Impact of the severity of AKI using pRIFLE criteria at initiation of renal replacement therapy on clinical outcome of critically ill children
Bibliographic record
Abstract
Abstract Objective To evaluate the association between patient outcomes and the severity of acute kidney injury (AKI) at renal replacement therapy (RRT) initiation using the pediatric RIFLE criteria (pRIFLE). Design and setting Single center, retrospective observational study in a pediatric intensive care unit (PICU). Patients and methods Data extraction was performed for the first treatment of RRT in children admitted to the PICU between 2008 and 2018. Main results Ninety-four patients required RRT.84% presented with AKI according to the pRIFLE criteria at RRT initiation (10.1% stage “R” (risk), 8.9% “I” (injury), and 81% “F” (failure)). Mortality was 45.7% with no significant difference between the different degree of AKI according to pRIFLE criteria at RRT initiation. No difference in PICU lengths of stay (LOS), duration of mechanical ventilation, and duration of RRT according to the pRIFLE criteria at RRT initiation. In multivariable logistic regression analysis, non-surgical cardiac disease, an elevated PELOD score and fluid overload at RRT initiation were associated with increased odds of mortality. Increased time spent in stage F (>24h vs early<24H) was associated with longer use of vasoactive support but there was not with mortality, PICU LOS, or duration of mechanical ventilation. Conclusion The severity of AKI according to the pRIFLE criteria before RRT initiation could not predict mortality or morbidity. The optimal timing to initiate RRT in children remains unknown and the severity of kidney dysfunction appeared to be important but insufficient by itself to predict the clinical outcome of children requiring RRT.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".