OBJECTIVE: Peak expiratory flow rates (PEFRs) differ among populations and between times. The new EU scale of the mini-Wright flow-meter has been introduced since 2004. This study updated the PEFR nomograms with the new scale for Chinese children and adolescents (aged 6-19 years) in Hong Kong. METHODS: A convenience sample was recruited from 34 primary care practices (patients' companions/children) and four schools. Standardization workshops were run for the physicians, and the proper use of the flow-meter was demonstrated to students prior to the data collection. Brand new meters were used. For each sex, the linear regression model was used to determine the relationship between PEFR and the variables of age and body height. The open-source software PyNomo was used to generate the nomograms. RESULTS: After excluding 66 participants with past/current history of respiratory tract diseases, heart disease, incomplete data, and poor effort, PEFRs were collected from 798 males and 794 females. The PEFR had a linear relationship with age but a curvilinear relationship with height. The regression equations for predicted PEFR were ln(PEFR) = 1.810256*ln(height) + 0.038297*age - 3.734139 for males and ln(PEFR) = 1.525509*ln(height) + 0.033275*age - 2.368592 for females. The corresponding nomograms were constructed. They were tested with 230 patients in primary care; 9.6% (12 males and 10 females) had PEFR less than the predicted value by ≥20%. CONCLUSION: The body height was a stronger determinant than age for PEFR. The predicted PEFR with these determinants bear a curvilinear relationship.
OBJECTIVE: Peak expiratory flow rates (PEFRs) differ among populations and between times. The new EU scale of the mini-Wright flow-meter has been introduced since 2004. This study updated the PEFR nomograms with the new scale for Chinese children and adolescents (aged 6-19 years) in Hong Kong. METHODS: A convenience sample was recruited from 34 primary care practices (patients' companions/children) and four schools. Standardization workshops were run for the physicians, and the proper use of the flow-meter was demonstrated to students prior to the data collection. Brand new meters were used. For each sex, the linear regression model was used to determine the relationship between PEFR and the variables of age and body height. The open-source software PyNomo was used to generate the nomograms. RESULTS: After excluding 66 participants with past/current history of respiratory tract diseases, heart disease, incomplete data, and poor effort, PEFRs were collected from 798 males and 794 females. The PEFR had a linear relationship with age but a curvilinear relationship with height. The regression equations for predicted PEFR were ln(PEFR) = 1.810256*ln(height) + 0.038297*age - 3.734139 for males and ln(PEFR) = 1.525509*ln(height) + 0.033275*age - 2.368592 for females. The corresponding nomograms were constructed. They were tested with 230 patients in primary care; 9.6% (12 males and 10 females) had PEFR less than the predicted value by ≥20%. CONCLUSION: The body height was a stronger determinant than age for PEFR. The predicted PEFR with these determinants bear a curvilinear relationship.