Background/Purpose: Gout Assessment Questionnaire (GAQ2.0) is a validated disease-specific measure used to evaluate patient reported outcomes in gout studies. The objective was to evaluate the correlates of Gout Impact Scale (GIS) of GAQ2.0 and change over time in a prospective cohort of patients.

Methods: 186 gout patients were recruited in a 9-month longitudinal study at 2 VA centers where data on HRQOL using SF36 v2 (higher score denotes better health), Health Assessment Questionnaire, HAQ-DI (0-3, higher score indicates worse function) and GIS (higher score denotes greater impact of disease) was administered. The GIS has 5 domains: gout concern overall, gout medication side effects, well-being during attack, unmet gout treatment needs, and gout concern during attack; GIS scores range from 0 to 100, where a higher score indicates worse health. Demographics, education level, acute flare frequency, Charlson comorbidities (CCI), serum urate (sUA), serum creatinine, functional class, and patient and physician rating of the severity of gout on a 0-10 scale, were assessed. Spearman Correlations were calculated between baseline GIS domains scores and SF-36 PCS & MCS, HAQ-DI Score, CCI, sUA, and age. Multivariate linear regression was used to assess demographic and clinical predictors of total GIS score at baseline. Paired Wilcoxon sign rank tests were used to assess change in GIS between baseline and last visit. P-values <0.05 were considered significant.

Results: Mean age (SD) of patients was 64.6 (10.9) years, they were predominantly male (98%); 57% Caucasian, 32% African American, 13% Hispanic, and 94% who graduated high school. Mean sUA was 8.3 mg/dL (3.4), physician assessment of gout severity was 3.1 (2.7) and patient gout-severity assessment was 5.7 (3.1). Moderate negative correlations were noted between SF-36 PCS and GIS scores (r=-0.29) and well-being during attack (r=-0.39); whereas SF 36 MCS showed higher correlation with GIS domains ranging from 0.35-0.43, all p<0.001. HAQ-DI was significantly correlated with GIS, (r=0.32, p<0.001). Average GIS scores correlated with age (r=0.4, p<0.001). GIS score was significant higher than those with self-reported recent attack of gout within ²3 months from baseline compared to those without a recent attack. Younger age and experiencing an attack within last 3 months were predictive of higher total GIS score in multivariate linear regression analyses.  A significant decrease in GIS scores was observed from baseline to 9-month period in the domains (Table).

Conclusion: GIS is a disease-specific measure that adequately captures the impact of gout over time. This study provides comprehensive validity of GIS in gout that meets the OMERACT filters.

*P<0.05; **p<0.01; *** p<0.001