Normative data for interpreting the SNOT-22

Abstract

Objectives: The Sino-Nasal Outcome Test 22 (SNOT-22) is a validated patient-reported outcome instrument to evaluate the health-related quality of life (HRQoL) in patients with chronic rhinosinusitis (CRS). There are no published normative SNOT-22 scores, limiting its interpretation.

Methods: Symptom scores from 1,000 SNOT-22 questionnaires were analysed by principal component analysis (PCA) and exploratory factor analyses. Data were derived from a survey with 1,000 healthy Europeans (reference cohort) who were recruited using the Respondi panel for market and social science research. This subsample was quoted to the population distribution of the German Microcensus and selected from a non-probability panel.

Results: The overall normative SNOT-22 score can be detected to be 20.2 ± 19.44. Male (18.49 ± 19.15) and older (> 50 years old; 18.3 ± 17.49) participants had overall lower SNOT-22 mean results than females (21.8 ± 19.6) and younger (21.4 ± 20.55) participants, indicating higher levels of satisfaction. PCA proposed two SNOT-22 domains ("physiological well-being" and "psychological well-being"), which explained 65% of the variance.

Conclusions: These are the first published (German) normative scores for the SNOT-22 and provide a clinical reference point for the interpretation of data.

Keywords: Sino-Nasal Outcome Test 22; chronic rhinosinusitis; functional endoscopic sinus surgery; normative score; patient-reported outcome measures.

Figure 1.

Forest plot showing the association of the SNOT-22 with age (A) and gender (B). The questionnaire score is plotted on the y-axis, the frequency of score responses on the x-axis. Lower scores of SNOT-22 signify a more positive rating indicating higher levels of satisfaction.

Figure 2.

Biplot figure of our two-factor Principal Component Analysis. It is clearly visible that the participant cohort is divided in two groups concerning the SNOT-22 questionnaire. Comp. 1 and Comp. 2 are the first two Components that will later be used as the sub-indices physiological and psychological well-being. The “cloud” of numbers reflects the observations and their respective case number, whereas the different variable names are represented in red. The arrow that stems from the graphs centroid and joins the variable name can be used to determine the relatedness between the variables. The closer they are, the higher the (positive) correlation between these variables. Arrows that oppose each other in a 90°-angle can be considered as uncorrelated. Arrows that go into a different direction are negatively correlated.

Figure 3.

Non-graphical analysis as elbow figure. The black curve, illustrating the Eigenvalues in descending order, would suggest the inclusion of 3 main components, since any further Eigenvalues are below one. However, the parallel analysis (green), comparing the actual matrix of Eigenvalues to a Monte-Carlo-simulated matrix of the same size, shows that only the EVs of two main-components are above the 95^th percentile. The same number of components is suggested in regard of the EVs’ gradients and the optimal coordinates (red), whereas the Acceleration Factor (blue) shows that the major shift in the slope of this curve can be seen after the first component.