Prognostic implications of adding urine output to serum creatinine measurements for staging of acute kidney injury after major surgery: a cohort study
Bibliographic record
Abstract
BACKGROUND: Current guidelines recommend staging acute kidney injury (AKI) according to the serum creatinine (SCr) or urine output (UO) criteria that achieve the highest stage. There is little information about the implications of adding UO to SCr measurements for staging AKI outside intensive care units and after cardiac surgery. METHODS: We performed a cohort study of all adults without end-stage renal disease who underwent major noncardiac surgery between January 2005 and March 2011 in Calgary, AB, Canada. Participants required at least two SCr and UO measurements to be included. We examined the implications of adding UO to SCr to stage AKI based on Kidney Disease: Improving Global Outcomes criteria. Logistic and linear regression models were used to examine the associations between AKI stage and 30-day mortality or hospital length of stay (LOS), respectively. RESULTS: A total of 4229 (17%) surgical patients had sufficient SCr and UO measurements for inclusion in the cohort. The apparent incidence of postoperative AKI substantially increased with the addition of UO to SCr criteria (8.1% with SCr alone versus 64.0% with SCr and UO). Mortality for a given stage of AKI was lower when UO was added to SCr criteria (0.3, 3.2, 1.9 and 3.0% for no AKI and Stages 1, 2 and 3, respectively) versus with SCr alone (1.2, 4.2, 15.4 and 12.8%). However, among participants without AKI based on the SCr criterion, the odds of mortality and mean LOS both significantly increased with lower UO. Models that reclassified AKI stage based on UO in addition SCr criteria had the best discrimination for mortality and LOS. CONCLUSIONS: Adding UO to SCr criteria substantially increases the apparent incidence of AKI on hospital wards and significantly changes the prognostic implications of AKI identification and staging. These measures should not be considered equivalent criteria in AKI staging.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".