Αρχειοθήκη ιστολογίου

Αλέξανδρος Γ. Σφακιανάκης
ΩτοΡινοΛαρυγγολόγος
Αναπαύσεως 5
Άγιος Νικόλαος Κρήτη 72100
2841026182
6032607174

Παρασκευή 13 Οκτωβρίου 2017

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

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.



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