A validated model for the 22-item Sino-Nasal Outcome Test subdomain structure in chronic rhinosinusitis

Int Forum Allergy Rhinol. 2017 Dec;7(12):1140-1148. doi: 10.1002/alr.22025. Epub 2017 Oct 13.

Abstract

Background: Previous studies have identified subdomains of the 22-item Sino-Nasal Outcome Test (SNOT-22), reflecting distinct and largely independent categories of chronic rhinosinusitis (CRS) symptoms. However, no study has validated the subdomain structure of the SNOT-22. This study aims to validate the existence of underlying symptom subdomains of the SNOT-22 using confirmatory factor analysis (CFA) and to develop a subdomain model that practitioners and researchers can use to describe CRS symptomatology.

Methods: A total of 800 patients with CRS were included into this cross-sectional study (400 CRS patients from Boston, MA, and 400 CRS patients from Reno, NV). Their SNOT-22 responses were analyzed using exploratory factor analysis (EFA) to determine the number of symptom subdomains. A CFA was performed to develop a validated measurement model for the underlying SNOT-22 subdomains along with various tests of validity and goodness of fit.

Results: EFA demonstrated 4 distinct factors reflecting: sleep, nasal, otologic/facial pain, and emotional symptoms (Cronbach's alpha, >0.7; Bartlett's test of sphericity, p < 0.001; Kaiser-Meyer-Olkin >0.90), independent of geographic locale. The corresponding CFA measurement model demonstrated excellent measures of fit (root mean square error of approximation, <0.06; standardized root mean square residual, <0.08; comparative fit index, >0.95; Tucker-Lewis index, >0.95) and measures of construct validity (heterotrait-monotrait [HTMT] ratio, <0.85; composite reliability, >0.7), again independent of geographic locale.

Conclusion: The use of the 4-subdomain structure for SNOT-22 (reflecting sleep, nasal, otologic/facial pain, and emotional symptoms of CRS) was validated as the most appropriate to calculate SNOT-22 subdomain scores for patients from different geographic regions using CFA.

Keywords: SNOT-22; chronic rhinosinusitis; disease severity; rhinosinusitis; sinusitis; statistics.

MeSH terms

  • Adult
  • Aged
  • Chronic Disease
  • Factor Analysis, Statistical
  • Female
  • Humans
  • Male
  • Middle Aged
  • Pain / diagnosis
  • Reproducibility of Results
  • Rhinitis / diagnosis*
  • Severity of Illness Index*
  • Sinusitis / diagnosis*
  • Sleep Wake Disorders / diagnosis
  • Surveys and Questionnaires
  • Symptom Assessment