Online data supplement
Exhaled nitric oxide thresholds to predict sputum eosinophil count ≥3% in a cohort of unselected
asthmatics
F. Schleich1, L. Seidel3, J. Sele1, M. Manise1, V. Quaedvlieg1, A. Michils², R. Louis1
1
Department of Pulmonary Medicine, CHU Sart-Tilman, Liege, I3 GIGA research group,
University of Liege, Belgium, ²Division of Pulmonary Medicine, Erasme Hospital University
of Brussels, 3Medical Informatics and Biostatistics, University of Liege, Belgium.
Statistical methods
We applied logistic regression analysis to the data. The binary outcome variable was Sputum
eosinophil count (SEC) (below or above 3%). Covariates included FENO (log-transform), age,
gender, smoking, ICS, and atopy. Since the group of 295 patients may be considered as a
representative sample of the population of asthmatic patients attending a University Clinic,
the proportion of subjects with sputum eosinophil count  3%, namely 42.3%, is an unbiased
estimate of the true proportion in the population. It follows that the linear combination of
covariates, the so-called risk index R, obtained by logistic regression analysis can be used to
calculate the probability of Sputum eosinophil count  3%. The equation writes
Pr SEC  3% R  
eR
1 e R
The value R=0 can be used to determine cut-off point on the FENO scale.
Given that Pr(SEC≥3%/R)=0.5, 0.5= e R/ (1+eR).
0.5 + 0.5eR = eR, eR=1 and R=0.
For example, using ln(FENO) as single covariate in the logistic model, we obtained
R  4.47  1.1933  ln FE NO , which yields a cut-off point equal to 42 ppb on the FENO scale.
When including smoking into the model, we obtained
R  4.83  1.2608  ln FE NO  0.6564  smoking , where the p-value for smoking was 0.066 (NS).
By equating R to 0 for smokers (smoking=1) and non-smokers (smoking=0), the cut-off
points were 27 ppb and 46 ppb, respectively. The difference between the two cut-off points
however is not statistically different (p= 0.066).
The same approach was used for age, gender, ICS and atopy. For ICS, it appeared that only
the last category (dose > 1000 µg/d beclomethasone) was significant. This led us to define a
new binary ICS variable ( 1000 or > 1000 µg/d) which turned out to be significant when
combined to FENO. Age and gender were not contributing significantly to the predictive value
of FENO. By contrast, we found that atopy had an effect on the slope of ln(FENO) in the logistic
model, not on the intercept.
When combining all variables into the logistic model, we found that only FENO (p<0.0001),
smoking (p=0.044) and ICS (p=0.019) were significant predictors of Sputum eosinophil count
3 %. When adding atopy, the p-value was not significant although a tendency was observed
(p=0.086). Akaike’s Information Criterion (AIC) however reached a minimum for this model.
We therefore decided to keep also this covariate in the model.
Analysis of Maximum Likelihood Estimates
Parameter
Estimate
Standard error
Wald Chi-Square
Pr>ChiSq
FENO
Intercept
-4.47
0.60
56.00
<0.0001
Ln FENO
1.19
0.16
52.75
<0.0001
FENO and
Intercept
-4.83
0.65
55.82
<0.0001
Smoking
Ln FENO
1.26
0.17
53.37
<0.0001
Smoking
0.66
0.36
3.38
0.066
FENO and
Intercept
-4.94
0.65
57.94
<0.0001
High Doses of Ln FENO
1.28
0.17
55.44
<0.0001
ICS
High doses ICS
0.72
0.33
4.80
0.028
FENO and Ln
Intercept
-4.69
0.62
57.01
<0.0001
FENO *Atopy
Ln FENO
1.38
0.20
49.14
<0.0001
LnFENO*Atopy
-0.18
0.09
3.85
<0.050
FENO and
Intercept
-5.50
0.72
58.95
<0.0001
smoking and
Ln FENO
1.52
0.21
53.05
<0.0001
High Doses of Smoking
0.75
0.36
4.17
0.041
ICS and Ln
High Doses ICS
0.69
0.34
4.28
0.032
FENO *Atopy
LnFENO*Atopy
-0.16
0.09
2.95
0.086