Improving the Prediction of Cardiac Surgery–Associated Acute Kidney Injury
Bibliographic record
Abstract
INTRODUCTION: Acute kidney injury (AKI) is a potentially fatal complication of cardiac surgery. The inability to predict cardiac surgery-associated AKI is a major barrier to prevention and early treatment. Current clinical risk models for the prediction of cardiac surgery-associated AKI are insufficient, particularly in patients with preexisting kidney dysfunction. METHODS: To identify intraoperative variables that might improve the performance of a validated clinical risk score (Cleveland Clinic Score, CCS) for the prediction of cardiac surgery-associated AKI, we conducted a prospective cohort study in 289 consecutive elective cardiac surgery patients at a tertiary care center. We compared the area under the receiver operator characteristic curve (AUC) of a base model including only the CCS with models containing additional selected intraoperative variables including mean arterial pressure, hematocrit, duration of procedure, blood transfusions, and fluid balance. AKI was defined by the Kidney Disease Improving Global Outcomes 2012 criteria. RESULTS: = 0.002). In the subcohort of patients without preexisting chronic kidney disease (n = 214), where the CCS underperformed (AUC, 0.60 [0.43-0.76]), the improvement with the addition of nadir hematocrit was more marked (AUC, 0.74 [0.62-0.86]). Other variables did not improve discrimination. DISCUSSION: Nadir intraoperative hematocrit is useful in improving discrimination of clinical risk scores for AKI, and may provide a target for intervention.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 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".