Abstract
Background.
Initiating urate-lowering therapy (ULT) in gout can precipitate arthritis flares. There have been limited comparisons of flare risk during the initiation and escalation of allopurinol and febuxostat, administered as a treat-to-target strategy with optimal anti-inflammatory prophylaxis.
Methods.
This was a post-hoc analysis of a 72-week randomized, double-blind, placebo-controlled, non-inferiority trial comparing the efficacy of allopurinol and febuxostat. For this analysis, the occurrence of flares was examined during weeks 0–24 when ULT was initiated and titrated to a serum urate (sUA) goal of <6 mg/dl (<5 mg/dl if tophi). Flares were assessed at regular intervals through structured participant interviews. Predictors of flare, including treatment assignment, were examined using multivariable Cox proportional hazards regression.
Results.
Study participants (n=940) were predominantly male (98.4%) and had a mean age of 62.1 years with approximately equal proportions receiving allopurinol or febuxostat. Mean baseline sUA was 8.5 mg/dl and all participants received anti-inflammatory prophylaxis (90% colchicine). In a multivariable model, there were no significant associations of ULT treatment (HR 1.17; febuxostat vs. allopurinol), ULT dose escalation (HR 1.18 vs. no escalation), prophylaxis type, or individual comorbidity with flare and no evidence of ULT-dose escalation interaction. Factors independently associated with flare risk during ULT initiation/escalation included younger age, higher baseline sUA, and absence of tophi.
Conclusion.
These results demonstrate that gout flare risk during the initiation and titration of allopurinol is similar to febuxostat when these agents are administered according to a treat-to-target strategy using gradual ULT dose titration and best practice gout flare prophylaxis.
Keywords: gout flare, allopurinol, febuxostat, treat-to-target, urate-lowering therapy
Introduction
Gout is the most common form of inflammatory arthritis worldwide, affecting 5% of adults and more than 12 million people in the United States (U.S.) alone (1). International subspecialty guidelines endorse treat-to-target urate-lowering therapy (ULT) as a cornerstone of effective gout management by optimally promoting the dissolution of articular and periarticular monosodium urate (MSU) deposits and related tophi in addition to reducing or in many cases even eliminating the risk of gout flares. Unfortunately, guideline concordant ULT initiation and dose escalation central to a treat-to-target strategy can also precipitate gout flares that can serve to undermine patient adherence, decrease patient health-related quality of life and work participation (2–7).
Increases in flare burden that accompany ULT initiation have been attributed to the disruption of insoluble MSU crystals due to serum urate (sUA) fluctuations (8). If the disruption and subsequent shedding of crystals is large enough, the crystals that are shed can initiate inflammation resulting in flare (9,10). This concept of rapid sUA fluctuations or declines leading to disruption and shedding was further supported by evidence demonstrating that the likelihood of experiencing a flare post-ULT initiation early in the treatment course was directly related to the magnitude of sUA reduction achieved from baseline (4). Recognizing gout flares as a ‘physiologic’ response to highly effective ULT, current best practices also advocate for the administration of anti-inflammatory prophylaxis, typically accomplished through the simultaneous use of colchicine, non-steroidal anti-inflammatory drugs (NSAIDs) or low-dose glucocorticoids during the initiation and early administration of ULT (11,12). Current guidance from the American College of Rheumatology (ACR), for example, strongly recommends the initiation and maintenance of anti-inflammatory prophylaxis extending through the first 3 to 6 months of ULT.
Though gout flares are a well-recognized consequence of the early phases of treat-to-target therapy and available data suggests that this risk varies based on differences in the rate of sUA reductions achieved, there is a paucity of data directly comparing flare risk associated with the initiation of different ULT agents. This is highly relevant, as sUA reductions that one can expect to achieve with the two most commonly used ULTs (allopurinol and febuxostat), using recommended starting daily doses (≤100 mg and 40 mg, respectively), appears to vary markedly. A prior randomized study demonstrated that the magnitude of sUA lowering achieved with febuxostat 40 mg daily approaches that of allopurinol 300 mg daily (13). While this suggests that the risk of flare attributable to allopurinol or febuxostat might differ during initiation and dose escalation phases of best practice treat-to-target management in gout, there have been limited investigations to date examining this important clinical question.
To address this knowledge gap, we conducted a post-hoc analysis of the STOP Gout trial (ClinicalTrials.gov identifier, NCT02579096), a comparative effectiveness study that examined the efficacy of allopurinol and febuxostat with both agents administered in a treat-to-target framework (14). We tested the hypothesis that even in the context of appropriate flare prophylaxis and gradual ULT dose titration, patients initiating febuxostat would experience more profound sUA reductions and as a result demonstrate a higher risk of flare than patients initiating allopurinol. We also tested the hypothesis that flare risk would be associated temporally with ULT dose escalation and this risk would be higher among those receiving febuxostat than allopurinol.
Methods
Data source.
The STOP Gout study was a multicenter, randomized, double-blind, placebo-controlled, 72-week non-inferiority trial comparing allopurinol to febuxostat in 940 participants with a primary outcome of flare occurring during the final study phase (weeks 48–72) (14). By design, approximately one-third of participants had stage 3 chronic kidney disease (CKD). Individuals with more advanced CKD (stages 4 or 5) were excluded. The treatment protocol consisted of three phases of equal duration: ULT therapy initiation and titration (phase 1: weeks 0 to 24), maintenance (phase 2: weeks 25 to 48), and observation (phase 3: weeks 49 to 72). During phase 1, those randomly assigned to allopurinol or febuxostat were initiated at daily doses of 100 mg and 40mg, respectively, with doses gradually escalated to achieve a sUA below 6.0mg/dl (<5.0mg/dl if tophi were present) or until the maximal allowable ULT dose was reached. The dose titration schedule (shown in Supplemental Figure 1) during phase 1 followed the 2012 ACR gout management guidelines (15,16), with maximum daily doses of 800 mg of allopurinol and 120 mg of febuxostat. The maximum allowable daily febuxostat dose was reduced from 120 mg to 80 mg during the study, in February of 2019, at the request of the U.S. Food and Drug Administration following a modification of febuxostat labeling to include a black box warning detailing cardiovascular safety concerns (17). Dose escalation was completed in 100 mg/day increases for allopurinol at intervals as frequent as every 3 weeks or 40 mg/day increases for febuxostat at intervals as frequent as every 9 weeks. Participants taking allopurinol ≤300 mg daily before the trial were eligible and continued taking their pretrial (blinded) allopurinol dose if relevant and randomly assigned to that arm, with dose titration delayed for those receiving 200 mg (starting at week 6) or 300 mg (starting at week 9) or, if randomly assigned to febuxostat, initiated at 40mg per day. All participants received guideline-directed anti-inflammatory prophylaxis per 2012 ACR guidelines (15) with colchicine, naproxen 250 mg twice daily, or prednisone ≤10 mg daily per investigator discretion that extended through the entirety of phase 1. For this analysis, colchicine dose was calculated as the median daily dose reported taken by participants (using medication diaries) during phase 1 and categorized as 0.6 mg every other day, 0.6 mg once daily or 0.6 mg twice daily (dose information missing for 10.2%).
Primary endpoint.
Time to occurrence of the first post-enrollment flare during phase 1 (0 to 24 weeks) served as the primary endpoint for this analysis. We focused on events occurring during the first study phase to specifically examine flares occurring as a possible consequence of ULT initiation and/or early dose escalation, requisite components of a treat-to-target strategy. Flares were captured throughout follow-up as part of structured interviews conducted by study personnel. Participants were defined as having experienced a gout flare if they met three of four participant-reported criteria (18) — warm joint(s), swollen joint(s), pain (>3) at rest on a scale of 0 to 10 (with higher numbers indicating more severe pain), or self-identified gout flare — or reported use of standard anti-inflammatory medications to treat a flare on a participant-reported questionnaire collected every 6 weeks. Measures of flare severity such as polyarticular vs. monoarticular involvement or those requiring ambulatory or urgent care were not available for this analysis.
Analyses.
Descriptive statistics were used to compare characteristics of participants assigned to allopurinol and febuxostat as previously reported (14). The total number of flares occurring during phase 1 and flare rates (per 100 patient-years of follow-up and over the entire 24-week observation period) were calculated and compared by ULT assignment using chi-square and t-tests, respectively. The probability of remaining flare free based on ULT assignment during phase 1 was examined visually using a Kaplan Meier plot. In addition to the descriptive comparisons above, we compared the change in sUA achieved from baseline to 24-weeks in participants experiencing ≥1 flare vs. those experiencing no flares.
In primary analyses, associations of ULT (febuxostat vs. allopurinol) with flare were examined using univariable and multivariable Cox proportional hazards regression with the at-risk period starting at trial enrollment and extending to the first of: flare, loss to follow-up or withdrawal, death, or the end of phase 1 after 24 weeks of observation. In addition to ULT assignment, baseline covariates examined included demographics (age, sex, self-reported race), gout-specific factors (prophylaxis used [colchicine vs. non-colchicine], gout duration, the presence of tophi, sUA concentration, prior allopurinol use, C-reactive protein (CRP) concentration, comorbidities (stage 3 CKD, hypertension, cardiovascular disease [CVD], diabetes), diuretic use, body mass index (BMI, kg/m2), and regular alcohol use. ULT dose escalation was modeled as a time-varying covariate by modeling whether an escalation occurred at each study visit in phase 1. We also examined for evidence of ULT-escalation interaction to account for possible differential effects of dose changes for the two ULTs. Identified a priori as possible risk factors for flare, all covariates were included in our multivariable model. This approach was used rather than backwards selection as the latter may exclude confounding factors and this exclusion might be particularly relevant since factors driving flare risk during early initiation and titration of ULT are unknown. Tests of the proportional hazards assumption demonstrated no evidence of violation (not shown).
In secondary analyses, the total number of flares occurring during phase 1 and flare rates (per 100 patient-years of follow-up and over the entire 24-week observation period) were calculated and compared by ULT assignment after excluding participants with allopurinol use at enrollment. Likewise, the aforementioned multivariable Cox proportional hazards regression analysis was repeated on the same sub-cohort, after excluding those with prior allopurinol use. These additional analyses were conducted as patients receiving allopurinol at enrollment may have experienced different rates of treatment escalation and less prominent fluctuations in sUA concentration.
Ethical considerations.
The STOP Gout study was approved by the Veterans Affairs Central Institutional Review Board and all participants provided written informed consent prior to enrollment. The STOP Gout trial steering committee provided approval for the conduct of this post-hoc analysis.
Results
Characteristics of the 940 study participants are shown in Table 1. Participants were primarily male (98.4%) and had a mean age of 62.1 years. As previously reported (14), there were no major differences in baseline characteristics between the treatment groups. Compared to those assigned febuxostat, participants in the allopurinol group were slightly older (mean 62.9 vs. 61.3 years) and more frequently had baseline cardiovascular disease (30.1% vs. 23.5%). Colchicine (89.6%) accounted for the vast majority of anti-inflammatory prophylaxis used, followed by NSAIDs and prednisone. Among those taking colchicine with dose data available (n=756/842), the most common dose was 0.6 mg once daily (88.1%) followed by 0.6 mg every other day (10.3%) and then 0.6 mg twice daily (1.6%).
Table 1:
Baseline characteristics of study participants
| Characteristic | Total (n=940) |
Allopurinol (n=468) |
Febuxostat (n=472) |
|---|---|---|---|
| Demographics | |||
| Age, years, mean ± SD | 62.1 (12.4) | 62.9 (11.8) | 61.3 (12.9) |
| Male, % | 98.4 | 98.5 | 98.3 |
| White | 67.8 | 67.3 | 68.2 |
| Comorbidities and Health Factors | |||
| Alcohol use, % | 55.3 | 53.6 | 57.0 |
| ≥35 (morbidly obese) | 35.3 | 34.8 | 35.8 |
| Cardiovascular disease, % | 26.8 | 30.1 | 23.5 |
| Diabetes, % | 33.3 | 35.0 | 31.6 |
| Diuretic use, % | 37.4 | 38.5 | 36.4 |
| Chronic kidney disease (Stage 3), % | 37.3 | 38.7 | 36.0 |
| Hypertension | 76.4 | 78.0 | 74.8 |
| Gout Related Factors | |||
| C-reactive protein, mg/L, mean ± SD | 8.9 (17.1) | 9.6 (18.7) | 8.2 (15.4) |
| Duration, years, mean ± SD | 10.0 (11.0) | 9.7 (10.6) | 10.2 (11.4) |
| Prior allopurinol (≤300 mg/day), % | 36.7 | 38.0 | 35.4 |
| Other | 2.8 | 3.6 | 1.9 |
| Serum urate (sUA), mg/dl, mean ± SD | 8.5 (1.4) | 8.6 (1.4) | 8.5 (1.3) |
| Tophi present, % | 16.2 | 17.3 | 15.0 |
NSAID, non-steroidal anti-inflammatory drug; ‘Other’ race combines Asian, American Indian or Alaska Native, Native Hawaiian or other Pacific Islander and ‘none of the above’; ‘Other’ prophylaxis includes combinations of treatments listed
Phase 1 gout flare frequency, flare rate, and the total number of ULT titrations by treatment assignment in the overall study population are shown in Table 2 (Panel A). During phase 1, at least one flare was observed in 44.1% of participants (42.1% for allopurinol and 46.2% for febuxostat). The mean (± standard deviation) change in sUA achieved over phase 1 of the study were similar among those experiencing at least 1 flare vs. those without flare (3.24 ± 1.87 mg/dl vs. 3.12 ± 1.68 mg/dl, respectively; p=0.34) (not shown). Participants <65 years of age were more likely than older participants to experience flare (51.1% vs. 37.6%). The proportion experiencing flare was also highest among those in the top quartile of baseline sUA (>9.4 mg/dl) compared to participants in the lower three quartiles (51.1% vs. 38.7% to 43.6%, respectively). Participants randomized to allopurinol were subjected to a significantly greater number of dose escalations compared to those administered febuxostat (median of 3 vs. 1). Flare rates and the proportion experiencing more frequent flares (2–3 flares or 4 or more flares) were similar by ULT treatment. Results were similar in the sub-cohort without prior allopurinol use (Table 2, Panel B) with 42.8% assigned allopurinol experiencing at least one flare during weeks 0 to 24 compared to 46.9% for febuxostat.
Table 2:
Urate-lowering therapy dose increases and gout flares during weeks 0 to 24 for all participants (panel A) and after excluding those with prior allopurinol use (panel B)
| A. | Total (n=940) |
Allopurinol (n=468) |
Febuxostat (n=472) |
P-value |
|---|---|---|---|---|
| Dose increases, median (IQR) | 2 (2) | 3 (2) | 1 (1) | <0.001 |
| Flares per 100 patient-years (95% CI) | 217.5 (203.6–232.3) | 209.5 (190.5–230.4) | 225.4 (205.8–246.9) | 0.28 |
| ≥4 flares | 6.0 | 6.2 | 5.7 | |
| B. | Total (n=595) |
Allopurinol (n=290) |
Febuxostat (n=305) |
P-value |
| Dose increases, median (IQR) | 2 (2) | 3 (2) | 1 (1) | <0.001 |
| Flares per 100 patient-years (95% CI) | 209.7 (192.8–228.1) | 199.8 (176.6–226.0) | 219.2 (195.4–245.9) | 0.28 |
| ≥4 flares | 6.1 | 6.2 | 5.9 |
The probability of remaining flare free during phase 1 for the overall cohort is shown in Figure 1, demonstrating similar flare burden by treatment assignment. In a univariable Cox proportional hazards model, we observed no difference in the risk of flare for febuxostat compared to allopurinol (HR 1.07; 95% CI 0.89 to 1.30) (Figure 2). Univariable and multivariable results for other predefined covariates are shown in Table 3. Results relevant to risk of flare for febuxostat vs. allopurinol remained unchanged in a multivariable model adjusting for predefined covariates (aHR 1.17; 95% CI 0.90 to 1.53) (Figure 2). In the multivariable model examined, there was no evidence of increased flare risk associated with dose escalation occurring during follow-up (aHR 1.18; 95% CI 0.86 to 1.63) and no evidence of a ULT-dose escalation interaction (p = 0.66). After multivariable adjustment, factors independently associated with increased flare risk included higher baseline sUA (aHR 1.09; 95% CI 1.01 to 1.18 per mg/dl) and younger age (HR 0.86; 95% CI 0.78–0.96 per 10 years) (Table 3). Baseline CRP (aHR 1.05; 95% 1.00–1.10 per 10 mg/L) and the presence of stage 3 CKD (aHR 1.24; 95% CI 0.97 to 1.59) trended towards a higher risk of flare, although neither factor achieved statistical significance. The presence of tophi (aHR 0.70; 95% CI 0.54 to 0.91) at enrollment was associated with a lower flare risk.
Figure 1:
Time to First Flare with Treat-to-Target Urate Lowering Therapy (ULT) Initiation. Participants initiated allopurinol (solid line) or febuxostat (dotted line) with appropriate anti-inflammatory prophylaxis; ULT dose was titrated as part of a treat-to-target strategy over a 24-week period. The Kaplan-Meier curve depicts flare-free survival for participants.
Figure 2:
Risk Factors for Gout Flare with Urate Lowering Therapy (ULT). Hazard ratios and 95% confidence intervals shown for univariable (A) and multivariable (B) analyses.
Table 3:
Unadjusted and adjusted associations of predefined covariates and the risk of flare between weeks 0 and 24 (n=940)
| Characteristic | Unadjusted HR (95% CI) |
Adjusted HR (95% CI) |
|---|---|---|
| Demographics | ||
| Age, per 10 years | 0.84 (0.78–0.90) | 0.86 (0.78–0.96) |
| Male | Ref. | Ref. |
| White | Ref. | Ref. |
| Comorbidities and Health Factors | ||
| Alcohol use | 0.84 (0.69–1.02) | 0.91 (0.74–1.12) |
| ≥35 (morbidly obese) | 1.01 (0.65–1.55) | 0.97 (0.62–1.52) |
| Cardiovascular disease | 1.07 (0.86–1.33) | 1.00 (0.77–1.30) |
| Diabetes | 1.05 (0.86–1.29) | 0.99 (0.78–1.25) |
| Diuretic use | 1.05 (0.86–1.28) | 0.97 (0.76–1.25) |
| Chronic kidney disease (Stage 3) | 1.35 (1.10–1.66) | 1.24 (0.97–1.59) |
| Hypertension | 1.06 (0.85–1.33) | 0.90 (0.69–1.17) |
| Gout Related Factors | ||
| C-reactive protein, per 10 mg/L | 1.05 (1.00–1.10) | 1.05 (1.00–1.10) |
| Duration, per year | 1.00 (0.99–1.01) | 1.00 (0.99–1.01) |
| Prior allopurinol (≤300 mg/day), % | 1.03 (0.84–1.25) | 0.95 (0.76–1.19) |
| Non-Colchicine | 1.26 (0.94–1.70) | 1.15 (0.84–1.57) |
| Serum urate (sUA), per 1 mg/dl | 1.10 (1.03–1.17) | 1.09 (1.01–1.18) |
| Tophi present | 0.75 (0.58–0.95) | 0.70 (0.54–0.91) |
Adjusted models include all variables shown in addition to urate-lowering therapy (ULT) assignment (febuxostat vs. allopurinol), ULT dose escalation and a ULT*dose escalation product term (see Figure 2)
Results of secondary analyses excluding patients taking allopurinol (≤300 mg daily) at enrollment (n=345 or 36.7% excluded) are shown in Supplemental Table 1. In a univariable model, after excluding those with prior allopurinol exposure, there was again no association of ULT assignment with flare (HR 1.09; 95% CI 0.86–1.39; febuxostat vs. allopurinol). After accounting for the same aforementioned covariates, there were trends suggesting a slightly higher flare risk with the use of febuxostat (aHR 1.33; 95% CI 0.92–1.93) and with ULT dose escalation (aHR 1.47; 95% CI 0.95–2.29) during phase 1, though neither of these factors achieved statistical significance. As in the primary analyses, there was no evidence of a ULT-dose escalation interaction (p=0.21) after excluding those with pre-enrollment allopurinol use. In this secondary multivariable analysis, there were no significant associations between other covariates and flare risk during phase 1 with the exception of age (aHR 0.87; 95% CI 0.77–0.99 per 10 years) (Supplemental Table 1).
Discussion
In this post-hoc analysis using data from the STOP Gout trial, we found no major differences in the risk of gout flare between allopurinol and febuxostat during the initiation and titration of ULT using a treat-to-target management strategy in the context of guideline concordant anti-inflammatory prophylaxis. Specifically, results from both the full study cohort and a sub-cohort that excluded those with prior allopurinol use demonstrated that compared to febuxostat the use of allopurinol was associated with a non-significant 4.1% reduction in the absolute risk of experiencing one or more gout flares during the first six months of treat-to-target ULT. In addition to failing to demonstrate a meaningful risk difference between allopurinol and febuxostat, and contrary to our a priori hypothesis, this analysis also failed to show a meaningful increase in gout flare risk related to ULT dose escalations occurring in the context of appropriate anti-inflammatory prophylaxis.
These findings are perhaps counter to findings from previous CONFIRMS and FACT trials, which also compared the efficacy of allopurinol and febuxostat (5,13,19). These regulatory trials demonstrated that higher doses of febuxostat (≥80 mg/day) were associated with higher flare risk compared to 200 to 300 mg of daily allopurinol. There are important differences, however, across these study protocols to consider when interpreting these varied results. In contrast to STOP Gout and CONFIRMS, in which participants were given anti-inflammatory prophylaxis through the first 6 months of ULT, participants in the FACT study received prophylaxis during only the first 8 weeks of ULT. While these early regulatory studies used a fixed-dose approach throughout follow-up, participants in the STOP Gout study received guideline concordant treat-to-target therapy with the use of initial low doses followed by gradual ULT dose titration.
Data available from this large multicenter trial afforded the unique opportunity to examine several other factors possibly associated with early flare beyond ULT type and dose escalation. After accounting for covariates, we found that for each 1 mg/dl higher baseline sUA concentration participants had an approximate 10% increase in flare risk during the first 6 months of ULT. These results are consistent with those from a recent 12-week study of 282 men with gout initiating febuxostat in the absence of anti-inflammatory prophylaxis (20). Adjusting for relevant covariates, these authors found that higher baseline sUA and gout flare burden in the year preceding ULT initiation were the only factors independently associated with flares experienced during treatment.
In addition to baseline sUA, others have shown that the magnitude of change in sUA accomplished via early ULT may also drive flare risk (4,21,22). In the current study, we selected to examine ULT type and dose escalation events as surrogates of sUA change as the time intervals spanning laboratory assessments could overlap with flare occurrence, raising the distinct possibility of reverse causation. This possibility relates to the fact that flare occurrence by itself can exert a urate-lowering effect (23), thus potentially leading to an increased sUA difference when flare occurs prior to the final laboratory measure. Importantly, in adjusted assessments of flare risk accompanying ULT initiation, Pang and colleagues found that the association of sUA difference over the course of this 12-week study was attenuated and no longer significant after accounting for baseline concentrations and flare burden prior to study enrollment. In our post-hoc assessment, we observed similar levels of sUA reduction over phase 1 regardless of whether patients experienced a flare or not. Results reported by Pang et al, coupled with those from our post-hoc analysis, suggest that existing gout burden at the time of treat-to-target ULT initiation may be more predictive of early flare risk than the magnitude of sUA change achieved. Unfortunately, data on pre-enrollment flare burden and other measures of gout severity were not available for the STOP Gout study, prohibiting assessments of how such factors might influence flare risk.
In contrast to another study showing that the presence of subcutaneous tophi predicted higher flare risk with ULT initiation/titration in the context of colchicine prophylaxis during the first 3–6 months of therapy (21), our data showed an unexpected “protective” effect. Specifically, patients with tophi at enrollment were 30% less likely than those without tophi to experience flare during the first six months of ULT. Whether the size or number of tophi might further predict flare risk is unknown as these data were not collected as part of the study. Mechanisms underpinning this unexpected finding are not clear and require further exploration. The consistency of findings across unadjusted and adjusted analyses in both the full and reduced cohorts suggests that this is not simply a product of model performance or overfitting. Recognizing that tophaceous gout is characterized by chronic inflammation, it is possible that patients with more severe gout are less likely to identify acute-onset, self-limited flares. Indeed, the criteria used to define flare in our study may lack sensitivity in patients with chronic tophaceous disease (18).
Other factors associated with flare risk following the initiation of treat-to-target ULT included younger age and a higher baseline CRP concentration, the latter not achieving statistical significance (adjusted p-value 0.051). In addition to its association with early flare, we and others have shown that younger patients with gout are also less likely to achieve recommended sUA thresholds when subjected to treat-to-target ULT (24,25). The consistency of our findings between univariable and multivariable models suggests that this association is not driven by age-related differences in diuretic use or the prevalence of comorbidities such as hypertension, diabetes, obesity, renal insufficiency or cardiovascular disease, factors that are more frequent in gout (26–30) but not associated with flare in our analyses. A potential possibility is that age-related changes in ULT metabolism or changes in the susceptibility of xanthine oxidase to the agents studied might drive these differences. Trends observed referent to CRP are consistent with those of a previous report finding that higher CRP values were predictive of flare recurrence in patients initiating ULT during an acute flare (31). This intriguing result suggests that CRP and possibly other inflammatory biomarkers more closely linked to gout pathogenesis might help to risk stratify patients initiating ULT.
There are limitations to this study. Data from this post-hoc analysis came primarily from a U.S. veteran population. Although this study population is similar to those reported in other gout trials (5,13,19,32), results may not be universally generalizable. This is particularly true among women, who comprised less than 2% of the STOP Gout cohort. In addition to bias inherent to any unplanned post-hoc analyses, other limitations include the possibilities of flare misclassification and unmeasured confounding. This effort also has notable strengths that include the comprehensive data and the large sample size available, representing among the largest studies to date to compare flare risk of the two most commonly used ULTs in clinical practice administered following current best practice strategies in gout management. Another consideration in interpreting these results is the slower pace of dose escalation used for febuxostat (with dose increases possible at 9 and 18 weeks) compared to dose escalations occurring after 2 weeks of treatment as recommended by the manufacturer. Although designed to better mirror the gradual escalation achieved with allopurinol in this study, this more gradual dose titration schedule may have favored febuxostat in these analyses.
In conclusion, this post hoc analysis showed no significant difference in flare risk between allopurinol and febuxostat during initiation and titration according to a treat-to-target management strategy in the context of appropriate anti-inflammatory prophylaxis. Participant factors associated with gout flare risk during early stages of treat-to-target ULT included age, baseline sUA concentration, and the presence of tophi. These factors, and others identified in future research, may help to risk stratify patients initiating ULT and identify individuals who might benefit from more robust anti-inflammatory prophylaxis.
Supplementary Material
Funding:
This study was funded by the VA Cooperative Studies Program of the Department of Veterans Affairs Office of Research & Development (ClinicalTrials.gov identifier, NCT02579096). TRM receives research support from the VA (Merit grant BX004600, BX003635) and the National Institutes of Health (U54 GM115458). TN is supported by a grant from the National Institutes of Health (K24 AR070892, P30 AR072571). BRE is supported by a VA CSR&D (IK2 CX002203) and the Rheumatology Research Foundation. MHP is supported by grants from the National Institutes of Health (1UL1 TR001445) and the VA (I01 CX002358).
Disclosures/Conflicts of Interest:
TRM has served as a consultant for Horizon Therapeutics, Olatech Therapeutics, Pfizer, UCB, and Sanofi and receives research support from Horizon. BRE has received consulting fees and research support from Boehringer-Ingelheim. MHP has served as a consultant for Horizon Therapeutics, Sobi, Federation Bio, Fortress Bioscience and Scilex, and receives research support from Hikma Pharmaceuticals. The views expressed in this article are those of the authors and do not necessarily reflect the position or policy of the Department of Veterans Affairs or the United States government.
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