A multi-centre evaluation of the RIFLE criteria for early acute kidney injury in critically ill patients
Bibliographic record
Abstract
BACKGROUND: The Acute Dialysis Quality Initiative Working Group recently developed the RIFLE criteria, a consensus definition for acute kidney injury (AKI). We sought to evaluate the RIFLE criteria on the day of ICU admission in a large heterogenous population of critically ill patients. METHODS: Retrospective interrogation of prospectively collected data from the Australian New Zealand Intensive Care Society Adult Patient Database. We evaluated 120 123 patients admitted for >/=24 h from 1 January 2000 to 31 December 2005 from 57 ICUs across Australia. RESULTS: The median (IQR) age was 64.3 (50.8-75.4) years, 59.5% were male, 28.6% had co-morbid disease, 50.3% were medical admissions and the initial mean (+/-SD) APACHEII score was 16.9 (+/-7.7). According to the RIFLE criteria, on the day of admission, AKI occurred in 36.1%, with a maximum RIFLE category of Risk in 16.3%, Injury in 13.6%, and Failure 6.3%. AKI, defined by any RIFLE category, was associated with an increase in hospital mortality (OR 3.29, 95% CI 3.19-3.41, P < 0.0001). The crude hospital mortality stratified by RIFLE category was 17.9% for Risk, 27.7% for Injury and 33.2% for Failure. By multivariable analysis, each RIFLE category was independently associated with hospital mortality (OR: Risk 1.58, Injury 2.54 and Failure 3.22). CONCLUSION: In a large heterogenous cohort of critically ill patients, the RIFLE criteria classified >36% with AKI on the day of admission. For successive increases in severity of RIFLE category, there were increases in hospital mortality. The RIFLE criteria represent a simple tool for the detection and classification of AKI and for correlation with clinical outcomes.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".