Predicting Acute Renal Failure after Cardiac Surgery: Validation and Re‐definition of a Risk‐Stratification Algorithm
Bibliographic record
Abstract
BACKGROUND: Acute renal failure (ARF) after cardiac surgery is associated with significant morbidity and mortality, irrespective of the need for dialysis. Previous studies have attempted to identify predictors of ARF and develop risk stratification algorithms. This study aims to validate the algorithm in an independent cohort of patients that includes a significant proportion of female and black patients and compares two different definitions of renal outcome. METHODS: A large single center cardiac surgery database was examined (n, 24,660; 1993-2000) which included 29.9% females and 3.7% black patients. Post-operative ARF was defined as: a) ARF requiring dialysis, b) > 50% reduction in creatinine clearance relative to baseline or requiring dialysis. Clinical variables related to baseline renal function and cardiovascular disease were used in recursive partitioning analysis for both outcome definitions. Chi-square goodness of fit analysis was performed to validate the algorithm. RESULTS: The frequency of post-operative ARF requiring dialysis ranged between 0.5 and 15.5% based on the risk categories with the area under the receiver operating characteristic (ROC) curve of 0.78. Using the more inclusive definition of ARF, the frequency was significantly higher ranging from 2.6 to 25%(P < 0.001) with an area under ROC curve of 0.65. CONCLUSIONS: The renal risk stratification algorithm is valid in predicting post-operative ARF in an independent cohort of patients, well represented by differences in gender and race. Since the need for dialysis remains subjective, a more objective and inclusive definition of ARF may help in identifying a larger number of patients 'at-risk'.
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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 | 0.001 |
| 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".