Development of a prediction model for inpatient gout flares in people with comorbid gout

Hospitalisation is a risk factor for flares in people with gout. However, the predictors of inpatient gout flare are not well understood. The aim of this study was to develop a prediction model for inpatient gout flare among people with comorbid gout.

We used data from a retrospective cohort of hospitalised patients with comorbid gout from Wellington, Aotearoa/New Zealand, in 2017 calendar year. For the development of a prediction model, we took three approaches: (A) a clinical knowledge-driven model, (B) a statistics-driven model and (C) a decision tree model. The final model was chosen based on practicality and performance, then validated using bootstrap procedure.

The cohort consisted of 625 hospitalised patients with comorbid gout, 87 of whom experienced inpatient gout flare. Model A yielded 9 predictors of inpatient gout flare, while model B and C produced 15 and 5, respectively. Model A was chosen for its simplicity and superior C-statistics (0.82) and calibration slope (0.93). The final nine-item set of predictors were pre-admission urate >0.36 mmol/L, tophus, no pre-admission urate-lowering therapy (ULT), no pre-admission gout prophylaxis, acute kidney injury, surgery, initiation or increase of gout prophylaxis, adjustment of ULT and diuretics prior to flare. Bootstrap validation of the final model showed adequate C-statistics and calibration slope (0.80 and 0.78, respectively).

We propose a set of nine predictors of inpatient flare for people with comorbid gout. The predictors are simple, practical and are supported by existing clinical knowledge.