Abstract
Objective. The aim was to evaluate the reliability, validity and responsiveness to change of the Gout Impact Scale (GIS), a disease-specific measure of patient-reported outcomes, in a multicentre longitudinal prospective cohort of gout patients.
Methods. Subjects completed the GIS, a 24-item instrument with five scales: Concern Overall, Medication Side Effects, Unmet Treatment Need, Well-Being during Attack, and Concern Over Attack. The total GIS score was calculated by averaging the GIS scale scores. HAQ-Disability Index (HAQ-DI), Short Form (SF)-36 physical and mental component summaries (PCS and MCS) and physician and patient gout severity assessments were also completed. Reliability was assessed with Cronbach’s α. Baseline GIS scores were compared in subjects with and without gout attacks in the past 3 months using Wilcoxon rank sum tests. Multivariate linear regression was used to evaluate predictors of total GIS. Pearson’s correlation coefficients 0.24–0.36 were considered moderate and >0.37 considered large. The effect size for responsiveness to change was interpreted as follows: 0.20–0.49 small, 0.50–0.79 medium and >0.79 large.
Results. In 147 subjects, reliability was acceptable for total GIS (0.93) and all GIS scales (0.82–0.94) except Medication Side Effects and Unmet Treatment Need. Total GIS and all scales except Medication Side Effects discriminated between subjects with and without recent gout attacks (P < 0.05). Total GIS showed moderate-to-large correlations with HAQ-DI, SF-36 PCS and MCS (0.33–0.46). Improvement in total GIS tracked with improved physician and patient severity scores. Worsening physician severity score and recent gout attack predicted worsening total GIS.
Conclusion. Total GIS score is reliable, valid and responsive to change in patients with gout, and differentiates between subjects with and without recent gout attacks.
Keywords: gout, validated measure, outcomes
Rheumatology key messages
Total Gout Impact Scale score is reliable, valid and responsive to change in patients with gout.
Total Gout Impact Scale score differentiates patients with recent gout attacks from those without recent attacks.
Based on OMERACT filters, total Gout Impact Scale score can be used in gout clinical trials.
Introduction
Gout is the most common form of inflammatory arthropathy, affecting 1–2% of the Western population and increasing in prevalence over the past two decades [1]. Patients with acute and chronic gout have impaired health-related quality of life (HRQOL) relative to the general population [2–6]; therefore, HRQOL is recognized as an important area of focus in gout clinical trials for patient-reported outcomes [7, 8]. Nonetheless, there are few studies correlating specific features of gout with reduced HRQOL, most of which use generic HRQOL metrics such as Short Form (36) physical and mental component summaries (SF-36 PCS and MCS), rather than gout-specific metrics [2, 5, 9]. While useful, such generic metrics are not always closely correlated with patient-reported factors and do not always capture concerns related to the treatment of gout over time; therefore, they usually lack the responsiveness needed to detect small changes in HRQOL [10, 11].
The Gout Assessment Questionnaire (GAQ2.0) is a validated [5], disease-specific measure used to evaluate patient-reported outcomes in gout studies. The Gout Impact Scale (GIS) is a 24-item subset of the GAQ2.0, which assesses the impact of acute gout flares from the patient’s perspective, including both disease- and treatment-related concerns. At the time of its development, the GIS was found to have acceptable face and content validity and internal consistency reliability in a large cross-sectional cohort [5] and was successfully used in clinical trials [12]. The GIS was considered by OMERACT 10 [7] but not endorsed pending additional validation studies, specifically construct validity relative to general HRQOL metrics and responsiveness to change. The objective of our current analysis, therefore, was to examine reliability and responsiveness to change over time of the GIS in an observational cohort of patients with gout.
Methods
Subjects
A longitudinal observational cohort was launched at two Veteran’s Affairs (VA) medical centres (Los Angeles and Birmingham). Subjects with gout diagnosed by ACR criteria were recruited over 9 months. Subjects were recruited when they presented to their rheumatologist or primary care physician at either enrolment site for gout care. At enrolment, the following data were collected: a standardized set of HRQOL measures enumerated below, physical examination including full joint examination, patient functional class determined according to ACR criteria, Charlson co-morbidity index (CCI) [13] and disease-specific assessments including duration of gout, acute flare frequency and incidence, serum urate level (sUA) and patient and physician rating of gout severity on a 1–10 scale. Subjects were seen four times over the next year. At each follow-up visit, all study-related evaluation as outlined below, including all questionnaires, was performed by the enrolling physician. At visit 4, all the above data except for physical examination and laboratory testing were re-collected. Institutional Review Board approval was obtained from the VA ethics committee at both centres prior to any data collection, and written consent was obtained from all subjects at the time of their enrolment. De-identified data were used for analyses in this study.
Outcome measures
GIS
A self-reported one-page 24-item questionnaire contained within the GAQ2.0, which assesses disease-related HRQOL related to acute gout attacks and chronic gout. The questionnaire takes ∼5 min to complete and is copyrighted by Takeda Global Research and Development. All response options are six-point Likert scales (i.e. strongly agree to strongly disagree, or all of the time to none of the time) [14]. It has also been translated and validated in other languages. The GIS yields scores in the following five scales: Gout Concern Overall, Gout Medication Side Effects, Unmet Gout Treatment Need, Gout Well-Being during Attack and Gout Concern during Attack. We also derived a Total GIS score, which is the mean of the 24 GIS items. Total GIS score was considered non-missing if at least 13 items were completed by the subject. All scores range from 0 to 100, with higher scores denoting more severe disease impact.
HAQ-Disability Index
A 20-item self-administered questionnaire assessing functional disability in the following eight domains: dressing, grooming, arising, eating, walking, hygiene, reach, grip and activities [15]. Scores range from 0.0 to 3.0, with higher score denoting greater functional impairment. It was endorsed by OMERACT as a tool to measure activity limitation [7].
SF-36
A 36-question self-administered survey focusing on functional status and HRQOL. It is endorsed by the OMERACT group as a validated tool to measure HRQOL and functional disability in gout [7, 16]. It generates eight scores in different scales, as well as a PCS and MCS [17, 18]. It is scored on a T-score metric with a US population mean (s.d.) of 50 [10]; a higher score denotes a better HRQOL. Version 2.0 of the SF-36 (standard 4-week recall) was used.
Disease progression questions
At enrolment and at each follow-up visit, physicians and subjects were asked to rate the subject’s change in gout condition over the past year on a scale from 1 (entirely well) to 10 (very severe gout). At enrolment and at visit 4, subjects were asked to estimate their frequency of gout attacks over the past year. To capture the severity of disease and lack of appropriate treatment/compliance with medications, we also asked subjects to estimate their frequency of gout attacks over the past 4 weeks and the past 3 months at each follow-up visit. Subjects who estimated that their attack frequency had decreased in the past 3 months were categorized as improved, whereas subjects who estimated that their attack frequency had increased were categorized as unimproved. The presence or absence of gout attack in the past 3 months was used in regression analysis to assess predictors of total GIS score, as described below.
CCI
Co-morbidities, including hypertension, diabetes, depression, moderate/severe renal disease, myocardial infarction and chronic pulmonary disease were reported by subjects and verified by medical record review. CCI was calculated according to Charlson et al. [13]. Numeric weights from 1 to 6 were assigned to each of a subject’s medical conditions. The numbers were summed, and a point was added for each decade of age over 40 (age–co-morbidity score).
Statistical analysis
One hundred and eighty-seven subjects were enrolled (87 in Los Angeles and 60 in Birmingham), and 147 completed all follow-up visits and formed our cohort. Summary statistics were calculated for all demographic and clinical variables. Mean and s.d. are reported for continuous variables, whereas frequency and percentage are reported for discrete categorical or binary variables. To assess the internal consistency reliability of the GIS, we computed Cronbach’s α [19] for each of the scales and for the total score, with values of 0.70 or higher indicating acceptable construct validity [20].
To determine whether there was an association between GIS scores and other measures at baseline, we calculated Pearson’s correlations between baseline GIS scale scores and age, number of joints affected, number of Charlson co-morbidities, SF-36 PCS and MCS scales, HAQ-Disability Index (HAQ-DI) score, CCI and sUA. For each pair of variables, we tested whether correlations were significantly different from zero using a t test. Pearson correlation coefficients were interpreted as proposed by Cohen: 0.0–0.10 no correlation, 0.10–0.23 small correlation, 0.24–0.36 moderate correlation and ⩾0.37 large correlation [21]. Baseline GIS scores were also compared between subjects who had experienced gout attacks within 3 months and those without, using Wilcoxon rank sum tests.
We assessed responsiveness to change in GIS scores relative to the disease progression questions asked at follow-up. Changes in GIS scores from baseline to last follow-up visit were calculated for all five scales of the GIS and for the total GIS score. The effect size (ES) and standardized response mean (SRM) of these score changes were used to assess the magnitude and precision of score change. ES was calculated as the ratio between mean change scores and the s.d. of baseline scores [22]. The SRM was calculated as a ratio of mean change scores to the s.d. of the change scores [22] Both ES and SRM were interpreted as follows: 0.20–0.49 represents a small change, 0.50–0.79 a medium change and 0.80 or greater a large change [23]. Paired Wilcoxon signed rank tests were used to assess change in GIS scores between baseline and final visit, and a paired t test was used to assess change in sUA between baseline and final visit. Mixed effect regression models were used to assess the associations between each GIS scale and the outcomes of sUA and gout attack in the previous 3 months, adjusting for a random patient effect.
Multivariate linear mixed regression analysis was used to assess demographic and clinical predictors of total GIS score over the course of follow-up. Backward model selection was used to fit the model, and covariates with a value of P < 0.05 were retained in the final model. Subjects’ baseline age, sUA, race, disease duration, tophi diagnosis, physician score, CCI and a dichotomous variable for experiencing gout attacks over past 3 months were considered as predictors, adjusting for random patient effect and visit number. Values of P <0.05 were considered significant, and all analyses were conducted using SAS software (SAS Institute Inc., Cary, NC, USA).
Results
One hundred and forty-seven subjects, 87 from Los Angeles and 60 from Birmingham, had data collected at baseline and at all time points during follow-up and were included in the analysis. The mean (s.d.) age of subjects was 64.8 (10.8) years, 99% were male, 62% were Caucasian and 29% were African American (Table 1), with mean (s.d.) disease duration 14.1 (12.8) years and average sUA 8.3 (3.5) mg/dl. Mean CCI at the time of enrolment was 5.0, corresponding to a 5-year risk of all-cause mortality 6.3 times that of subjects with a CCI of 0 (13). Mean (s.d.) GIS scale scores at baseline were 68.4 (27.1) for Gout Concern Overall, 49.7 (28.3) for Gout Medication Side Effects, 36.0 (19.4) for Unmet Gout Treatment Need, 56.0 (27.3) for Well-Being during Attack, 55.0 (25.6) for Gout Concern during Attack and 54.9 (20.3) for total GIS. Subjects with gout attacks in the 3 months before enrolment (n = 100) had significantly higher scores in all GIS scales, except for Gout Medication Side effects, than did subjects without gout attacks in the 3 months before enrolment (n = 46; Fig. 1). The mean (s.d.) baseline total GIS score for subjects with an attack during this time was 59.0 (19.7), and mean total GIS score for those without an attack was 26.3 (19.3), P = 0.0004. No significant differences were seen in age, patient or physician severity score, number of joints affected, SF-36 MCS or PCS, any of the GIS scales or total GIS (P = 0.1–0.9; data not shown).
Table 1.
Baseline characteristics of cohort at enrolment
| Characteristics | Total number of patients | |
|---|---|---|
| Age, mean (s.d.), years | 64.8 (10.8) | 147 |
| Sex, male, n (%) | 146 (99) | 147 |
| Race, n (%) | 147 | |
| Black or African American | 42 (29) | |
| Caucasian | 91 (62) | |
| Other | 14 (9) | |
| Ethnicity, n (%) | 130 | |
| Hispanic or Latino | 16 (11) | |
| Not Hispanic Latino | 144 (77) | |
| Unknown | 17 (12) | |
| Self-reported medical co-morbidities, n (%) | ||
| Systolic hypertension | 115 (78) | 139 |
| Diabetes mellitus | 50 (34) | 139 |
| Depressed mood | 40 (27) | 139 |
| Moderate/severe renal disease | 33 (22) | 138 |
| Myocardial infarction | 26 (18) | 139 |
| Chronic pulmonary disease | 22 (15) | 139 |
| Alcohol use, n (%) | 83 (45) | 147 |
| Charlson co-morbidity index, mean (s.d.) | 4.8 (2.5) | 122 |
| Number of joints affected in the past 3 months, mean (s.d.) | 5.3 (9.4) | 118 |
| Presence of tophi, n (%) | 139 | |
| Yes | 28 (19) | |
| No | 111 (76) | |
| Unknown | 8 (5) | |
| Serum urate, mean (s.d.), mg/dl | 8.3 (3.5) | 113 |
| Baseline ULT use, n (%) | ||
| Allopurinol | 111 (76) | 147 |
| Probenecid | 7 (5) | 147 |
| Sulfinpyrazone | 5 (3) | 147 |
| Baseline acute treatment/prophylaxis use, n (%) | ||
| Colchicine | 79 (54) | 147 |
| NSAID | 42 (29) | 147 |
| CS | 20 (14) | 147 |
| Disease duration, mean (s.d.), years | 14.1 (12.8) | 146 |
| Physician severity score on scale of 0–10,a mean (s.d.) | 3.0 (2.7) | 138 |
| Patient severity score on scale of 0–10,a mean (s.d.) | 5.7 (3.1) | 135 |
| Health-related quality of life instruments | ||
| GIS scores,b mean (s.d.) | ||
| Gout Concern Overall | 68.4 (27.1) | 147 |
| Gout Medications Side Effects | 49.7 (28.3) | 147 |
| Unmet Gout Treatment Need | 36.0 (19.4) | 147 |
| Well-Being during Attack | 56.0 (27.3) | 147 |
| Gout Concern during Attack | 55.0 (25.6) | 147 |
| Total GIS score | 54.9 (20.3) | 147 |
| HAQ-DI score,c mean (s.d.) | 0.7 (0.6) | 146 |
| SF-36 PCS score,d mean (s.d.) | 39.0 (10.2) | 146 |
| SF-36 MCS score,d mean (s.d.) | 44.3 (13.9) | 146 |
a0–10, where a higher score indicates worse health.
b0–100, where a higher score indicates worse health.
c0–3, where a higher score indicates greater functional impairment.
dHigher score indicates better health. GIS: Gout Impact Scale; HAQ-DI, HAQ-Disability Index; MCS: mental component summaries; PCS: physical component summaries; ULT: urate-lowering therapy.
Fig. 1.
Baseline Gout Impact Scale scores in patients with and without gout attacks in the 3 months prior to enrolment
GIS: Gout Impact Scale.
Reliability
Cronbach’s α for each GIS scale was acceptable, with the exceptions of Gout Medication Side Effects and Unmet Gout Treatment Need (0.32 and 0.62, respectively). The remainder of the Cronbach’s α values were 0.90 for Gout Concern Overall, 0.94 for Well-Being during Attack and 0.82 for Gout Concern during Attack. Cronbach’s α for the total GIS was 0.93. Cronbach’s α did not change substantially when subjects were stratified by presence or absence of attack in the past 4 weeks or in the past 3 months (Table 2).
Table 2.
Cronbach’s α for baseline GIS scales, stratified by recent attack experience
| Attack in previous 4 weeks |
Attack in previous 3 months |
|||
|---|---|---|---|---|
| No | Yes | No | Yes | |
| Concern Overall | 0.88 | 0.90 | 0.87 | 0.90 |
| Medication Side Effect | 0.56 | 0.68 | 0.56 | 0.63 |
| Unmet Gout Treatment Need | 0.30 | 0.36 | 0.18 | 0.29 |
| Well-Being during Attack | 0.93 | 0.93 | 0.94 | 0.93 |
| Concern during Attack | 0.78 | 0.87 | 0.85 | 0.81 |
| Total GIS | 0.92 | 0.93 | 0.93 | 0.93 |
GIS: Gout Impact Scale.
Baseline correlations between GIS scores and other measures
Regarding demographic measures, moderate to large negative correlations were seen between age and four of the five GIS scales (r = 0.26–0.45, except Unmet Gout Treatment Need) and between number of joints affected and four of the five GIS scales (r = 0.26–0.37, except Medication Side Effects; Table 3). Total GIS showed a large negative correlation with age (r = −0.45) and a large positive correlation with the number of joints commonly affected (r = 0.37). No correlations were seen between GIS scales and sUA.
Table 3.
Correlations between Gout Impact Scale scales, demographic factors and health-related quality of life measures
| Age, n = 147 | No. of joints affected n = 118 | sUA n = 113 | CCI n = 122 | Pt. Gout Severity n = 135 | MD Gout Severity n = 138 | SF-36 MCS n = 146 | SF-36 PCS n = 146 | HAQ-DI n = 146 | Concern Overall n = 147 | Medication side effects n = 147 | Unmet Tr Need n = 147 | Well-Being during Attack n = 147 | Concern during Attack n = 147 | Total GIS n = 147 | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Age | Age | 1 | 0.08 | −0.04 | 0.47a | −0.24b | −0.04 | 0.26b | 0.13 | −0.08 | −0.38a | −0.26b | −0.19 | −0.43a | −0.26b | −0.45a |
| No. of joints affected | 1 | 0.03 | 0.10 | 0.15 | 0.32b | −0.30b | −0.24b | 0.46a | 0.25b | 0.10 | 0.27b | 0.35b | 0.31‡ | 0.37a | ||
| sUA | 1 | −0.11 | −0.18 | 0.01 | 0.11 | 0.11 | −0.10 | −0.09 | −0.12 | −0.02 | −0.02 | −0.08 | −0.07 | |||
| CCI | 1 | −0.16 | −0.14 | 0.00 | −0.17 | 0.11 | −0.19 | −0.07 | −0.04 | −0.04 | −0.10 | −0.10 | ||||
| Pt. Gout Severity | 1 | 0.19 | −0.20 | −0.11 | 0.13 | 0.33b | 0.06 | 0.23 | 0.33b | 0.28b | 0.36b | |||||
| MD Gout Severity | 1 | −0.22 | −0.21 | 0.34b | 0.15 | −0.04 | 0.24b | 0.25b | 0.18 | 0.25b | ||||||
| SF-36 MCS | 1 | 0.11 | −0.39a | −0.36b | −0.18 | −0.20 | −0.43a | −0.34b | −0.46a | |||||||
| SF-36 PCS | 1 | −0.67a | −0.21 | −0.03 | −0.18 | −0.40a | −0.08 | −0.33b | ||||||||
| HAQ-DI | 1 | 0.23 | 0.12 | 0.21 | 0.39a | 0.19 | 0.37a | |||||||||
| Concern overall | 1 | 0.41a | 0.48a | 0.55a | 0.59a | 0.79a | ||||||||||
| Med side effects | 1 | 0.10 | 0.24b | 0.37a | 0.45a | |||||||||||
| Unmet Tr need | 1 | 0.33b | 0.29b | 0.50a | ||||||||||||
| Well-being during attack | 1 | 0.51a | 0.91a | |||||||||||||
| Concern during attack | 1 | 0.73a | ||||||||||||||
| Total GIS | 1 |
aHigh correlation.
bModerate correlation. CCI: Charlson co-morbidity index; GIS: Gout Impact Scale; HAQ-DI: HAQ-Disability Index; MCS: mental component summaries; MD: physician; Med: medication; PCS: physical component summaries; Pt.: patient; sUA: serum urate level; Tr: treatment.
HAQ-DI showed small to moderate positive correlations with four of the five GIS scales (except for Medication Side Effects), with large correlations seen with Well-Being during Attack and total GIS Score (r = 0.37–0.39). SF-36 MCS showed negative correlations with all GIS scales; large correlations were seen with Well-Being during Attack and total GIS score (r = −0.43 −0.46). SF-36 PCS showed moderate negative correlation with total GIS score (r = −0.33) and large negative correlation with Well-Being during Attack (r = −0.40). Physician severity score showed a moderate correlation with the total GIS Score (r = 0.25). Patient severity score showed a moderate correlation with the total GIS Score (r = 0.36).
Longitudinal data analyses
The mean (s.d.) time elapsed from first to last visit at the Los Angeles site was 514 (146) days; elapsed time from first to last visit was not collected at the Birmingham site. During follow-up, there was a decrease in the proportion of subjects experiencing a gout attack, with 68, 62, 59 and 58% of subjects experiencing a gout attack in the 3 months before visits 1, 2, 3 and 4, respectively. In subjects who reported an improvement in gout attacks, there was a mean improvement in the total GIS score of 12.7 units, compared with a 3.5 unit improvement among those who experienced no change or an increase in annual gout attack incidence (P < 0.01).
At follow-up, subjects with improved physician severity scores (i.e. scores at follow-up lower than scores at baseline) also had improved GIS scores in all scales relative to subjects with unimproved physician severity scores. Relative to those with unimproved scores, subjects with improved patient severity scores at follow-up had numerically lower GIS scores in all scales, with significant improvement in all GIS scales except Unmet Gout Treatment Need (P ⩽ 0.03 for all scales except Unmet Gout Treatment Need; data not shown).
Responsiveness to change
At the final visit, improvements were seen in the mean scores on all five GIS scales as well as in the mean total GIS score (Table 4). ES ranged from 0.07 to 0.34 and SRM from 0.09 to 0.45 among the scales. Relative to the overall cohort, subjects who had a gout attack in the 3 months before enrolment showed lower (improved) mean GIS scale scores, ES and SRM in all scales except Gout Medication Side Effects, with correspondingly larger scores in ES in all scales except Gout Medication Side Effects (as above) and Gout Concern Overall (Table 4).
Table 4.
Change in GIS scores, visit 1 to visit 4
| Overall cohort, n = 147 |
No attack in the 3 months prior to enrolment, n = 46 |
Attack in the 3 months prior to enrolment, n = 100 |
|||||||
|---|---|---|---|---|---|---|---|---|---|
| GIS change, mean (s.d.) | ES | SRM | GIS change, mean (s.d.) | ES | SRM | GIS change, mean (s.d.) | ES | SRM | |
| Gout Concern Overall | −9.1 (21.2) | 0.34 | 0.43 | −5.7 (18.9) | 0.20 | 0.30 | −10.8 (22.2) | 0.46 | 0.49 |
| Gout Medication Side Effects | −2.0 (22.6) | 0.07 | 0.09 | −5.2 (23.4) | 0.21 | 0.22 | −0.6 (22.4) | 0.02 | 0.03 |
| Unmet Gout Treatment Need | −4.1 (18.8) | 0.21 | 0.22 | −0.5 (9.5) | 0.05 | 0.05 | −5.7 (21.7) | 0.28 | 0.26 |
| Well-Being during Attack | −6.6 (19.5) | 0.24 | 0.34 | −2.7 (19.4) | 0.10 | 0.14 | −8.5 (19.4) | 0.31 | 0.44 |
| Gout Concern during Attack | −3.9 (19.4) | 0.15 | 0.20 | −3.0 (19.3) | 0.12 | 0.16 | −4.3 (19.7) | 0.17 | 0.22 |
| Total GIS | −5.8 (12.8) | 0.29 | 0.45 | −3.1 (12.8) | 0.16 | 0.24 | −7.2 (12.8) | 0.37 | 0.56 |
ES: effect size; GIS: Gout Impact Scale; SRM: standardized response mean.
Predictors of total GIS score over time
Subjects’ age, physician severity score and experience of gout attack in the previous 3 months were significantly predictive of total GIS score in a linear mixed model adjusting for random patient effect and visit number (Table 5). Subjects’ baseline sUA, race, disease duration, tophi diagnosis and diagnosis of moderate/severe renal disease, myocardial infarction and chronic pulmonary disease were eliminated from the multivariate model through backward selection. In a separate model, where CCI was used instead of medical conditions, it was excluded from the regression model.
Table 5.
Multivariate mixed model predicting total Gout Impact Scale score
| Effect | Estimate | Standard error | P-value |
|---|---|---|---|
| Age | −0.05 | 0.02 | <0.01 |
| Physician Severity Score | 1.27 | 0.34 | <0.01 |
| Gout attack in previous 3 months | 9.63 | 1.74 | <0.01 |
Discussion
The GIS is a validated disease-specific patient-reported outcome measure in gout. Our data suggested that the newly derived total GIS score is reliable, valid and responsive to change in subjects with gout. It demonstrated good internal consistency, showed moderate to large correlations with patient severity scores, correlated well with other validated HRQOL measures and demonstrated responsiveness to change using physician- and patient-reported anchors. Total GIS is also able to differentiate between subjects with higher and lower flare frequency at baseline and between subjects with and without improvement in flare frequency at follow-up.
GIS was considered by OMERACT 10 [7] but not endorsed pending further studies of construct validity relative to established HRQOL metrics and responsiveness to change. At the time of its construction, Cronbach’s α was sufficient for all scales (0.86–0.97) in a cohort of 308 subjects recruited from three US cities and a variety of treatment settings [5]. Reliability was redemonstrated by Spaetgens et al. [14] in a population of 126 Dutch subjects from rheumatology clinics (adjusted Cronbach’s α = 0.83–0.94) for all scales except Medication Side Effects (α = 0.51) and by Khanna et al. [12] in a multicentre randomized control trial of 73 subjects with inter-critical gout and two or more flares in the past year [adjusted Cronbach’s α = 0.79–0.94 for all scales except Unmet Gout Treatment Need (0.59) and Medication Side Effects (not evaluated)]. Sarkin et al. [24] used the original cohort and demonstrated that GIS scales, particularly Gout Concern Overall, Gout Concern during Attack and Gout Well-Being during attack, correlated more strongly with patient-reported disease severity ratings than with physician-reported ratings, whereas objective variables tended to be more correlated with physician-reported severity. There is only one study by Khanna et al. [12] that assessed the GIS in a longitudinal study. Minimally important differences were calculated for the scales and showed that subjects with higher GIS scale scores reported marked improvement in symptoms relative to those reporting minimal change, no change or worsening.
Our present observation adds to previous literature. As in previous studies, we showed GIS to have acceptable internal consistency reliability for all GIS scales, except Unmet Gout Treatment Need and Gout Medication Side Effects, and to have strong correlations with patient severity scores. Gout Medication Side Effects has been shown to have less than satisfactory internal consistency in different cohorts, possibly because of lack of a recall period for GIS. For example, the response to the Gout Medication Side Effects question might differ if recall period was 1 rather than 4 weeks, and might depend on the time of last gout attack. Our data also showed moderate to large correlations between GIS scales and previously validated HRQOL metrics. Most GIS scales correlated well with SF-36 MCS in our population; our newly proposed total GIS score and Well-Being during Attack score correlated well with SF-36 PCS and HAQ-DI, suggesting that they better reflect decreased HRQOL resulting from physical impairment, whereas other scales reflect the mental/emotional impact of gout.
Our data also provided the first evidence of responsiveness to change of GIS scales. Improvement in GIS scale scores over time tracked well with improvement in patient and physician severity scores. Interestingly, subjects with gout attacks in the 3 months prior to enrolment had more improvement in all GIS scales, except for Gout Medication Side Effects, than those without attacks. The subgroup with recent attacks might represent acute gout patients with intermittent flares, and the subgroup without might represent chronic gout patients. It makes sense that patients with acute flares would feel that medications are more effective at relieving symptoms, and be more willing to tolerate more side effects, than would patients with chronic gout pain. There is also likely to be a component of regression to the mean in patients with recent attacks, with scores increasing relative to the mean during an attack and decreasing below it afterwards.
We also performed a multivariate analysis of GIS. Worsening physician severity score and gout attack in the previous 3 months predicted worsening of the total GIS score, which is consistent with the analysis reported above. Increasing age predicted very slight but significant improvement in the total GIS score, independent of self-reported co-morbidities.
This study has several strengths. It is the first longitudinally designed evaluation of GIS to have answered some important concerns regarding GIS. Specifically, the longitudinal study design also allowed us to demonstrate responsiveness of the GIS to change relative to all measures above. In addition, rich clinical and HRQOL data provided important associations of GIS scales. We have also proposed a total GIS score, a single composite score, which we demonstrated to be reliable, valid, responsive to change and associated with the presence of a recent gout attack (a surrogate for gout activity) and physician severity score in the multivariate analysis. There are obviously some limitations of the study, which include lack of information about subjects’ treatment plans. Specifically, we did not capture gout-specific medications in detail because the goal of the cohort was to assess HRQOL in a longitudinal fashion. Also, as this was a longitudinal multicentre cohort study, missing follow-up data were unavoidable.
In conclusion, total GIS score of GAQ2.0 has acceptable reliability, construct validity and responsiveness to change. Total GIS was also able to discriminate between subjects with and without improvement in flare frequency at follow-up. Based on OMERACT filters, total GIS score is acceptable for future observational studies and randomized control trials.
Acknowledgements
Dinesh Khanna was supported by the National Institutes of Health/NIAMS K24 AR063120.
Funding: This work was supported by American College of Rheumatology-REF Clinical Investigator Fellowship Award to P.P.K. and by an unrestricted grant from Savient Pharmaceuticals to D.K.
Disclosure statement: P.P.K. is supported by research grants from AstraZeneca and has served as consultant to Crealta and Takeda. D.K. has received a research grant from AstraZeneca and is a consultant for AstraZeneca and Takeda. J.A.S. has received research grants from Takeda and Savient and consultant fees from Savient, Takeda, Regeneron, Iroko, Merz, Bioiberica, Crealta and Allergan pharmaceuticals. J.A.S. serves as the principal investigator for an investigator-initiated study funded by Horizon pharmaceuticals through a grant to DINORA, Inc., a 501c3 entity; they are a member of the executive of OMERACT, an organization that develops outcome measures in rheumatology and receives arms-length funding from 36 companies; a member of the American College of Rheumatology’s Guidelines Subcommittee of the Quality of Care Committee; and a member of the Veterans Affairs Rheumatology Field Advisory Committee. All other authors have declared no conflicts of interest.
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