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Multi-Dimensional Gastrointestinal Symptom Severity Index: Validation of a Brief GI Symptom Assessment Tool
Michael D Crowell1, Sarah B Umar, Brian E Lacy
1Division of Gastroenterology and Hepatology, Mayo Clinic Arizona, 13400 East Shea Blvd, Scottsdale, AZ, 85259, USA, crowell.michael@mayo.edu.
Unlabelled:
Few instruments have been developed and validated for the evaluation of multi-dimensional GI symptoms. The Gastrointestinal Symptoms Severity Index (GISSI), a multi-dimensional, self-report instrument, was designed as a brief measure of the frequency, severity, and bothersomeness of individual GI and pelvic floor/urogynecologic symptoms.
Aim:
To report the psychometric properties of the GISSI subscales, including factorial structure, validity, and internal consistency.
Methods:
The GISSI included 32 items that assessed upper and lower GI symptoms and seven items related to pelvic floor/urogynecologic symptoms. A total of 934 patients presenting for upper and lower GI complaints completed the questionnaire between January 2013 and December 2013. The sample was randomly split into derivation (n = 466) and validation datasets (n = 468). A non-patient sample of 200 was collected separately.
Results:
Exploratory factor analysis supported a six-factor model for the derivation sample that accounted for 69.3 % of the total variance. The six GI symptom clusters were labeled as constipation/difficult defecation (five items), abdominal pain/discomfort (four items), dyspepsia (four items), diarrhea/anal incontinence (four items), GERD/chest symptoms (four items), and nausea/vomiting (two items). Inclusion of additional items related to female pelvic floor/urogynecologic symptoms resulted in a separate factor. Confirmatory factor analysis of the validation dataset supported the a priori hypothesized six-factor measurement model (Χ (2)(428) = 1462.98; P < 0.001; GFI = .88; RMSEA = .051).
Conclusion:
The GISSI demonstrated good to excellent psychometric properties and provided multi-dimensional scaling of prominent GI symptom clusters. Further validation may provide an efficient, valid, and reliable measure of patient-reported clinical outcomes.
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