Nomogram of Six second Spirometric Manoeuvre – FEV6, FEV1/FEV6 and other Spirometric Variables from a Sample of Healthy Bangladeshi Adults

Nirmal Kanti Sarkar1, Md. Khairul Hassan Jessy2, Syed Rezaul Huq3,
Md. Khairul Anam3, S. M. Abdur Razzak3, Nihar Ranjan Saha3, Bipul Kanti Biswas3,Jalal Mohsen Uddin4, Moumita Roy4

Abstract:
Introduction: Assessment of airway obstruction plays a key role in the
diagnosis and management of chronic obstructive pulmonary disease (COPD),
asthma and other obstructive airway diseases. Spirometry is gold standard
for the diagnosis of both airway obstruction and restrictive lung disease.
But the standard FVC has the problem of being dependent on expiratory
time. Six-second FVC maneuver (FEV6) makes spirometry easier, less
exhausting and enhance the reproducibility of the test. There was no previous
work in our country on FEV6. So, to plot a nomogram of six-second FVC
maneuver (FEV6) for Bangladeshi adults was time demanding.
Methods: This cross-sectional study was carried out from January 2010 to
December 2010 among 1035 healthy volunteers. The participants were lifetime
non-smoker and without any respiratory complaints. Data were collected from
community, during health camp and from volunteers at NIDCH. Spirometry
was done by a handheld spirometer. Two hundred and fifty five subjects were
discarded due to faulty maneuver. Linear and multiple regression analysis were
performed for plotting nomogram by using age, height and weight as the
independent variables and FVC, FEV1, FEV6 as the dependent variables.
Unpaired ‘t’ test was done. Multiple regression analysis was done to measure
the predicted values of nomogram of the study. Results were presented for male
and female. For statistical analysis, statistical software (SPSS 16.0) was used.
Result: The lung function values showed a linear positive correlation with
height, weight and age. FVC, FEV1 and FEV6 increases with increasing height.
Males showed higher values of lung function variables than female. Forward
stepwise regression analysis was done using age, height and weight as
independent variable. Strong correlation was found between lung function
values and the independent variables. Height showed the highest correlation.
The regression equations for lung function variables were determined for
males and females considering height as independent variable. The lung
function values showed a linear positive correlation with height,
Conclusion: Height is the best predictor for Spirometry. Age and weight
also correlate with spirometry but less predictive in comparison to height.
Keywords: Lung function tests, Spirometry.

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