Wun Y T, Chan Mark S H, Wong Nai Ming, Kong Albert Y F, Lam Tai Pong
Department of Family Medicine and Primary Care, The University of Hong Kong, Hong Kong.
J Asthma. 2013 Feb;50(1):39-44. doi: 10.3109/02770903.2012.743152. Epub 2012 Nov 22.
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.
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.
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) = 1.810256ln(height) + 0.038297age - 3.734139 for males and ln(PEFR) = 1.525509ln(height) + 0.033275age - 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%.
The body height was a stronger determinant than age for PEFR. The predicted PEFR with these determinants bear a curvilinear relationship.
呼气峰值流速(PEFR)在不同人群及不同时间存在差异。自2004年起引入了新型欧盟标准的小型赖特流量计。本研究采用新的标准更新了香港6至19岁中国儿童及青少年的PEFR列线图。
从34个基层医疗诊所(患者同伴/儿童)和4所学校招募便利样本。为医生举办标准化研讨会,并在数据收集前向学生演示流量计的正确使用方法。使用全新的流量计。对于每种性别,采用线性回归模型确定PEFR与年龄和身高变量之间的关系。使用开源软件PyNomo生成列线图。
排除66名有既往/当前呼吸道疾病、心脏病、数据不完整及测试不规范的参与者后,收集到798名男性和794名女性的PEFR数据。PEFR与年龄呈线性关系,但与身高呈曲线关系。男性预测PEFR的回归方程为ln(PEFR)=1.810256ln(身高)+0.038297年龄 - 3.734139,女性为ln(PEFR)=1.525509ln(身高)+0.033275年龄 - 2.368592。构建了相应的列线图。在基层医疗中对230名患者进行了测试;9.6%(12名男性和10名女性)的PEFR低于预测值≥20%。
对于PEFR,身高比年龄是更强的决定因素。这些决定因素预测的PEFR呈曲线关系。