Objectives 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.
Methods 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.
Results 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).
Conclusion 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.
- gout flare
- inpatient gout
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Handling editor Josef S Smolen
Contributors KJ and WT designed the study. KJ collected the data. KJ, RG, ND and WT were involved in the data analysis, data interpretation and model development. All authors were involved in manuscript preparation and have approved the submitted version of the manuscript.
Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.
Competing interests RG reports grants and personal fees from AbbVie, personal fees from Janssen and personal fees from Pfizer, outside submitted work. ND reports grants and personal fees from AstraZeneca, grants from Amgen, personal fees from Dyve, personal fees from Hengrui, personal fees from Horizon, personal fees from Kowa, personal fees from Abbvie, personal fees from Pfizer, personal fees from Janssen, outside the submitted work. KJ and WT have nothing to disclose.
Patient consent for publication Not required.
Ethics approval The Human Research Ethics Committee, University of Otago, reviewed and approved the study protocol (reference number H18/012).
Provenance and peer review Not commissioned; externally peer reviewed.
Data availability statement All data relevant to the study are included in the article or uploaded as supplementary information.
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