Population-Based Epidemiology and Outcomes of Acute Kidney Injury in Critically Ill Children*
Bibliographic record
Abstract
OBJECTIVES: We describe the epidemiology, characteristics, risk factors, and incremental risks associated with acute kidney injury in critically ill children at a population-level. DESIGN: Population-based retrospective cohort study. SETTING: PICUs in Alberta, Canada. PATIENTS: All children admitted to PICUs in Alberta, Canada between January 1, 2015, and December 31, 2015. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: A total of 1,017 patients were included. Acute kidney injury developed in 308 patients (30.3%; 95% CI, 28.1-33.8%) and severe acute kidney injury (Kidney Disease: Improving Global Outcomes stage 2 and 3) developed in 124 patients (12.2%; 95% CI, 10.3-14.4%). Incidence rates for critical illness-associated acute kidney injury and severe acute kidney injury were 34 (95% CI, 30.3-38.0) and 14 (95% CI, 11.38-16.38) per 100,000 children-year, respectively. Severe acute kidney injury incidence rates were greater in males (incidence rate ratio, 1.55; 95% CI, 1.08-2.33) and infants younger than 1 year old (incidence rate ratio, 14.77; 95% CI, 10.36-21.07). Thirty-two patients (3.1%) did not survive to PICU discharge. The acute kidney injury-associated PICU mortality rate was 2.3 (95% CI, 1.4-3.5) per 100,000 children-year. In multivariate analysis, severe acute kidney injury was associated with greater PICU mortality (odds ratio, 11.93; 95% CI, 4.68-30.42) and 1-year mortality (odds ratio, 5.50; 95% CI, 2.76-10.96). Severe acute kidney injury was further associated with greater duration of mechanical ventilation, duration of vasoactive support, and lengths of PICU and hospital stay. CONCLUSIONS: The population-level burden of acute kidney injury and its attributable risks are considerable among critically ill children. These findings emphasize the need for enhanced surveillance for acute kidney injury, identification of modifiable risks, and evaluation of interventional strategies.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".