Discussion
This prospective study in real-life paediatric secondary care setting investigated the potential of exhaled volatile biomarkers in the differentiation of noisy breathing infants presenting with wheezing or rattling. Due to the low number of wheezing infants included in the study, the statistical analysis focused on the differentiation of rattling infants with different severity from recovered infants. The severity was a classification based on the paediatrician’s diagnosis. Collecting exhaled breath samples form infants using an in-house adjusted commercial breath sampler was feasible and acceptable. In order to avoid difficulties during breath sampling, placing the child on the parent’s lap, distracting it with toys or movies and assistance from the parent were very important.
Offline GC-MS and SIFT-MS analysis showed similar performance in discriminating the different patient groups (recovered, mild rattling, moderate rattling and severe rattling). The differentiation of recovered/mild rattlers and moderate/severe rattlers showed slightly better performance than the four group discrimination. Both the four group and the two group discrimination improved by combining exhaled breath data with PROs. For comparison, we also ran a discriminant model using only PROs. This resulted in much lower performance, particularly for the discrimination of mild, moderate and severe rattling.
We used both GC-MS and SIFT-MS analytical techniques to explore their diagnostic capacity. GC-MS allows the detection and identification of compounds, and is the current golden standard for breath VOC analysis. However, it cannot be used for real-time exhaled breath measurements12, which limits its potential as a point-of-care application. The instrumental compacity and simplicity of use make SIFT-MS a candidate as point-of-care breath screening system.13 However, SIFT-MS could not be used for identification of VOCs. Multiple studies have reported the potential of breath VOCs to diagnose respiratory illnesses in adults and children.14-19 Exhaled VOCs may reflect reactions in the target organ and therefore, reflect a person’s health status. The fact that VOC metabolites are directly measured in the exhaled air, makes them interesting as a potential easy to use point-of-care tool.
Some of the differentiating VOCs identified by GC-MS in the current study, have been previously reported to be associated with asthma, wheezing or viral infections. 2-butanone has been associated with asthma.20,21 Pentanoic acid was reported as one of the discriminating VOCs for the differentiation of preschool children (1.9-4.5 years) with recurrent wheeze from children without wheeze.22 Exhaled 2,8-dimethylundecane in adults was shown to be associated with oxidative stress induced by viral infections.23 A very similar compound, 3,9-dimethylundecane has been described to be altered in wheezing or asthmatic children compared to controls.19
The comparison of the different discriminating models in the current study demonstrated that exhaled breath markers can be of value for the discrimination of rattling infants from recovered infants and the differentiation based on severity. PROs also had its value in combination with the exhaled breath markers, but on this own it was less valuable for discrimination of the different groups. However, PROs is subjective and depends on the knowledge of the parents. In this pilot study all parents were specifically informed by a paediatrician specialized in pulmonology who used sound recordings of wheeze and rattle in infants to demonstrate the specific traits of the sounds, and the difference. Furthermore, the paediatrician explained carefully what the indications of burden to the child were (not eating, not sleeping, less playful). That information may have influenced the added value of the parent reported parameter. The quality of parent’s reporting might be lower in a non-informed population. This would be especially the case in primary care where general physicians (GP) are not always aware of the difference between wheezing, rattling, and its severity.
The main limitation of this study was its small sample size which may result in overfitting of the statistical models used for classification. To avoid this, validation of the discriminant model is mandatory i.e. ideally, the sample size should be large enough to split the dataset into a training set (used to train the classification method) and validation set (used to test the trained classification model).24 Instead of splitting the data, we applied leave-one-out cross validation which is usually used when the number of available samples per class is low (around 20 per class).24 Another limitation, was the identification of VOCs measured by GC-MS based on the National Institute of Standards and Technology (NIST) library. Although the NIST standard reference database is one of the most popular mass spectral databases for metabolite identification, results show a high rate of false identifications of metabolites.6 Therefore, identification of VOCs is not 100% certain and standards should be used in follow up studies to check retention time and mass spectrum in order to confirm identification.
Confounders known to effect breath VOC composition are food intake25, age and gender.26Especially the timing of food intake is difficult to control in the infant population. These confounders may also have played a role in our population. The children were between 5 and 16 months old at the time of inclusion. This age difference may cause differences in exhaled metabolites due to differences in metabolization and food pattern. Medication is also a known confounder27,28, however parents were instructed not to give their child medication on the day of the paediatrician visit. Blanchet et al. demonstrated that although the VOC breath profile of males and females are different, the separation between the two groups is not very marked and the difference is not sufficient to discriminate males and females.26Additionally, not accounting for confounders resulted in the recruitment of a study population that represents the infant population presenting to the real clinical practice as close as possible.