种族、身高和体重，使用人工神经网络（ANN）算法，推导出了 AEX 的正常参考值。我们分析了来自两个国家（美国和西班牙）的 3567 份具有可用 AEX 值的正常肺量计测试。使用传统的肺功能预测因子，建立了常规线性或优化回归和 ANN 模型。基于 ANN 的模型在使用种族、性别、年龄、身高和体重作为预测因素时，在 AEX 和 AEX z 分数方面优于基于从头开始回归的方程。我们将这些参考值与先前为 AEX（按性别和种族）开发的方程进行了比较，发现 ANN 模型得出了最准确的预测。当我们比较 ANN 模型在推导/训练、内部验证/测试和外部验证随机组中的性能时，我们发现基于从不同地理区域汇集样本的模型优于其他模型（在中心趋势和残差的离散度方面都有改善，减轻了任何队列效应）。在一个具有正常肺量计的地理上多样化的受试者队列中，我们使用回归和 ANN 模型计算了几个预测方程和 AEX 的 z 分数，这是一种替代的呼吸功能测量方法。我们发现 ANN 的动态性质允许不断提高预测模型的性能，因此有望使 AEX 成为评估呼吸损害的重要工具。
