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Indian Pediatr 2016;53: 779-780 |
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Spirometry Reference Equations for Indian
Children: Create Local or Go Global?
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* Lokesh Guglani and
#Daniel J Weiner
From the *Division of Pulmonology,
Allergy/Immunology, Cystic Fibrosis and Sleep (PACS), Department of
Pediatrics, Emory University School of Medicine and Children’s
Healthcare of Atlanta, Atlanta GA; and #Division of Allergy, Immunology
and Pulmonology, Department of Pediatrics, Children’s Hospital of
Pittsburgh of UPMC, Pittsburgh PA.
Email:
[email protected]
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S pirometry is a valuable tool for assessing
patterns of lung disease, monitoring changes over time, and measuring
response to interventions. Interpretation of spirometry data requires
the use of reference values based on the subject’s age, gender, height
and ethnicity. Until recently, predicted lung function equations were
plagued by their inability to account for the age-related increase in
lung function in early childhood and adolescence, and for the decline in
lung function with advancing age in the geriatric population. This led
to the widespread use of separate sets of lung function normative data
for each age-group. However, each of these datasets was created with
differing inclusion criteria, equipment, measurement conditions, and
acceptability standards. These differences become especially apparent as
subjects move from one age category to the next and there are
significant shifts in predicted values noted in changing from one
dataset to another. Body composition varies between different
ethnicities [1] and environmental factors have a direct correlation with
lung function [2]. Hence, having a set of reference values for
spirometry that have been derived from the population being tested makes
the most sense. However, with increased global migration, increasing
number of children with mixed race, and research collaborations between
institutions, it can be helpful to avoid all the variability from
different predicted values for spirometry.
The Global Lung Function Initiative (GLI) started by
Quanjer [3] and Stanojevic [4] has sought to overcome the above
limitations and provided a universal set of reference values for
spirometry that cover ages 3 to 95 years. They are based on spirometry
results from 97 579 healthy, lifelong non-smokers from 72 centers in 33
countries and included the following ethnicities: Caucasians (n=57
395), African Americans (n=3 545), North Asian (n=4 992)
and South-East Asian (n=8 255). These equations were based on the
LMS (lambda, mu, sigma) method, which is an extension of non-linear
regression analysis that has also been used for creating growth charts.
This allows the GLI equations to model the developmental changes that
happen during adolescence and also provides appropriate age-dependent
lower limits of normal (LLN). GLI also allows for results to be
expressed as percent predicted, Z-scores or percentiles. A number
of studies done in various populations [5,6] and ethnicities around the
world [2,7,8] have validated the use of GLI equations across various
age-groups. However, the GLI dataset does not have adequate
representation from Africa, Latin America and the Indian subcontinent
[9].
The manuscript by Chhabra, et al. [10] in this
issue highlights the importance of adapting normative data reference
equations to the adolescent growth spurt in Indian children and its
direct impact on lung function. Recognizing that increase in lung
function during adolescence is non-linear due to accelerated growth,
they have used linear and non-linear models in their multiple regression
analysis. They showed that non-linear regression matched better with the
observed lung function values and were able to derive the lung function
equations using multivariate analysis. They were unable to create an
equation for FEV1/FVC ratio as there was no relationship with any of the
independent variables. This may be a significant limitation, since the
assessment of FEV1/FVC ratio is important for defining an obstructive
pattern on spirometry. Having normative data for Indian children is
especially important in the wake of increasing recognition of effects of
ambient air pollution in urban areas of India on the growing lungs of
adolescents [11.12]. Limitations of this study include being limited to
a single school from where all the subjects were recruited, and not
providing LLN values. In comparison, the GLI dataset includes 8 255
subjects of South East Asian origin, but did not include enough subjects
from the Indian subcontinent. It may be interesting to compare the GLI
equations to those proposed by Chhabra, et al. [10], and see if
any differences can be noted during adolescence.
Finally, it raises the question of which spirometry
reference values should a pulmonary function testing (PFT) facility use?
Most spirometry equipment comes with reference equations selected by the
manufacturer and it is important for the end-user to select appropriate
reference sets and to provide that information on each spirometry
result. Among the 300 published reference sets that are available, only
the GLI dataset provides the expanded age span coverage, the wider
spectrum of ethnicities and the adjustment for adolescent growth spurt
and other age-related changes. While it would be ideal for every PFT lab
to generate their own normative data, it is generally not feasible and
would make comparisons between different labs more difficult. Hence, it
would be important to conduct additional studies to generate normative
data using GLI methodology and to understand the accuracy of GLI
equations for predicting the lung function of Indian children compared
to the conventionally used equations.
Funding: None; Competing interest: None
stated.
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