Latent Class Growth Analysis of Gout Flare Trajectories: A Three-Year Prospective Cohort Study in Primary Care.

OBJECTIVE

To investigate the existence of distinct classes of gout flare trajectories and compare their gout-specific, comorbid, and sociodemographic characteristics.

METHODS

In a prospective cohort study, adults with gout who were registered with 20 general practices self-reported the number of gout flares experienced at baseline and after 6, 12, 24, and 36 months via postal questionnaires. Latent class growth analysis (LCGA) was used to identify distinct gout flare trajectory classes. Statistical criteria and clinical interpretability were used to decide the optimal number of classes. Baseline comorbidities, medications, and sociodemographic and gout-specific characteristics of members of each class were described.

RESULTS

A total of 1,164 participants (mean ± SD age 65.6 ± 12.5 years; 972 [84%] male) were included. Six latent gout flare trajectory classes were identified: "frequent and persistent" (n = 95), "gradually worsening" (n = 276), "frequent then improving" (n = 14), "moderately frequent" (n = 287), "moderately frequent then improving" (n = 143), and "infrequent" (n = 349). The "frequent and persistent" trajectory had the most class members classified as obese and, along with the "gradually worsening" class, the highest proportion who were socioeconomically deprived. The "frequent and persistent," "gradually worsening," and "frequent then improving" classes had the highest proportions of class members with an estimated glomerular filtration rate <60 ml/minute/1.73 m2 . The "infrequent" gout flare class was associated with more frequent allopurinol use and lower urate levels.

CONCLUSION

Six distinct gout flare trajectories were identified. Infrequent flares were associated with allopurinol use and lower serum urate levels, supporting the use of urate-lowering therapy to reduce flare frequency. The characteristics of flare trajectory classes could help to target interventions and improve patient care.