A Multi-Center Evaluation of Early Acute Kidney Injury in Critically Ill Trauma Patients
Bibliographic record
Abstract
RATIONALE: Few studies have evaluated the epidemiology of acute kidney injury (AKI) in trauma. OBJECTIVE: To evaluate the incidence, risk factors, and outcomes associated with early AKI (evident within 24 hours of admission) in critically ill trauma patients. METHODS: A retrospective interrogation of prospectively collected data from the Australian New Zealand Intensive Care Society Adult Patient Database. A total of 9,449 trauma patients were admitted for >or=24 hours to 57 intensive care units across Australia from January 1(st), 2000, to December 31(st), 2005. MAIN FINDINGS: The crude incidence of AKI was 18.1% (n = 1,711). Older age, female sex (OR 1.60, 95% CI, 1.43-1.78, p < 0.0001), and the presence of co-morbid illness (OR 2.70, 95% CI 2.3-3.2, p < 0.0001) were associated with higher odds of AKI. Those with trauma not associated with brain injury (OR 2.40, 95% CI, 2.1-2.7, p < 0.0001) and a higher illness severity (OR 1.12, 95% CI, 1.11-1.12, p < 0.001) also had higher likelihood of AKI. Overall, AKI was associated with a higher crude mortality (16.7% vs. 7.8%, OR 2.36, 95% CI, 2.0-2.7, p < 0.001). Each RIFLE category of AKI was independently associated with hospital mortality in multi-variable analysis (risk: OR 1.69; injury OR 1.88; failure 2.29). CONCLUSIONS: Trauma admissions to ICU are frequently complicated by early AKI. Those at high risk for AKI appear to be older, female, with co-morbid illnesses, and present with greater illness severity. Early AKI in trauma is also independently associated with higher mortality. These data indicate a higher burden of AKI than previously described.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".