Seizure-Induced Hyperuricemia and Associated Urate Nephropathy: A Prospective Cohort Study
Bibliographic record
Abstract
Background: Urate nephropathy is an uncommon cause of acute kidney injury (AKI). Although most factors are associated with tumor lysis syndrome and rhabdomyolysis, occurrence following severe seizure has also been described. There are effective ways to prevent and treat urate-associated AKI, when adequately identified. However, uric acid measurement following convulsion episodes is rarely performed and therefore, the incidence of hyperuricemia in this context is unknown. Our objective was to quantify these metabolic disturbances following severe generalised tonic-clonic seizures (GTCS). Methods: We prospectively recruited patients admitted in our hospital for severe GTCS (≥5 min or a series of seizures with an incomplete return to baseline) and described the kinetics of serum uric acid, creatinine, creatine kinase and lactate during a 72h follow-up. Urine urate-to-creatinine ratio was used to monitor urate tubular toxicity. Results: From August 2018 to September 2019, 13 patients with a median GTCS duration of 5.0 minutes (IQR 2.0-12.5) were included. The median serum uric acid was initially 346.0 μmol/L (IQR 155.0-377.5) and decreased to 178.0 μmol/L (IQR 140.0-297.5), while serum creatinine passed from 73.0 μmol/L (IQR 151.0-80.0) to 57.0 μmol/L (IQR 44.0-70.0) over follow-up (Figure). AKI occurred in 4 patients (KDIGO Stage ≥1). Conclusions: Serum uric acid levels increase acutely following a severe GTCS than return to baseline within 3 days. During that period, there is an increased risk of AKI that might be associated with urate nephropathy. To quickly identify and manage patients at risk of acute hyperuricemia and related complications, measurement of uric acid following a GTCS might be beneficial. Funding: Clinical Revenue SupportFigure. Post-seizure laboratory values for each participant. Star dots are the maximal value of creatinine where AKI was diagnosed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".