Association of Postoperative Proteinuria with AKI after Cardiac Surgery among Patients at High Risk
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Preoperative proteinuria is associated with a higher incidence of postoperative AKI. Whether the same is true for postoperative proteinuria is uncertain. This study tested the hypothesis that increased proteinuria after cardiac surgery is associated with an increased risk for AKI. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: This prospective cohort study included 1198 adults undergoing cardiac surgery at six hospitals between July 2007 and December 2009. Albuminuria, urine albumin-to-creatinine ratio (ACR), and dipstick proteinuria were measured 0-6 hours after surgery. The primary outcome was AKI, defined as a doubling in serum creatinine or receipt of acute dialysis during the hospital stay. Analyses were adjusted for patient characteristics, including preoperative albuminuria. RESULTS: Compared with the lowest quintile, the highest quintile of albuminuria and highest grouping of dipstick proteinuria were associated with greatest risk for AKI (adjusted relative risks [RRs], 2.97 [95% confidence interval (CI), 1.20-6.91] and 2.46 [95% CI, 1.16-4.97], respectively). Higher ACR was not associated with AKI risk (highest quintile RR, 1.66 [95% CI, 0.68-3.90]). Of the three proteinuria measures, early postoperative albuminuria improved the prediction of AKI to the greatest degree (clinical model area under the curve, 0.75; 0.81 with albuminuria). Similar improvements with albuminuria were seen for net reclassification index (0.55; P<0.001) and integrated discrimination index (0.036; P<0.001). CONCLUSIONS: Higher levels of proteinuria after cardiac surgery identify patients at increased risk for AKI during their hospital stay.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".