摘要
健康人二氧化碳总量参考值是血气分析的一个重要指标.目前,国内外缺乏二氧化碳总量参考值的统一标准,该研究为制定中国成年人二氧化碳总量参考值的统一标准提供了科学依据.收集了中国70个单位用血气分析仪测定的9 157例健康成年人二氧化碳总量参考值,运用相关分析和回归分析的方法,研究了其与8个地理因素的关系.结果发现健康成年人二氧化碳总量参考值与中国地理因素之间有很显著的相关关系(F=41.098,P=0.000).回归方程为:Y=25.99-0.002115X1-0.03764X4+0.05247X5-0.5907X8±3.38.得出结论:如果知道了中国某地的地理因素,就可以用回归方程计算这个地区的成年人二氧化碳总量参考值.依据成年人二氧化碳总量参考值与地理因素的依赖关系,把中国分为东北、华北、晋陕内蒙古、长江中下游、东南、西北、西南、青藏8个区.
The reference value of total carbon dioxide (TCO2) of the healthy people is an important indicator of blood gas analysis, but it is lack of the uniform criterion of the reference value of total carbon dioxide in the domestic and external. This paper aims at supplying a scientific basis for unifying the reference value standard of total carbon dioxide of Chinese adults. A research is made about relationship between the normal reference value of 9 157 examples of Chinese healthy adults' total carbon dioxide and eight geographical factors in 70 areas in China; the normal reference value is determined by the blood gas analysis instrument. It is found that the correlation of geographical factors and the normal reference value of Chinese adults' total carbon dioxide are quite significant (F=41.098, P=0.000). One regression equation is: ^↑Y=25.99-0.002115X1-0. 03764X4+0. 05247X5 -0. 5907X8 ± 3. 38. Conclusion: If geographical values are obtained in some areas, the normal reference value of Chinese adults' total carbon dioxide of this area can be reckoned using the regression equations. Furthermore, according to the similarity of the normal reference value of Chinese adults' total carbon dioxide, China can be divided into eight regions: Northeast China Region, North China Region, Shanxi-Shaanxi-Inner Mongolia Region, Middle and Lower Reaches of the Changjiang River Region, Southeast China Region, Northwest China Region, Southwest China Region, Qinghai-Tibet Plateau Region.
出处
《华中师范大学学报(自然科学版)》
CAS
CSCD
2008年第3期486-489,共4页
Journal of Central China Normal University:Natural Sciences
基金
国家自然科学基金项目(40371004)
关键词
二氧化碳总量
参考值
地理因素
回归分析
total carbon dioxide
reference value
geographical elements
regression analysis