Predicting Acute Kidney Injury Following Non-Emergent Cardiac Surgery: A Preoperative Scorecard
Bibliographic record
Abstract
Objective: To determine the predictors of postoperative AKI following non-emergent cardiac surgery among patients with variable preoperative eGFR levels. Methods: Retrospective study of patients who underwent elective or in-hospital cardiac surgical procedures performed between January 2006 and November 2015. The procedures included isolated CABG, isolated AVR or combined CABG and AVR. The primary outcome AKI (any stage) following non-emergent cardiac surgery utilizing the 2012 KDIGO criteria. Patients were categorized based the following renal outcomes: mild AKI, severe AKI (KDIGO stage 2 or 3) and post-operative dialysis.. Results: A total of 6713 patients were included in our study. The mean age was 66.8 years (SD ± 10.3), with 76.2% being males. A total of 4487 patients had normal or mildly decreased eGFR (G1 or G2) preoperatively (66.8%), while 1960 patients were in the G3 category (29.1%). Only 266 patients (3.9%) had G4 or worse renal function. A total of 1489 (28.5%) patients experienced post-operative AKI. The need for postoperative dialysis occurred in 4.2% of the AKI subgroup. In-hospital mortality was higher among the AKI subgroup (7.3% vs 0.5%, p<0.0001). In an adjusted model, a lower pre-operative eGFR category was the strongest predictor of AKI. A practical scorecard for the preoperative estimation of severe AKI for non-emergent cardiac procedures incorporating these parameters was developed. Conclusions: Preoperative eGFR is the strongest predictor of post-operative AKI in individuals undergoing non-emergent cardiac surgery. A practical scorecard incorporating preoperative predictors of AKI may allow informed decision making and to predict AKI following non-emergent cardiac surgery
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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 | 0.004 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".