Hyperhydration to Improve Kidney Outcomes in Children with Shiga Toxin-Producing E. coli Infection: a multinational embedded cluster crossover randomized trial (the HIKO STEC trial)
Bibliographic record
Abstract
BACKGROUND: Shiga toxin-producing E. coli (STEC) infections affect children and adults worldwide, and treatment remain solely supportive. Up to 15-20% of children infected by high-risk STEC (i.e., E. coli that produce Shiga toxin 2) develop hemolytic anemia, thrombocytopenia, and kidney failure (i.e., hemolytic uremic syndrome (HUS)), over half of whom require acute dialysis and 3% die. Although no therapy is widely accepted as being able to prevent the development of HUS and its complications, several observational studies suggest that intravascular volume expansion (hyperhydration) may prevent end organ damage. A randomized trial is needed to confirm or refute this hypothesis. METHODS: We will conduct a pragmatic, embedded, cluster-randomized, crossover trial in 26 pediatric institutions to determine if hyperhydration, compared to conservative fluid management, improves outcomes in 1040 children with high-risk STEC infections. The primary outcome is major adverse kidney events within 30 days (MAKE30), a composite measure that includes death, initiation of new renal replacement therapy, or persistent kidney dysfunction. Secondary outcomes include life-threatening, extrarenal complications, and development of HUS. Pathway eligible children will be treated per institutional allocation to each pathway. In the hyperhydration pathway, all eligible children are hospitalized and administered 200% maintenance balanced crystalloid fluids up to targets of 10% weight gain and 20% reduction in hematocrit. Sites in the conservative fluid management pathway manage children as in- or outpatients, based on clinician preference, with the pathway focused on close laboratory monitoring, and maintenance of euvolemia. Based on historical data, we estimate that 10% of children in our conservative fluid management pathway will experience the primary outcome. With 26 clusters enrolling a mean of 40 patients each with an intraclass correlation coefficient of 0.11, we will have 90% power to detect a 5% absolute risk reduction. DISCUSSION: HUS is a devastating illness with no treatment options. This pragmatic study will determine if hyperhydration can reduce morbidity associated with HUS in children with high-risk STEC infection. TRIAL REGISTRATION: ClinicalTrials.gov NCT05219110 . Registered on February 1, 2022.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".