Table 2.

Multivariable models exploring determinants of joint damage in patients with gout separately for (a) erosions using zero-inflated negative binomial regressions and (b) joint space narrowing (JSN) using negative binomial regressions.

(a)Not Being Erosive*Erosion Score (count)¥
βOR95% CI (OR)pΒExp(β)95% CI [Exp(β)]p
Age, yrs−0.150.860.74–0.990.0360.051.061.02–1.090.002
Sex, female3.3628.800.87–955.740.060.501.650.72–3.750.23
Disease duration, yrs0.041.041.01–1.070.018
No. tophi0.071.071.03–1.120.001
sUA ≤ 0.36 mmol/l, yes/no4.3980.531.25–5192.790.039
(b)JSN score (count)¥
Age, yrs####–1.020.63
Sex, female####−0.130.880.55–1.390.58
Tophaceous gout, yes/no####0.571.761.23–2.530.002
  • Significant values are shown in bold face.

  • * Logistic model, predicting being nonerosive (erosion score being “certain zero”).

  • ¥ Negative binomial model, predicting expected count.

  • Factor change in odds for 1-unit increase in the independent variable.

  • Factor change in expected count for 1-unit increase in the independent variable. # No estimates, because a negative binomial model has no “certain zeros.” sUA: serum uric acid.