Development and validation of a multidimensional and interpretable artificial intelligence model to predict gout recurrence in hospitalised patients: a real-world, ambispective multicentre cohort study in China.

ABSTRACT:

Background

Gout is the most common inflammatory arthritis. Recurrent flares are common among hospitalised patients and contribute to substantial clinical and economic burden. However, the accurate prediction of inpatient recurrence remains challenging, particularly in individuals with comorbid gout. This study aims to evaluate the predictive value of variables and develop a multidimensional, interpretable artificial intelligence (AI) model to predict gout recurrence (GoutRe).

Methods

We conducted a real-world, retrospective, prospective, multicentre cohort study across five tertiary hospitals in China (2010-2024), enrolling 6,526 hospitalised patients with comorbid gout. A total of 82 multidimensional features encompassing laboratory findings, comorbidities, medications, and

SUBMITTER: Li M 

PROVIDER: S-EPMC12584441 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature