Second Rate, Residual Total Percentage And Functional Residual Capacity Reference Value Of Geographical Distribution

Objective:The reference value of FEV1.0%, RV/TLC and FRC are important indexes on lung function testing. They are as judging indexes for the degree of airflow obstruction, which provides the basis for prevention and treatment. Due to the lack of uniting the reference value standard, the accuracy of clinical diagnosis is seriously affected. Many medical workers measured the reference value of FEV1.0%, RV/TLC and FRC in some regions, and some descript qualitative relation between the reference value of FEV1.0%, RV/TLC, FRC and geographical factors, but topic and quantitative study has not been reported before. Method:This paper selects six geographical factors that geographical document provided, which are altitude, annual sunshine duration, annual mean air temperature, annual range of air temperature, annual mean relative humidity, annual precipitation amount, and explores the relation between the reference value of FEV1.0%, RV/TLC and FRC of different gender and different age of Chinese people. To the whole of China as the study area, geographical distribution map of the reference value of medical indexes is precisely interpolated by GIS spatial analysis.Results:The relation between the reference value of presenile men' FEV1.0%, the reference value of middle-aged men' RV/TLC, the reference value of old men' RV/TLC and geographical factors is explored in this paper, and mathematical models are set up.Factorial regression model between the reference value of presenile men' FEV1.0% and geographical factors is Y=77.93-0.0004518X1-0.0005240X2+0.05639X3-0.01615X4+0.03800X5+0.0003352X6±4.333Factorial regression model between the reference value of middle-aged men' RV/TLC and geographical factors is Y=28.49-0.0007474x1-0.0002139x2+0.04396x3+0.03615x4+0.02961x5+0.0005054x6±4. 552Factorial regression model between the reference value of old men' RV/TLC and geographical factors is Y=33.49-0.001319x1-0.0003431x2+0.09287x3-0.01044x4+0.04255x5+0.001286x6±4.85 6Geographical factors affecting the reference value of medical indexes largely are selected, and the relation between the reference value of middle-aged men' FEV1.0%, the reference value of middle-aged women' FEV1.0%, the reference value of middle-aged women' RV/TLC,. the reference value of middle-aged FRC and geographical factors through building BP artificial neural network.When the relation between the reference value of middle-aged men' FEV1.0% and geographical factors is explored, annual sunshine duration, annual mean air temperature, annual range of air temperature, annual mean relative humidity, annual precipitation amount are selected as input data. BP artificial neural network is built, and the best result is training 1500 times.When the relation between the reference value of middle-aged women' FEV1.0% and geographical factors is explored, annual sunshine duration, annual mean air temperature, annual range of air temperature, annual mean relative humidity, annual precipitation amount are selected as input data. BP artificial neural network is built, and the best result is training 1200 times.When the relation between the reference value of middle-aged women' RV/TLC and geographical factors is explored, altitude, annual sunshine duration, annual mean air temperature, annual mean relative humidity are selected as input data. BP artificial neural network is built, and the best result is training 1500 times.When the relation between the reference value of middle-aged FRC and geographical factors is explored, annual sunshine duration, annual mean air temperature, annual range of air temperature, annual mean relative humidity, annual precipitation amount are selected as input data. BP artificial neural network is built, and the best result is training 700 times.Conclusion:To the country as a study area, the reference value f unknown points is obtained through mathematical models or BP artificial neural network based on 4343 regions selected. geographical distribution map of the reference value of medical indexes is precisely interpolated by GIS spatial analysis. Therefore, if geographical values are obtained in some area, the reference value of medical indexes can be obtained by mathematical models or BP artificial neural network accurately; we can also obtain the reference value of medical indexes from the geography Trend-surface distribution figure in any area.