Predictive equations using regression analysis of pulmonary function for healthy children in Northeast China. 2013

Ya-Nan Ma, and Jing Wang, and Guang-Hui Dong, and Miao-Miao Liu, and Da Wang, and Yu-Qin Liu, and Yang Zhao, and Wan-Hui Ren, and Yungling Leo Lee, and Ya-Dong Zhao, and Qin-Cheng He
Department of Biostatistics and Epidemiology, School of Public Health, China Medical University, Shenyang, Liaoning Province, PR China.

BACKGROUND There have been few published studies on spirometric reference values for healthy children in China. We hypothesize that there would have been changes in lung function that would not have been precisely predicted by the existing spirometric reference equations. The objective of the study was to develop more accurate predictive equations for spirometric reference values for children aged 9 to 15 years in Northeast China. RESULTS Spirometric measurements were obtained from 3,922 children, including 1,974 boys and 1,948 girls, who were randomly selected from five cities of Liaoning province, Northeast China, using the ATS (American Thoracic Society) and ERS (European Respiratory Society) standards. The data was then randomly split into a training subset containing 2078 cases and a validation subset containing 1844 cases. Predictive equations used multiple linear regression techniques with three predictor variables: height, age and weight. Model goodness of fit was examined using the coefficient of determination or the R(2) and adjusted R(2). The predicted values were compared with those obtained from the existing spirometric reference equations. The results showed the prediction equations using linear regression analysis performed well for most spirometric parameters. Paired t-tests were used to compare the predicted values obtained from the developed and existing spirometric reference equations based on the validation subset. The t-test for males was not statistically significant (p>0.01). The predictive accuracy of the developed equations was higher than the existing equations and the predictive ability of the model was also validated. CONCLUSIONS We developed prediction equations using linear regression analysis of spirometric parameters for children aged 9-15 years in Northeast China. These equations represent the first attempt at predicting lung function for Chinese children following the ATS/ERS Task Force 2005 guidelines on spirometry standardization.

UI MeSH Term Description Entries
D008168 Lung Either of the pair of organs occupying the cavity of the thorax that effect the aeration of the blood. Lungs
D008297 Male Males
D012129 Respiratory Function Tests Measurement of the various processes involved in the act of respiration: inspiration, expiration, oxygen and carbon dioxide exchange, lung volume and compliance, etc. Lung Function Tests,Pulmonary Function Tests,Function Test, Pulmonary,Function Tests, Pulmonary,Pulmonary Function Test,Test, Pulmonary Function,Tests, Pulmonary Function,Function Test, Lung,Function Test, Respiratory,Function Tests, Lung,Function Tests, Respiratory,Lung Function Test,Respiratory Function Test,Test, Lung Function,Test, Respiratory Function,Tests, Lung Function,Tests, Respiratory Function
D002648 Child A person 6 to 12 years of age. An individual 2 to 5 years old is CHILD, PRESCHOOL. Children
D002681 China A country spanning from central Asia to the Pacific Ocean. Inner Mongolia,Manchuria,People's Republic of China,Sinkiang,Mainland China
D003710 Demography Statistical interpretation and description of a population with reference to distribution, composition, or structure. Demographer,Demographic,Demographic and Health Survey,Population Distribution,Accounting, Demographic,Analyses, Demographic,Analyses, Multiregional,Analysis, Period,Brass Technic,Brass Technique,Demographers,Demographic Accounting,Demographic Analysis,Demographic Factor,Demographic Factors,Demographic Impact,Demographic Impacts,Demographic Survey,Demographic Surveys,Demographic and Health Surveys,Demographics,Demography, Historical,Demography, Prehistoric,Factor, Demographic,Factors, Demographic,Family Reconstitution,Historical Demography,Impact, Demographic,Impacts, Demographic,Multiregional Analysis,Period Analysis,Population Spatial Distribution,Prehistoric Demography,Reverse Survival Method,Stable Population Method,Survey, Demographic,Surveys, Demographic,Analyses, Period,Analysis, Demographic,Analysis, Multiregional,Demographic Analyses,Demographies, Historical,Demographies, Prehistoric,Distribution, Population,Distribution, Population Spatial,Distributions, Population,Distributions, Population Spatial,Family Reconstitutions,Historical Demographies,Method, Reverse Survival,Method, Stable Population,Methods, Reverse Survival,Methods, Stable Population,Multiregional Analyses,Period Analyses,Population Distributions,Population Methods, Stable,Population Spatial Distributions,Prehistoric Demographies,Reconstitution, Family,Reconstitutions, Family,Reverse Survival Methods,Spatial Distribution, Population,Spatial Distributions, Population,Stable Population Methods,Technic, Brass,Technique, Brass
D005260 Female Females
D006262 Health The state of the organism when it functions optimally without evidence of disease. Individual Health,Normalcy,Normality,Health, Individual,Normalcies,Normalities
D006801 Humans Members of the species Homo sapiens. Homo sapiens,Man (Taxonomy),Human,Man, Modern,Modern Man
D000293 Adolescent A person 13 to 18 years of age. Adolescence,Youth,Adolescents,Adolescents, Female,Adolescents, Male,Teenagers,Teens,Adolescent, Female,Adolescent, Male,Female Adolescent,Female Adolescents,Male Adolescent,Male Adolescents,Teen,Teenager,Youths

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