Statistical analysis
Statistical analysis was performed in IBM SPSS Statistics for Windows
version 28.0.1.0 (IBM Corp., NY, USA) for both GC-MS and SIFT-MS data.
Kruskal-Wallis tests with pairwise comparisons were performed to
determine significant differences between recovered, mild rattling,
moderate rattling and severe rattling infants for each of the 32 VOCs
detected by GC-MS. For SIFT-MS data, an additional dimension reduction
was performed on all 167 detected features by means of principal
component analysis. Again Kruskal-Wallis tests with pairwise comparisons
were performed to determine significant differences between recovered,
mild rattling, moderate rattling and severe rattling infants, for each
of the principal components (PC). Discriminant analysis with
leave-one-out cross-validation was performed with the significantly
different VOCs for the GC-MS data and with the significantly different
PCs for the SIFT-MS data. The quality of the discrimination models (DMs)
was evaluated with receiver operating characteristics (ROC) curve
analysis.
To investigate whether PRO parameters could improve the DM based on the
VOCs for GC-MS data or the PCs for SIFT-MS data, Pearson correlation
coefficients were used to assess the correlation between the
paediatrician’s rattle score and the most clinically relevant parameters
extracted from the questionnaires (age at onset of noisy breathing, age
at first cold, number of colds, PROb and
PROs. Furthermore, Kruskal-Wallis tests were used to
assess if those clinically relevant parameters were significantly
different between the four groups (recovered, mild rattling, moderate
rattling and severe rattling infants). In case a PRO parameter
correlated with the paediatrician’s rattle score or was significantly
different between the four groups, is was added to the DMs. For
comparison, a third DM including only the PRO parameter(s) was used.