Development of a prediction model for inpatient gout flares in people with comorbid gout.
Ann Rheum Dis
; 79(3): 418-423, 2020 03.
Article
en En
| MEDLINE
| ID: mdl-31811060
ABSTRACT
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.Palabras clave
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Árboles de Decisión
/
Modelos Estadísticos
/
Medición de Riesgo
/
Reglas de Decisión Clínica
/
Gota
Tipo de estudio:
Etiology_studies
/
Observational_studies
/
Prognostic_studies
/
Risk_factors_studies
Límite:
Adult
/
Female
/
Humans
/
Male
/
Middle aged
País como asunto:
Oceania
Idioma:
En
Año:
2020
Tipo del documento:
Article