

ORIGINAL ARTICLE 

Year : 2018  Volume
: 11
 Issue : 1  Page : 38 


Regression Equations for Peak Expiratory Flow Rates for Children (8–12 years) in Ernakulam District
Jomon Mathew John
Department of Paediatrics, Azeezia Medical College, Kollam, Kerala, India
Date of Web Publication  8May2018 
Correspondence Address: Jomon Mathew John House No. 44/2398  Nirmal Enclave, Santhipuram Road, Palarivattom, Ernakulam  682 025, Kerala India
Source of Support: None, Conflict of Interest: None  Check 
DOI: 10.4103/MJDRDYPU.MJDRDYPU_20_17
Objective: The objective of this study was to study the peak expiratory flow rate (PEFR) in healthy school going children between 8 and 12 years in Ernakulum district and derive a regression equation to predict expected PEFR. Methods: A crosssectional study was done in schools of Ernakulum among healthy children between 8 and 12 years of age. Pro formas and consent forms were distributed to schools who gave their consent for participating in this study. Eligible children were examined, anthropometric measures were taken, and PEFR values were recorded using a MiniWright peak flow meter. The best of the three recordings was taken to compare against each variable. Statistical analysis was carried out using statistical package, SPSS (version 22.0.0.0). Results: A total of 954 students between 8 and 12 years were studied, of which 482 were boys and 472 were girls. The scatter diagram for PEFR and age, height, weight, body mass index, and chest circumference for boys and girls suggests that there is a significant positive correlation between PEFR and the studied variables. Based on the multiple regression analysis, only age and height have a significant effect on PEFR for both males and females. The regression equation based on both height and age is PEFR = −150.290 + 1.811 (height) + 10.831 (age) for males and PEFR = −149.623 + 1.813 (height) + 8.329 (age) for females. Conclusion: Among different factors affecting PEFR, height and age correlate better with PEFR than the other variables studied. The derived regression equations can be used to predict the regional reference value.
Keywords: Asthma, peak expiratory flow rate, regression equations
How to cite this article: John JM. Regression Equations for Peak Expiratory Flow Rates for Children (8–12 years) in Ernakulam District. Med J DY Patil Vidyapeeth 2018;11:38 
How to cite this URL: John JM. Regression Equations for Peak Expiratory Flow Rates for Children (8–12 years) in Ernakulam District. Med J DY Patil Vidyapeeth [serial online] 2018 [cited 2019 Aug 25];11:38. Available from: http://www.mjdrdypv.org/text.asp?2018/11/1/3/232064 
Introduction   
Peak expiratory flow rate (PEFR) is the maximum flow rate generated during forceful exhalation, starting from full lung inflation whereas forced expiratory volume over 1 second is a dynamic measure of flow used in formal spirometry. Although spirometry does provide useful information for the management of respiratory tract illnesses in pediatric practice, implementation is relatively expensive and restricted to hospital settings. In contrast, PEFR can be measured using relatively inexpensive peak flow meters and is of value in identifying and assessing the degree of airflow limitation of individuals. Peak flow rate can be measured at home and is beneficial to patients for both longterm and shortterm monitoring. A recent metaanalysis found peak flow rate monitoring to be equivalent to symptombased asthma action plans.^{[1]} It also provides objective data for clinicians to base their therapeutic decisions. PEFR becomes clinically useful only if it is compared with normative/standard data as airflow limitation is diagnosed objectively if PEFR is less than 80% of the reference value. Hence, regional reference values need to be established for proper assessment and diagnosis. The American Thoracic Society has recommended that laboratories should use the published reference equations (based on crosssectional data) that most closely describe the populations tested in their laboratories.^{[2]}
Pulmonary function is known to vary considerably between different regional and ethnic groups, residing within the same country. India, being a subcontinent, changes in pulmonary function can occur from one region to another. Therefore, it is essential to have reference standard for each ethnic group of region for better evaluation of pulmonary function. Unfortunately, PEFR values for normal children are not available in all parts of India. Nomograms predicting the PEFR from height are available for western children. Similar data are available in Indian adults and children from North India, Tamil Nadu, and Karnataka,^{[3],[4],[5],[6]} but no adequate data is available for Ernakulam, Kerala.
Objective
The objective of this study was to study the PEFR in healthy school going children between 8 and 12 years in Ernakulam district and derive a regression equation to predict expected PEFR.
Materials and Methods   
A crosssectional study was done in schools of Ernakulam, among healthy children between 8 and 12 years of age, between April 2014 and April 2015.
Exclusion criteria
History of wheeze, nocturnal cough, allergy, tuberculosis (TB) contactHistory of acute respiratory tract illness in the preceding 7 daysFamily history of asthma, TB, allergyPresence of any major illness affecting cardiovascular system (CVS), respiratory system, central nervous system (CNS), gastrointestinal tract, etc.Presence of cough with/without feverPresence of structural anomalies of chest, chest retractionsPresence of rales, wheeze on auscultation.
Once the Institutional Ethical Committee had approved the study, schools in Ernakulam district were contacted requesting their participation. Those schools who gave consent were visited one at a time, after taking prior appointment with the principal.
On the first visit, an appeal was distributed among students that provided written information on the intention of the survey and requested a written consent from parents for the participation of each student in the survey as well as a pro forma. Classrooms and school environment were noted. During the second visit, all the children in the specified age group attending the school who satisfied the study criteria and had obtained the consent were studied. The children were taken as a group into a separate place for examination. The age to the completed years and sex of each child were noted.
The following measurements were taken: weight to the nearest kilogram while standing with light clothing on an electronic scale, height to the nearest centimeter while standing without shoes against a stadiometer, and chest circumference in maximum inspiration to the nearest centimeter. Body mass index (BMI) was calculated using the formula:
BMI = Weight in kg/height in m ^{2}
Each child was clinically examined for the presence of cough, fever, chest retractions, chest deformities, wheezing, rales or any major illness affecting the CVS, respiratory system, GIT, and CNS. The procedure of PEFR measurement using the MiniWright peak flow meter was demonstrated to the child. Disposable mouthpiece was used for recording PEFR. The child was given two trials and the next three readings were noted down. The best of three readings was taken as the PEFR of the child. If the difference between any two readings was large, the probability of a faulty procedure was considered. The procedure will be demonstrated again to the child and a new set of readings was taken.
Statistical analysis
In this study, PEFR is the study variable. The gender and age distribution of the children are studied. The mean PEFR of boys and girls is calculated. The relationship between PEFR with age, height, weight, BMI, and chest circumference is also studied. Scatter diagrams are also drawn for PEFR with age, height, weight, BMI, and chest circumference. Multiple regression analysis is conducted to suggest a statistical model for PEFR based on age, height, weight, BMI, chest circumference. The estimated multiple regression can be used to predict the values of the PEFR based on these variables  age, height, weight, BMI, chest circumference.
In all analyses, significance level is taken to be 0.05 (i.e., if the P < 0.05, reject the null hypothesis or it can be concluded that the hypothesis is statistically significant). Statistical analysis was carried out using statistical package, SPSS (version 22.0, IBM, United States).
Results   
Nine hundred and fiftyfour students of both sexes between 8 and 12 years were analyzed for PEFR values. 482 were boys and 472 were girls [Table 1]. The scatter diagram for PEFR and age, height, weight, BMI, and chest circumference for boys and girls [Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5], [Figure 6], [Figure 7], [Figure 8], [Figure 9], [Figure 10] suggests that there is a positive relationship between PEFR and age, height, weight, BMI, and chest circumference. Furthermore, there seems to be no outliers present in the scatter plot. As the correlation was more robust with height and age, regression equations were constructed separately for boys and girls using these variables.  Figure 3: Scatter plot for peak expiratory flow rate and weight for males
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 Figure 4: Scatter plot for peak expiratory flow rate and weight for females
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 Figure 5: Scatter plot for peak expiratory flow rate and height for males
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 Figure 6: Scatter plot for peak expiratory flow rate and height for females
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 Figure 7: Scatter plot for peak expiratory flow rate and body mass index for males
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 Figure 8: Scatter plot for peak expiratory flow rate and body mass index for females
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 Figure 9: Scatter plot for peak expiratory flow rate and chest circumference for males
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 Figure 10: Scatter plot for peak expiratory flow rate and chest circumference for females
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Simple regression equation based on age is PEFR = 8.696 + 19.902 (age) for males and PEFR = 7.959 + 17.521 (age) for females.
Simple regression equation based on height is PEFR = −185.666 + 2.859 for males (height) and PEFR = −176.810 + 2.617 for females (height).
Multiple regression equation based on both age and height is PEFR = −150.290 + 1.811 (height) + 10.831 (age) for males and PEFR = −149.623 + 1.813 (height) +8.329 (age) for females
Discussion   
In the present study, PEFR values were measured in a large number of children between 8 and 12 years so that resulting PEFR values derived would be better representation of the widely variable PEFRs that occur in different children belonging to the same age group. PEFR values increased in linear relation to age and height. Similar to our study results, many other authors have also found a significant positive correlation of PEFR with age and height, out of which height has been maximally correlated with PEFR [Table 2]. According to the current study, BMI accounts for only 25% of the variability in PEFR which is in contrary to the findings of a study done by Sudha et al.^{[7]} on correlation of nutritional status and PEFR in normal South Indian children between 6 and 10 years. The authors have reported a significant correlation between BMI, skin fold thickness with PEFR, thereby concluding that excessive deposition of fat in children is associated with the decrease in PEFR values.
A standardized comparison of predicted PEFR values from the present study for three different height was made with the PEFR values for the same height from the following previously published studies [Table 3]. Parmar et al.^{[8]} studied PEFR values in healthy north Indian school children, which were similar to the findings from the western countries. Malik et al.^{[9],[10]} observed the PEFR from Punjab, north Indian school children and found that the height standardized value of PEFR showed no ruralurban differences. Kashyap et al.^{[11]} measured the PEFR of healthy tribal children living at high altitude in the Himalayas and found that the values are comparable with those of north Indian urban children. Sharma et al.^{[12]} studied PEFR measurements in rural children of Rajasthan and found that they were lower than those reported for Caucasian and urban Indian children of the same height. Taksande et al.^{[13]} measured PEFR in 1078 healthy rural school going children living in Wardha district, Maharashtra, using MiniWright peak flow meter and compared it with anthropometric measurements. A positive correlation was seen between age, height, weight, and PEFR and he also concluded that boys had higher values than girls at all heights.  Table 3: A comparison of PEFR between present study and previous studies
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Comparing our data with previously published values showed that PEFR measurements of children in Ernakulam district are lower than those reported for North Indian and other South Indian children of the same height.
Prediction formulas derived from statistical analysis:
Regression equation based on age is PEFR = 8.696 + 19.902 (age) for males and PEFR = 7.959 + 17.521 (age) for females
The above equation is derived from the regression analysis using age as the only explanatory variable and can be interpreted as “for a unit increase in age, PEFR for males increases by 19.902 units and 17.521 units for females.”
Regression equation based on height is PEFR = −185.666 + 2.859 (height) for males and PEFR = −176.810 + 2.617 (height) for females.
The above equation is derived from the regression analysis using height as the only explanatory variable and can be interpreted as “for a unit increase in height, PEFR for males increases by 2.859 units and 2.617 units for females.”
Regression equation based on both height and age is PEFR = −150.290 + 1.811 (height) + 10.831 (age) for males and PEFR = −149.623 + 1.813 (height) + 8.329 (age) for females.
The above equation is derived from the regression analysis using height and age as the only explanatory variable and can be interpreted as “for a unit increase in age, PEFR increases by 10.831 units for males and 8.329 units for females” and “for a unit increase in height, PEFR increases by 1.811 units for males and 1.813 units for females.”
For example: A 10yearold boy weighing 34 kg and 135 cm tall, walked into your OPD with complaints of cough more during early morning and evening hours. His PEFR is measured to be 190. To estimate his expected PEFR and compare with the observed value, substitute his age and height in the above formula.
−150.290 + 1.811× (135) + 10.831× (10) = 202.5
The observed value can either be plotted on the nomogram or compared with the derived mean PEFR for his age and height which is 210.1 ± 38.9 and 202.4 ± 40.8, respectively.
From the coefficients table of multiple regression analysis [Table 4], it can be observed that only age (B = 11.368, P = 0.000 for males and B = 7.764, P = 0.000 for females) and height (B = 1.965, P = 0.038 for males and B = 2.733, P = 0.003 for females) have significant effect on PEFR for both males and females.
Conclusion   
There is a significant positive correlation between PEFR and the studied variables; however, the correlation is more robust with regard to age and height. The derived regression equations can be used to estimate the expected PEFR values of children between 8 and 12 years in Ernakulam district.
Financial support and sponsorship
Nil.
Conflicts of interest
There are no conflicts of interest.
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[Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5], [Figure 6], [Figure 7], [Figure 8], [Figure 9], [Figure 10]
[Table 1], [Table 2], [Table 3], [Table 4]
