南方医科大学学报

南方医科大学学报 ›› 2020, Vol. 40 ›› Issue (12): 1799-1803.doi: 10.12122/j.issn.1673-4254.2020.12.15

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FEV1多元线性回归模型在肺功能测试中的应用

董泉明，宋天然，姜晨宇，姚 钦，陈 芳   

  • 出版日期:2020-12-20 发布日期:2020-12-28

Application of a multiple linear regression model of FEV1 in pulmonary function test

  • Online:2020-12-20 Published:2020-12-28

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摘要： 目的 构建第1秒用力呼气容积（FEV1）多元线性回归模型，为无法执行肺通气功能测试的、或配合不佳的特殊人群提供接近实测值的FEV1计算参考值。方法 选取符合研究标准的人群，其中建模样本813例，验证样本94例。收集其基本资料，肺通气功能测试测定FEV1、强迫振荡技术（FOT）法测定呼吸阻力（Rrs）。各因素相关性采用Pearson分析，多元逐步回归分析建立模型方程，配对t检验验证每个样本的实测值与计算值差异等。结果 FEV1与体质量指数（BMI）无明显相关关系（r=-0.026，P=0.457），与体质量相关性较差（r=0.382，P=0.000），与身高呈正相关关系（r=0.723，P=0.000），与Rrs呈负相关关系（r=-0.503，P=0.000），性别差异明显（t=18.517，P=0.000），年龄<25岁时FEV1与年龄呈正相关关系（r=0.578，P=0.000），年龄>=25岁时与年龄呈负相关关系（r=-0.589，P=0.000）。建模与验证：年龄>=25岁时，纳入身高、性别、年龄和Rrs，实测值与计算值在建模样本（751例，t=1.293，P=0.196>0.05）、验证样本（83例，t=-1.736，P=0.086>0.05）中差异均无统计学意义，且验证样本两值相关性好（r=
0.891，P=0.000）；年龄<25岁时，仅纳入身高，两值在建模样本（62例，t=-0.009，P=0.993>0.05）、验证样本（11例，t=-0.635，P=0.540>0.05）中差异均无统计学意义，验证样本两值相关性较好（r=0.795，P=0.003）。结论 本研究构建的FEV1多元线性回归模型适用于临床。

关键词: 第1秒用力呼气容积；多元线性回归；肺功能测试；FEV1

Abstract: Objective To construct a multiple linear regression model of forced expiratory volume in 1 second (FEV1) for estimating FEV1 in special populations unable to receive or uncooperative in pulmonary ventilation function tests. Methods The multiple linear regression model of FEV1 was constructed based on the data of 813 individuals undergoing pulmonary function tests in First Affiliated Hospital of Zhejiang Chinese Medical University between September, 2017 and September, 2019, and was validated using the data of another 94 individuals from the same hospital between January and July, 2020. FEV1 of the individuals was measured by pulmonary ventilation function test, and respiratory resistance (Rrs) was measured using forced oscillation technique (FOT). Pearson correlation analysis was used to assess the correlation between the factors, and the model equation was established by multiple stepwise regression analysis. The calculated FEV1 based on the model was compared with the measured FEV1 among both the individuals included for modeling and validation. Results FEV1 was not significantly correlated with BMI (r=-0.026, P=0.457), poorly correlated with body mass (r=0.382, P=0.000), positively correlated with height (r=0.723, P=0.000), and negatively correlated with Rrs (r=-0.503, P=0.000) with an obvious gender differences (t=18.517, P=0.000). FEV1 was positively correlated with age among individuals below 25 years of age (r=0.578, P=0.000) and was negatively correlated with age among those beyond or at the age of 25 (r=-0.589, P=0.000). For individuals beyond or at the age of 25 years, the variables of height, gender, age and Rrs were included in the model, and the calculated FEV1 did not differ significantly from the measured values in either the modeling sample (n=751; t=1.293, P=0.196) or the verification sample (n=83; t=-1.736, P=0.086), and the two values were well correlated in the verification sample (r=0.891, P=0.000). For individuals below 25 years, only height was included in the model, and the calculated FEV1 and the measured values showed no significant difference in the modeling sample (n=62; t=- 0.009, P=0.993) or the verification sample (n=11; t=- 0.635, P=0.540) with a good correlation in the verification sample (r=0.795, P=0.003). Conclusion The multiple linear regression model for calculating FEV1 constructed in this study is suitable for clinical application.

Key words: FEV1; multiple linear regression; pulmonary fun