Evaluating Surrogate Measures of Renal Dysfunction After Cardiac Surgery
Bibliographic record
Abstract
UNLABELLED: Renal insufficiency after cardiac surgery is associated with increased mortality, morbidity, and length of intensive care unit stay. A convenient surrogate measure would facilitate the evaluation of renal-protective therapies. We evaluated two measures: the 72-h change in serum creatinine (Cr) (DeltaCr(72h)) and the percentage 72-h change in calculated (Cockcroft-Gault equation) Cr clearance (%DeltaCrCl(72h)). We randomly selected 2000 individuals who underwent aortocoronary bypass, valve surgery, or both at the Toronto General Hospital between May 1999 and August 2000. The variables were analyzed with frequency histograms and normal probability plots. Their association with dialysis, mortality, and prolonged intensive care unit stay was determined by using receiver operating characteristic (ROC) curves. DeltaCr(72h) was skewed to the right, whereas %DeltaCrCl(72h) was normally distributed. ROC curve areas showed DeltaCr(72h) to be a good predictor of dialysis (0.98), death (0.83), and prolonged hospitalization (0.74). %DeltaCrCl(72h) had similar ROC curve areas for predicting dialysis (0.97), death (0.82), and prolonged hospitalization (0.74). ROC curve areas did not differ significantly with respect to mortality (P = 0.89), dialysis (P = 0.49), or prolonged hospitalization (P = 0.85). Both variables were correlated with patient-relevant outcomes. Mathematical transformation of DeltaCr(72h) to %DeltaCrCl(72h) results in a normal distribution that is amenable to parametric statistical tests. DeltaCr(72h) and %DeltaCrCl(72h) may be used as surrogate outcomes in future trials. IMPLICATIONS: A convenient surrogate measure of renal function is needed for evaluating renal-protective therapies in cardiac surgery. We evaluated the performance of serum creatinine concentration and calculated creatinine clearance for predicting dialysis, mortality, and prolonged hospitalization. Both measures were correlated with clinical outcomes. Creatinine clearance had the advantage of a distribution suitable for parametric statistical tests.
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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 | 0.004 | 0.016 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".