Estimation of peak expiratory flow rate in young Indians.

Q4 Health Professions Revista Pesquisa em Fisioterapia Pub Date : 2021-11-17 DOI:10.17267/2238-2704rpf.v11i4.3874
S. Kale, Khyati Bhatt, Medha Deo
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引用次数: 1

Abstract

INTRODUCTION: Peak expiratory flow rate (PEFR) is commonly used to monitor the progression of respiratory diseases as it gives good information about the status of airways. A good amount of research is going across the world to establish a local prediction equation. The joint task force of the American thoracic society and European Respiratory Society has promoted research in this regard. In India, data derived from the Caucasian population are still used for PEFR. OBJECTIVE: To verify the relationship between PEF levels and the variables age, sex, anthropometric and body surface area, and establish the regression equation for young Indian adults. METHODS:  A cross-sectional observational study was conducted in 15-25 years aged 1000 subjects from the Metropolitan region of Mumbai. Pearson’s correlation coefficient was used to understand the relation of anthropometric parameters and PEFR. Multivariate regression analysis was done for establishing a prediction equation (Alpha 5%).  RESULTS: Age and all anthropometric parameters were correlated with PEFR. The mean PEFR of the male population was 515 ml/sec, whereas, for females, it was 399 ml/sec, for PEFR highest correlation was observed with BSA (.696) followed by weight (.667), height (.630), age (.504) whereas BMI shown lowest correlation coefficient (.445). PEFR had the best significance with age, BSA, Height, and BMI. It had less significance with weight. In females, PEFR had the best significance with Height, weight, BMI, and Age. CONCLUSION: Gender-wise differences exist in PEFR. Hence gender-specific equations are needed for the estimation of PEFR.
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年轻印度人呼气流速峰值的估计。
呼气流量峰值(PEFR)通常用于监测呼吸系统疾病的进展,因为它提供了气道状态的良好信息。世界各地正在进行大量的研究,以建立一个局部预测方程。美国胸科学会和欧洲呼吸学会的联合工作组已经推动了这方面的研究。在印度,来自高加索人口的数据仍然用于PEFR。目的:验证PEF水平与年龄、性别、人体测量、体表面积等变量的关系,建立印度青年成人PEF水平的回归方程。方法:对孟买大都市区15-25岁的1000名受试者进行了横断面观察研究。采用Pearson相关系数分析人体测量参数与PEFR的关系。通过多元回归分析建立预测方程(Alpha 5%)。结果:年龄和所有人体测量参数与PEFR相关。男性人群PEFR均值为515 ml/sec,女性人群PEFR均值为399 ml/sec, PEFR与BSA相关性最高(0.696),其次是体重(0.667)、身高(0.630)、年龄(0.504),BMI相关性最低(0.445)。PEFR与年龄、BSA、身高、BMI的相关性最强。它对体重的影响较小。在女性中,PEFR与身高、体重、BMI和年龄的相关性最好。结论:PEFR存在性别差异。因此,估计PEFR需要有性别特点的方程式。
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来源期刊
Revista Pesquisa em Fisioterapia
Revista Pesquisa em Fisioterapia Health Professions-Occupational Therapy
CiteScore
0.30
自引率
0.00%
发文量
21
审稿时长
4 weeks
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