Risk factors of acute renal failure in critically ill children: A prospective descriptive epidemiological study*
Bibliographic record
Abstract
OBJECTIVE: Acute renal failure is a serious condition in critically ill patients, but little literature is available on acute renal failure in critically ill children. The aim of the study was to determine incidence rate, identify risk factors, and describe the clinical outcome of acute renal failure in the pediatric intensive care unit (PICU). DESIGN: Prospective, descriptive study. SETTING: A tertiary PICU. PATIENTS: Patients were 1,047 consecutively admitted children over a 1-yr period. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Acute renal failure was defined as doubling of baseline serum creatinine. A comparison between patients with acute renal failure and without acute renal failure was carried out, and the risk factors playing a significant role in the manifestation of acute renal failure were analyzed. There were 985 cases included in the study, with the incidence rate of acute renal failure in PICU being 4.5%. The most common PICU admission diagnoses in acute renal failure cases were hemolytic uremic syndrome (18.2%), oncologic pathologies (18.2%), and cardiac surgery (11.4%). Significant risk factors for acute renal failure following multivariate analysis were thrombocytopenia (odds ratio, 6.3; 95% confidence interval, 2.5, 16.2), age >12 yrs (odds ratio, 4.9; 95% confidence interval, 1.9, 13), hypoxemia (odds ratio, 3.2; 95% confidence interval, 1.3, 8.0), hypotension (odds ratio, 3.0; 95% confidence interval, 1.2, 7.5), and coagulopathy (odds ratio, 2.7; 95% confidence interval, 1.3, 5.6). The mortality rate was estimated to be higher in patients with acute renal failure compared with patients without acute renal failure (29.6% vs. 2.3%, p < .001). CONCLUSIONS: Although not frequent in the PICU, acute renal failure is associated with a significant increase in mortality. The risk factors of acute renal failure are multiple and are often present before PICU admission. A multiple-center study is planned with the intention to confirm these results.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".