Indian J Med Res 122, August 2005, pp 153-164
Applicability of commonly used Caucasian prediction equations
for spirometry interpretation in India
A.N. Aggarwal, Dheeraj Gupta, Digamber Behera & S.K. Jindal
Department of Pulmonary Medicine, Postgraduate Institute of Medical Education & Research
Chandigarh, India
Received December 30, 2003
Background & objectives: The applicability of Caucasian prediction equations in interpreting
spirometry data in Indian patients has not been studied. The present study was undertaken to
see if Caucasian and north Indian prediction equations can be used interchangeably while
interpreting routine spirometric data.
Methods: Forced vital capacity (FVC), forced expiratory volume in first second (FEV 1), and
FEV1/FVC ratio were recorded from 14733 consecutive spirometry procedures in adults. Predicted
values and lower limits of normality were calculated using regression equations previously derived
at this centre, and four commonly used Caucasian equations described by Knudson, Crapo,
European Community for Coal and Steel (ECCS) and the Third National Health and Nutrition
Examination Survey (NHANES III). For men, 90 per cent of predicted values were also derived.
Kappa estimates were used to study agreement, and Bland Altman analysis was performed to
quantify differences, between interpretations from Indian and Caucasian equations. Receiver
operating characteristic (ROC) curves were constructed to assess utility of using a fixed
percentage of Caucasian predicted values in categorizing FVC or FEV1 as abnormal.
Results: The use of Caucasian prediction equations (and 90% of predicted values in men) resulted
in poor agreement with Indian equation in most height and age categories among both men and
women. Bland Altman analysis revealed a large bias and wide confidence limits between
Caucasian and Indian equations, indicating that the two cannot be used interchangeably. ROC
analysis failed to yield good results with use of any single fixed percentage of Caucasian predicted
value while categorizing FVC or FEV1.
Interpretation & conclusion: Our results showed that the use of Caucasian prediction equations,
or a fixed percentage of their predicted values, resulted in misinterpretation of spirometry data
in a significant proportion of patients. There is a need to assess performance of more than one
regression equation before choosing any single prediction equation.
Key words Agreement - ethnic differences - prediction equation - respiratory function tests
153
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INDIAN J MED RES, AUGUST 2005
The most important step in diagnosing abnormality
of lung function in individuals is to define whether
they are within or outside the healthy subjects range.
For this purpose, the observed value of a patient is
usually compared to a reference value obtained from
prediction equations derived from studies on healthy
people. It is often recommended that pulmonary
function laboratories should employ prediction
equations derived from subjects with a similar ethnic
background as the patients1. Difficulties arise when
either the patient population being investigated at a
particular centre is markedly heterogeneous in terms
of ethnic composition, or when no prediction equations
are available for use in the patient population
predominantly investigated at a centre. The problem
gets compounded when computerized equipment used
for pulmonary function testing provides a numeric
output of results derived from a limited (and often
only a single) set of prediction equations incorporated
into the software. Often these equations are totally
alien to the patient population being investigated. For
example, the numeric output of results from
spirometers built in the West, which are commonly
used in India, is largely derived from Caucasian
prediction equations.
Though it is perhaps best to use different prediction
equations for patients from different ethnic
backgrounds, it is usually not possible to do so because
of (i) non availability of spirometers with built-in
Indian prediction equations, and/or modification of
existing software, and (ii) extra time and manpower
requirements for manual calculation of predicted
values for each patient prior to actual interpretation
of spirometry results. Clinicians in most instances,
and even researchers, rely on the results obtained from
Caucasian prediction equations incorporated into the
software of spirometers, even though reference
equations are available from several areas of India2,3.
As an alternative, one can use a fixed percentage of
the value derived from a Caucasian prediction equation
when interpreting results for patients from a different
ethnic background4. The percentages so recommended
are largely arbitrary, and there has been no systemic
analysis to show as to how well these approximate
the predicted lung function in the patient population
being studied. The present study was designed to
assess, both qualitatively and quantitatively, the
impact of applying commonly used Caucasian
prediction equations for the interpretation of results
of routine spirometry performed in patients in India.
Material & Methods
Study population: The Pulmonary Function
Laboratory at the Postgraduate Institute of Medical
Education & Research, Chandigarh, north India, offers
spirometry as a routine service to both outpatients and
inpatients, and approximately 3500 procedures are
performed annually. Records of all consecutive
patients referred for spirometry over a four year period
(1999-2002) were retrieved. Reasons for performing
spirometry as well as other clinical details were not
analyzed further. Age, gender, height and spirometry
data were recorded for all patients using a computer
software previously developed by us5. A total of 15106
patients underwent spirometry during this period.
After exclusion of 22 incomplete records and 351
patients aged less than 15 yr, 14733 records were
available for further analysis. There were 8155 men
(age 15-97 yr, height 123-215 cm) and 6578 women
(age 15-90 yr, height 121-181 cm) (Table I).
Spirometry and its interpretation: All subjects
performed spirometry on a dry rolling seal spirometer
(Spiroflow; P.K. Morgan Ltd.; Kent, UK). Spirometric
indices such as forced vital capacity (FVC), forced
expiratory flow in first second (FEV 1) and peak
expiratory volume were measured using American
Thoracic Society guidelines, and the highest
measurements from among three technically
acceptable and reproducible maneuvers were
expressed at body temperature and pressure saturated
with water vapor 6. Height was measured using a
stadiometer and expressed to the nearest centimetre.
The spirometer calibration was frequently checked to
ensure performance. Predicted values for FVC, FEV 1
and FEV1/FVC ratio were generated using previously
defined prediction equations, as detailed below. Lower
limits of normal (LLN) for FEV1, FVC and FEV1/
FVC were calculated using lower 95 per cent
confidence limits derived from the regression equation
being used, and were computed as the difference
between the predicted value and 1.645 times the
standard error of estimate (SEE) or residual standard
deviation (RSD) of the regression equation1. Any
observed value lower than its corresponding LLN was
considered abnormal.
AGGARWAL et al: CAUCASIAN EQUATIONS FOR SPIROMETRY IN INDIA
155
Table I. Height and age distribution in the study population
Men
Women
Total
116 (1.4)
1453 (17.8)
4420 (54.2)
2013 (24.7)
153 (1.9)
1981 (30.1)
3754 (57.1)
814 (12.4)
28 (0.4)
1 (0.0)
2097 (14.2)
5207 (35.3)
5234 (35.5)
2041 (13.9)
154 (1.0)
15-24
25-34
35-44
45-54
55-64
65-74
>75
817 (10.0)
1010 (12.4)
1357 (16.6)
1771 (21.7)
1629 (20.0)
1185 (14.5)
386 (4.7)
647 (9.8)
1103 (16.8)
1472 (22.4)
1526 (23.2)
1074 (16.3)
611 (9.3)
145 (2.2)
1464 (9.9)
2113 (14.3)
2829 (19.2)
3297 (22.4)
2703 (18.3)
1796 (12.2)
531 (3.6)
Total
8155
6578
14733
Height (cm):
<150
151-160
161-170
171-180
> 180
Age (yr):
Values in parentheses are percentages
FEV 1, FVC and FEV 1/FVC ratio were used as
basic parameters to interpret spirometry data 1,5. A
spirometry record with FEV 1/FVC ratio less than the
LLN for that subject was categorized as having an
obstructive pattern. Although a true restrictive defect
can be diagnosed only on demonstration of a reduced
total lung capacity, a restrictive pattern was inferred
from spirometry results for categorization comparison
purposes only. A record with FVC less than the LLN,
associated with a normal FEV 1 /FVC ratio was
categorized as having a restrictive defect. Static lung
volumes and total lung capacity were not routinely
estimated in subjects having restrictive defects on
spirometry.
Prediction equations: Four sets of prediction
equations were used in this study. Regression
equations previously derived at this centre, and
routinely used at the Pulmonary Function Laboratory,
were considered as the gold standard7. These equations
were generated from spirometry studies performed on
962 healthy non smoking north Indian adults, aged
15-74 yr, using a water seal spirometer. Predicted
values were also derived from four other previously
described regression equations commonly used in
Caucasian subjects. The Crapo equations were derived
from 251 non smoking American subjects, aged
15-91 yr and residing in Utah 1400 m above sea level,
using a water seal spirometer8. The Knudson equations
were obtained from 746 American white non smoking
subjects, aged 8-90 yr and residing in Arizona, using
a pneumotachygraph device 9 . The European
Community for Coal and Steel (ECCS) equations are
summary equations derived for Caucasian subjects
aged 5-70 yr from previously published reference
equations 10. The NHANES III (the third National
Health and Nutrition Examination Survey) equations
are the most recently described of the four Caucasian
equations evaluated, and were derived from 2281
Caucasian subjects randomly selected from 81
American counties, using a dry rolling-seal
spirometer11. All five equations predict FEV1, FVC
and FEV1/FVC based on gender, age and height of a
subject as primary variables. All equations, except
the Crapo and ECCS equations, are nonlinear with
respect to age.
In addition, it has been suggested that reducing
corresponding Caucasian values by 10 per cent might
approximate predicted values for FEV1 and FVC in
north Indian men4; no similar guidelines exist for
women. Hence, LLN for each parameter was also
calculated after reducing predicted values obtained
from Crapo, Knudson, ECCS and NHANES III
equations by 10 per cent each. Thus nine expressions
each of LLN for FEV1, FVC and FEV1/FVC were
calculated for each subject (LLN from north Indian
equation, and actual and 90% of LLN each from
Crapo, Knudson, ECCS and NHANES III equations).
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INDIAN J MED RES, AUGUST 2005
Statistical analysis: Spirometry was interpreted for
each patient using each of the nine schemes for
defining LLN. Spirometry results (normal, obstructive
or restrictive patterns) obtained using north Indian and
various Caucasian equations were compared after
construction of contingency tables. Agreement
between classification of spirometry results using
north Indian and other equations were calculated using
the Kappa estimate12. Bootstrapping was employed
to check the robustness of Kappa estimate. For this
purpose, study population was resampled with
replacement 1000 times and Kappa estimates
recalculated in each instance; 95 per cent confidence
limits for Kappa estimate were then computed as the
2.5th and 97.5th percentile of the 1000 Kappa
estimates obtained for each set of north Indian and
Caucasian equations 13. The proportion of subjects
having different interpretations using north Indian and
another Caucasian equation was calculated for all
combinations of prediction strategies as a measure of
discordance. This group represented individuals whose
interpretation changed if another scheme for
calculating LLN was used in place of the standard
north Indian equation.
To assess how closely the LLNs obtained by
Caucasian equations match those obtained by the north
Indian equation, we calculated the difference (bias)
between values estimated by the north Indian and each
Caucasian equation. In order to assess if the values
determined by the different prediction equations can
be used interchangeably across different age and height
groups, we calculated the limits of agreement for each
set of north Indian and Caucasian equation 14. In
addition, we also evaluated if using a fixed percentage
of the Caucasian LLNs instead of north Indian LLNs
can result in useful approximations. For this purpose
we expressed the LLNs obtained by north Indian
equations as a percentage of LLNs obtained by
different Caucasian equations for both FVC and FEV1.
We then calculated the mean of this value for both
men and women in different age and height groups to
search for any consistent trends. Receiver-operating
characteristic (ROC) curves were also constructed to
assess the utility of using such a fixed percentage as
a discriminator in categorizing FVC or FEV 1 as
abnormal. For this purpose sensitivity and specificity
of each possible cut off percentage available from the
data was calculated in classifying observed FVC or
FEV 1 values as abnormal. The distribution of
probability for false positives (1 - specificity) versus
true positives (sensitivity) was plotted for each of the
four sets of comparison data to obtain ROC curves;
area under each curve was calculated using the
trapezoidal method15.
All mathematical computations and statistical
procedures were performed using the software SPSS
10.0 (SPSS Inc., Chicago, IL).
Results
Using north Indian prediction equations, 4018
(27.3%) and 3901 (26.5%) spirometry records were
interpreted as having obstructive and restrictive
defects respectively. Among the 6814 normal records,
2728 (40.0%), 961 (14.1%), 699 (10.3%) and 3584
(52.6%) were classified as being abnormal using the
Crapo, Knudson, ECCS and NHANES III equations
respectively (Table II). Misclassification was worst
for obstructive defects using the ECCS equation
(31.9%) and worst for restrictive defects using the
Knudson equation (21.0%) (Table II). Overall, the
Crapo, Knudson, ECCS and NHANES III equations
misclassified results in 23.1, 16.7, 14.7 and 29.1 per
cent subjects respectively. For men, corresponding
Kappa estimates of agreement were best for ECCS
equation and worst for NHANES III equation
(Table III). Among women, agreement was best with
Knudson and worst with NHANES III equation.
The Crapo and Knudson equation showed poorest
agreement and worst misclassification in tall men and
men at extremes of age; use of 90 per cent of predicted
LLN improved agreement for the Crapo, but not for
the Knudson, equations. The ECCS equation showed
fairly good agreement, and the NHANES III equation
showed poor agreement, across the entire range of age
and height for men. All four Caucasian equations
demonstrated poor agreement in women of all age and
height categories, with high misclassification rates
(Table III). Use of 90 per cent of predicted values in
men improved overall agreement when using Crapo
or NHANES III equation, but worsened it when using
ECCS or Knudson equation (Table III).
The Crapo and NHANES III equations consistently
overpredicted LLN for both FVC and FEV1 in men
and women in almost all age and height categories,
AGGARWAL et al: CAUCASIAN EQUATIONS FOR SPIROMETRY IN INDIA
157
Table II. Comparison of spirometry interpretation by Caucasian and north Indian prediction equations
North Indian 7
Normal
Obstruction
Restriction
Normal
Obstruction
Restriction
4086 (60.0)
622 (9.1)
2106 (30.9)
132 (3.3)
3761 (93.6)
125 (3.1)
0
412 (10.6)
3489 (89.4)
Knudson 9
Normal
Obstruction
Restriction
5853 (85.9)
278 (4.1)
683 (10.0)
448 (11.1)
3332 (82.9)
238 (5.9)
588 (15.1)
231 (5.9)
3082 (79.0)
ECCS 10
Normal
Obstruction
Restriction
6115 (89.7)
0
699 (10.3)
814 (20.3)
2735 (68.1)
469 (11.7)
185 (4.7)
0
3716 (95.3)
NHANES III11
Normal
Obstruction
Restriction
Total
3230 (47.4)
25 (0.4)
3559 (52.2)
6814
274 (6.8)
3140 (78.1)
604 (15.0)
4018
7 (0.2)
20 (0.5)
3874 (99.3)
3901
Crapo
8
Data presented as number of patients and respective percentages (in parenthesis)
ECCS, European Community for Coal and Steel; NHANES III, Third National Health and Nutrition Examination Survey
with the latter giving slightly better results in the
youngest subjects (Table IV). The differences between
predicted LLNs for FVC and FEV 1 increased with
height and decreased with age in both men and women.
Although use of 90 per cent of predicted LLN lowered
these differences in men, the limits of agreement still
remained wide, and were more than 150 ml in most
instances (Table IV).
The Knudson equation consistently underpredicted
LLN for FVC and FEV1 in short and old men, and
overpredicted them in women of all age and height
categories (Tables IV). The limits of agreement were
wide in all instances and were only marginally
improved by the use of 90 per cent of predicted LLN
in men.
The ECCS equation consistently overpredicted
LLN for FVC and FEV 1 for short men and most
women (Tables IV). The differences as well as limits
of agreement were worsened by use of 90 per cent of
predicted LLN in men.
A few important observations emerged on
expressing the LLN derived from north Indian
equations as a percentage of the corresponding LLN
derived from a Caucasian equation (Table V). Firstly,
the resulting percentages were much lower for women
as compared to men for all Caucasian equations in
most instances. Secondly, there was considerable
difference in the values obtained across various
categories of age and height. Thirdly, the percentages
were lower for FEV1 as compared to FVC. While the
LLN for FVC obtained by north Indian equation was
close to 90 per cent of LLN obtained by Caucasian
equations in men of several age and height groups,
the same was not true for FEV1. Fourthly, there was
also considerable difference between results obtained
from different equations. For example, the values
obtained by Knudson and ECCS equations were higher
than those obtained by north Indian equations in older
and shorter men; this trend was not observed with
the Crapo or the NHANES III equations (Table V).
Overall, no consistent percentages were noted for
any of the Caucasian equation used. Construction of
ROC curves also demonstrated that the use of a
fixed percentage of LLN from a Caucasian equation
was a poor discriminator in identifying abnormal
values of FVC or FEV1, as areas under the curves in
nearly all age and height groups were close to 0.5.
Areas under the ROC curve for the entire population
for Crapo, Knudson, ECCS and NHANES III
equations were 0.583, 0.589, 0.553 and 0.598
respectively for FVC, and 0.589, 0.593, 0.614 and
0.593 respectively for FEV1 (Fig.).
158
INDIAN J MED RES, AUGUST 2005
Table III. Kappa estimate of agreement, and percentage of misclassified results, for spirometry interpretation between north
Indian and Caucasian prediction equations.
Regression equation
Crapo 8
Age (yr)
Actual
<20
>20
90%*
<20
>20
Knudson 9
Actual
<20
>20
90%*
<20
>20
ECCS 10
Actual
<20
>20
90%*
<20
>20
NHANES III11
Actual
<20
>20
90%*
<20
>20
Kappa estimate of agreement
(95% confidence limits)
Percentage of misclassified
results
Men
Women
Men
Women
0.623
(0.617-0.629)
0.698
(0.684-0.712)
0.850
(0.805-0.892)
0.793
(0.781-0.805)
0.565
(0.501- 0.629)
0.620
(0.604-0.636)
23.3
29.8
20.4
25.9
0.857
(0.813-0.901)
0.756
(0.744-0.768)
0.752
(0.698-0.806)
0.706
(0.692-0.720)
0.866
(0.820-0.912)
0.702
(0.686-0.718)
0.728
(0.670-0.786)
0.875
(0.865-0.885)
0.897
(0.859-0.935)
0.788
(0.776-0.800)
0.556
(0.488-0.624)
0.648
(0.632-0.664)
0.744
(0.688-0.800)
0.674
(0.660-0.688)
0.852
(0.808-0.894)
0.870
(0.860-0.880)
0.675
(0.611-0.739)
0.402
(0.384-0.418)
9.5
13.7
8.9
8.6
15.8
19.0
15.7
18.7
16.0
29.2
8.1
22.0
6.2
13.3
15.3
21.4
22.1
42.5
9.1
8.5
ECCS, European Community for Coal and Steel; NHANES III, Third National Health and Nutrition Examination Survey
*Results from using 90 per cent of predicted values of forced vital capacity (FVC) and forced expiratory volume in first second
(FEV 1) of respective Caucasian prediction equations
Discussion
Differences in pulmonary function among various
racial and ethnic groups are well known, and
differences in body proportions, chest wall anatomy,
mechanical properties of the thorax, and parenchymal
lung development have been postulated as contributory
factors 1,11,16-26 . Most reference equations used to
predict normal lung function have been derived in
American or European subjects, and may not be
suitable for use in other populations. It has been
generally suggested that lung volumes in Indian
subjects are approximately 10 per cent smaller than
corresponding values in Caucasians4. The origin of
this figure is not clear, but probably stems from a
single small study on Pakistani workers resident in
United Kingdom27. Though such ‘ethnic discounting’
has been widely recommended, there is no firm
evidence that it provides accurate estimates28. This
single figure also does not account for the
heterogeneity among Indian population. It is well
known that north Indians have higher lung volumes
than south Indians 7. This study was conducted to
evaluate how well commonly used Caucasian reference
Regression equation
Crapo
8
FVC (l)
<20
0.40 (0.18 to 0.61)
0.48 (0.38 to 0.58)
0.60 (0.42 to 0.78)
0.64 (0.54 to 0.73)
>20
0.26 (0.06 to 0.47)
0.36 (0.18 to 0.55)
0.46 (0.25 to 0.67)
0.50 (0.25 to 0.75)
<20
0.01 (-0.09 to 0.12)
0.14 (0.11 to 0.16)
>20
-0.06 (-0.15 to 0.04)
0.09 (0.00 to 0.19)
<20
-0.03 (-0.43 to 0.37)
-0.08 (-0.54 to 0.38)
0.03 (-0.27 to 0.34)
0.10(-0.22 to 0.42)
>20
-0.20 (-0.86 to 0.47)
-0.08 (-0.63 to 0.48)
0.20 (0.09 to 0.31)
0.30 (0.19 to 0.42)
<20
-0.37 (-0.68 to -0.05)
-0.37(-0.74 to 0.00)
>20
-0.49(-0.99 to 0.03)
-0.30 (-0.73 to 0.12)
<20
0.28 (0.11 to 0.45)
0.32 (0.20 to 0.45)
0.42 (0.30 to 0.54)
0.35 (0.19 to 0.51)
>20
0.00 (-0.29 to 0.29)
0.06 (-0.26 to 0.38)
0.16 (-0.11 to 0.42)
0.23 (-0.05 to 0.51)
<20
-0.09 (-0.16 to -0.02)
0.00 (-0.05 to 0.04)
>20
-0.30(-0.47 to -0.12)
-0.18 (-0.40 to 0.04)
Actual
<20
0.22 (-0.36 to 0.79)
0.20 (-0.28 to 0.68)
0.36 (0.17 to 0.56)
0.38 (0.16 to 0.61)
>20
<20
0.52 (0.23 to 0.80)
-0.15 (-0.65 to 0.36)
0.34 (-0.02 to 0.69)
-0.12 (-0.54 to 0.30)
0.65 (0.50 to 0.80)
0.55 (0.36 to 0.74)
90%*
>20
0.17 (-0.02 to 0.36)
0.07 (-0.20 to 0.33)
Actual
90%*
ECCS 10
Actual
90%*
NHANES III
11
Women
FEV1 (l)
90%*
Knudson
Men
FVC (l)
Actual
9
Age (yr)
ECCS European Community for Coal and Steel; NHANES III, Third National Health and Nutrition Examination Survey
FVC and FEV1 values expressed in litres
*Results from using 90 per cent of predicted values of FVC and FEV1 of respective Caucasian prediction equations
FEV 1 (l)
AGGARWAL et al: CAUCASIAN EQUATIONS FOR SPIROMETRY IN INDIA
Table IV. Mean bias and limits of agreement for lower limit of normal for forced vital capacity (FVC) and forced expiratory vloume in first second (FEV 1) on
comparison of Caucasian with north Indian prediction equations
159
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INDIAN J MED RES, AUGUST 2005
Table V. Mean predicted values of forced vital capacity (FVC) and forced expiratory volume in first second (FEV1) obtained by
north Indian equations, expressed as a percentage of those obtained by Caucasian equations
Crapo 8
FVC
FEV 1
Knudson 9
ECCS 10
NHANES III9
FVC
FEV 1
FVC
FEV 1
FVC
FEV 1
76.9±8.0
Age (yr):
15-24
Men
89.8±1.6
76.5±3.0
96.3±9.2
82.1±9.9
92.8±1.5
79.0±2.8
90.3±7.8
Women
79.9±0.9
69.6±1.9
96.0±6.3
83.8±7.1
85.4±0.9
74.4±1.6
84.6±3.5
73.7±4.2
Men
90.5±1.3
74.5±1.8
98.9±9.1
81.5±8.3
94.9±1.6
78.1±1.9
83.8±0.9
69.0±0.8
Women
80.2±1.1
66.4±1.6
91.7±1.6
76.0±2.2
87.6±1.2
72.6±1.4
78.4±1.8
65.0±2.1
Men
91.5±1.4
73.3±1.7
103.9±10.
83.3±8.9
97.5±1.9
78.1±2.0
83.4±0.9
66.8±0.6
Women
80.5±1.2
63.9±1.3
91.3±1.7
72.5±1.8
90.4±1.7
71.7±1.4
74.4±1.9
59.1±1.8
Men
92.3±1.7
72.2±1.7
110.5±14.
86.5±11.3
100.5±2.6
78.6±2.3
83.8±1.0
65.6±0.5
Women
80.6±1.2
61.9±1.0
90.5±1.6
69.5±1.4
93.7±2.3
72.0±1.5
71.7±2.0
55.1±1.7
Men
92.9±1.8
71.3±1.6
118.9±50.9 91.3±39.7
103.5±3.2
79.5±2.6
85.4±1.1
65.6±0.5
Women
80.5±1.5
60.8±1.0
89.2±1.7
98.1±3.9
74.1±2.7
70.1±2.5
53.0±2.1
Men
93.3±2.2
70.8±1.8
127.7±26.4 96.9±20.2
106.9±4.6
81.1±3.6
88.3±1.5
67.0±0.9
Women
79.8±1.6
60.8±1.1
87.4±2.4
66.6±1.8
103.3±6.0
78.7±4.7
70.5±2.9
53.7±2.4
Men
93.4±2.6
70.6±2.0
152.1±102
115.0±78.
111.8±7.2
84.5±5.6
95.0±4.6
71.8±3.7
Women
78.0±1.8
62.4±2.3
90.8±3.9
72.7±4.7
111.5±12.3 89.5±12.8
75.0±4.6
60.1±5.5
Men
98.0±4.3
80.9±5.2
206.8±249
166.4±190
110.1±15.
90.4±8.3
87.7±11.1
72.9±13.4
Women
81.6±1.3
64.5±3.4
92.1±4.1
72.9±6.7
97.0±8.8
76.5±5.9
71.7±5.4
56.9±7.4
Men
94.5±1.7
74.7±2.2
134.7±27.
106.0±18.
105.0±6.7
82.9±2.7
85.3±5.2
67.5±5.3
Women
79.9±0.7
63.3±2.9
90.6±2.8
71.9±5.2
92.0±4.8
72.8±2.4
75.1±4.3
59.7±6.2
Men
92.0±1.2
72.5±2.0
110.5±11.5 86.9±7.0
100.6±4.8
79.2±1.6
86.0±3.9
67.8±3.9
Women
78.7±0.6
62.7±2.6
89.4±2.6
71.2±4.6
89.1±3.3
70.8±1.4
77.4±3.7
61.8±5.5
Men
90.2±0.9
71.2±1.8
97.8±6.0
77.1±3.4
97.2 ±3.6
76.7±0.9
86.0±3.2
67.9±3.4
Women
77.6±0.6
61.7±3.0
88.8±4.1
70.8±6.2
87.8±3.1
69.7±1.3
78.9±4.4
62.9±6.4
> 180
Men
88.8±0.7
70.0±1.7
90.5±3.8
71.3±2.6
95.1±3.1
74.9±0.7
85.9±2.9
67.7±2.9
Total
Men
92.0±2.1
72.6±2.6
112.7±37.
88.7±28.0
100.6±5.9
79.3±3.2
85.9±4.2
67.8±4.4
Women
80.3±1.3
63.6±3.1
90.9±3.3
72.1±5.6
93.1±6.7
73.6±4.2
74.4±5.0
59.1±6.7
25-34
35-44
45-54
55-64
65-74
>75
67.4±1.2
Height (cm):
<150
151-160
161-170
171-180
ECCS- European Community for Coal and Steel; NHANES III, Third National Health and Nutrition Examination Survey
AGGARWAL et al: CAUCASIAN EQUATIONS FOR SPIROMETRY IN INDIA
161
(A)
(B)
Fig. Receiver operating characteristic curves to assess if use of a fixed percentage of Caucasian predicted value (References 8-11)
can be used to correctly categorize forced vital capacity ( A) or forced expiratory volume in first second (B) as abnormal when
compared to north Indian equations (Reference 7). All plots have area under curve close to 0.5, indicating that this cannot be
achieved at any specified percentage. ECCS European Community for Coal and Steel; NHANES III, Third National Health and
Nutrition Examination Survey.
162
INDIAN J MED RES, AUGUST 2005
equations perform, in comparison to already available
north Indian prediction equations, in interpretation of
routine spirometry data at our institute.
Few investigators have tried to establish the utility
of Caucasian prediction equations in other population
subsets. Almost all of these studies have suffered from
common methodological problems that preclude the
extrapolation of their results into routine clinical
practice. Most studies correlated values obtained from
‘external’ prediction equations with values obtained
on a small number of native healthy subjects18,22,23,25.
It is well known that there can be clinically important
disagreement in results despite statistically good
correlation14. More importantly, these results have not
been verified on patients undergoing spirometry. In
other instances, predicted values from two equations
have been compared for a specified age and/or height
in men and women 7,21, or mean values have been
compared18,22. Such results hold true only for a small
proportion of the large spectrum of subjects studied.
Most studies also do not take into account the complex
interplay between different variables in predicting lung
volumes.
analysis was that predicted north Indian FEV1 values
were much lower than Caucasian values, in
comparison to corresponding FVC values. Based
solely on this observation, we would hesitate to
recommend a uniform reduction in predicted values
for both these lung volumes for approximation from
Caucasian prediction equations. Even otherwise, there
is wide variability in individual differences in both
FVC and FEV 1 across different age and height
categories, and again, a single fixed percentage value
is unlikely to be useful for the entire spectrum of
patients. In fact, the use of a fixed reduction factor of
any magnitude will be a poor discriminator in labeling
either FVC or FEV1 as abnormal, since areas under
corresponding ROC curves are quite close to the 0.5
value obtained purely by chance.
Data on the utility of Caucasian equations in
interpreting spirometry results in patients of other
populations are sparse. In a small study on 442
Spanish patients at a New Mexico hospital, local
Hispanic prediction equations were compared to three
Caucasian equations in categorizing FVC and FEV1
as normal or abnormal29. In this report, 2-10 per cent
results were misclassified, depending on the reference
equation used. Subjects with discordant classifications
tended to be at extremes of age and height
distributions. On average, the differences between the
predicted values from the three sets of external
equations and local Hispanic equation were small, but
the range of differences was wide.
We also attempted to quantify agreement between
predicted values of different spirometric parameters
using north Indian and different Caucasian equations.
This approach has definite advantages over merely
reporting correlation or regression coefficients, as it
provides a numerical estimate of how similar are
values obtained from two distributions, and whether
results from the two approaches can be used
interchangeably14. We observed wide variations in the
mean bias for all spirometric parameters studied. We
also calculated the limits of agreement, which
represent a numerical expression of range in which
95 per cent of the bias values are likely to be situated.
We documented wide limits of agreement in most age
and height categories, and more so in women. The
range was more than 150 ml of FVC and FEV1 in
several categories of age and height, and thus clearly
beyond the normal variability accepted in the
measurement of these lung volumes. These findings
suggested a rather poor agreement between Caucasian
and north Indian predicted values.
Our results highlighted some important facts about
use of Caucasian prediction equations in other
populations. It is quite evident that these equations
led to misinterpretation of spirometry data in a
significant proportion of patients and this might result
in inappropriate diagnosis and/or management. The
use of 90 per cent of predicted Caucasian values for
FVC and FEV1 in men also did not totally ameliorate
the problem, and actually worsened it in some
instances. An important aspect brought out by our
The main strength of our study is the large number
of spirometry records included for analysis that
ensured adequate representation from all categories
of age and height in both men and women allowing
confident identification of even small differences
without any bias. In fact, our estimates appear much
more precise and associated with much smaller error
distributions than previous studies23,29. The patients
studied herein represent a population that undergoes
routine pulmonary function testing at our centre. The
AGGARWAL et al: CAUCASIAN EQUATIONS FOR SPIROMETRY IN INDIA
relatively higher proportion of normal spirometric
records in this study is probably a reflection of the
fact that a large number of patients from other
departments are routinely investigated at our
laboratory, and spirometry is a screening procedure
for several of them (for example, as part of routine
preoperative workup). Though the results from this
study can only be strictly applied to a similar
population tested with similar instruments and
procedures, the conclusions can perhaps be
generalized to other situations as well with minor
differences. Our analysis also provides a scheme that
could be used to ascertain how well (or poorly) existing
regression equations predict lung function in a
different set of patient population.
The absence of concurrent controls may be
regarded by same as a limitation of our study. We
preferred to use regression equations previously
desired by us from asymptomatic non smokers selected
from the general population, rather than asymptomatic
patients. Moreover, it is not a standard procedure to
derive regression equations for population use from
patients attending hospitals.
It is also well known that different Caucasian
prediction equations yield different results and classify
subjects differently when applied to a study
population 30-32. The fact reinforces the utility of a
strategy of assessing performance of more than one
regression equation in one’s own clinical practice
before choosing any single prediction equation.
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Reprint requests: Dr S.K. Jindal, Professor & Head, Department of Pulmonary Medicine
Postgraduate Institute of Medical Education & Research, Chandigarh 160012, India
e-mail: skjindal@indiachest.org