Validating a Scoring Tool to Predict Acute Kidney Injury (AKI) following Cardiac Surgery
Bibliographic record
Abstract
BACKGROUND: Acute kidney injury (AKI) after cardiac surgery is associated with an increased risk of mortality. Preoperative risk scores can identify patients at risk for AKI and facilitate preventive strategies. Currently, validated risk scores are used to predict AKI requiring dialysis (AKI-D); less is known about whether these tools predict less severe forms of AKI. OBJECTIVE: To evaluate the Cleveland Clinic scoring tool in predicting both AKI-D and less severe stages of AKI in patients after cardiac surgery in a Canadian tertiary care center. DESIGN: Retrospective case-control study. SETTING: Regina Qu'Appelle Health Region (RQHR) from 2007 to 2011. PATIENTS: Patients who underwent cardiac surgery and developed postoperative kidney injury (n = 2316). MEASUREMENTS: Data on risk factors for AKI and outcomes of cardiac surgery were collected from a retrospective chart review. METHODS: The primary outcome was AKI, defined as Stage 1 (increase in serum creatinine 1.5-1.9 X baseline within 5 days), Stage 2 (increase 2.0-2.9 X baseline), or Stage 3 (increase 3.0 X baseline or more OR initiation of dialysis during hospital stay). We assessed the performance of a modified version of the Cleveland Clinic tool using receiver operating curve analyses. RESULTS: The incidence of AKI was 6.1% (Stage 1), 2.6% (Stage 2), and 5.8% (Stage 3). The area under the curve (AUC) for the Cleveland score was 0.61 (95% CI: 0.56 to 0.65; p < 0.001) for Stage 1, 0.61 (95% CI: 0.54 to 0.68; p < 0.01) for Stage 2, and 0.78 (95% CI: 0.74 to 0.82; p < 0.001) for Stage 3. Greater level of risk on the Cleveland tool was associated with a higher risk of Stage 3 AKI. LIMITATIONS: Lack of prospective validation. CONCLUSIONS: The modified Cleveland Clinic tool was valid in identifying patients with severe stages of AKI but did not have strong discrimination for early AKI stages.
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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 | 0.002 | 0.103 |
| 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 | 0.001 |
| 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".