The Association of Albumin/Creatinine Ratio with Postoperative AKI in Children Undergoing Cardiac Surgery
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: This study determined if preoperative and postoperative urine albumin/creatinine ratios (ACRs) predict postoperative AKI in children undergoing cardiac surgery (CS). DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: This was a three-center, prospective study (2007-2009) of 294 children undergoing CS (n=145 aged <2 years). Urine ACR was measured preoperatively and 0-6 hours after intensive care unit arrival. AKI outcomes were based on the Acute Kidney Injury Network serum creatinine (SCr) criteria (stage 1 AKI, ≥50% or 0.3 mg/dl SCr rise from baseline; and stage 2 or worse AKI, ≥SCr doubling or dialysis). AKI was predicted using preoperative and postoperative ACRs and postoperative ACR performance was compared with other AKI biomarkers. RESULTS: Preoperative ACR did not predict AKI in younger or older children. In children aged <2 years, first postoperative ACR ≥908 mg/g (103 mg/mmol) predicted stage 2 AKI development (adjusted relative risk, 3.4; 95% confidence interval, 1.2-9.4). In children aged ≥2 years, postoperative ACR ≥169 mg/g (19.1 mg/mmol) predicted stage 1 AKI (adjusted relative risk, 2.1; 95% confidence interval, 1.1-4.1). In children aged ≥2 years, first postoperative ACR improved AKI prediction from other biomarker and clinical prediction models, estimated by net reclassification improvement (P≤0.03), but only when serum cystatin C was also included in the model. CONCLUSIONS: Postoperative ACR is a readily available early diagnostic test for AKI after pediatric CS that performs similarly to other AKI biomarkers; however, its use is enhanced in children aged ≥2 years and in combination with serum cystatin C.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 | 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".