Prevalence, Risk Factors, and Outcomes of Gout Flare in Patients Hospitalized for PCR-Confirmed COVID-19: A Multicenter Retrospective Cohort Study
Bibliographic record
Abstract
OBJECTIVE: The study aimed to describe the prevalence and outcomes of gout flare in patients with comorbid gout hospitalized for coronavirus disease 2019 (COVID-19). Factors associated with gout flare and hospital length of stay were explored. METHODS: This retrospective cohort study included adults with comorbid gout who were hospitalized for PCR-confirmed COVID-19 between March 2020 and December 2021 in 3 hospitals in Thailand. Prevalence, characteristics, and outcomes of gout flare were described. Factors associated with gout flare were explored using least absolute shrinkage and selection operator selection and multivariate logistic regression. The association between gout flare and hospital length of stay was explored using multivariate linear regression. RESULTS: Among 8697 patients hospitalized for COVID-19, 146 patients with comorbid gout were identified and gout flare occurred in 26 (18%). Compared to those without flare, patients with gout flare had higher baseline serum urate and lower prevalence of use of urate-lowering therapy (ULT) and gout flare prophylaxis medications. One-third of gout flare episodes were treated with ≥ 2 antiinflammatory medications. Logistic regression identified GOUT-36 rule ≥ 2, a predictive index for inpatient gout flare, as the only factor associated with gout flare (odds ratio 5.46, 95% CI 1.18-25.37). Gout flare was found to be independently associated with hospital length of stay and added 3 days to hospital course. CONCLUSION: Gout flare occurred in 18% of patients with comorbid gout hospitalized for COVID-19 and added up to 3 days to hospital length of stay. Patients with suboptimal ULT appeared to be at high risk for gout flare during COVID-19 hospitalization.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".