Risk Factors and Outcomes of Acute Kidney Injury After Cardiac Surgery: A Retrospective Observational Single-Center Study
Bibliographic record
Abstract
Background: Acute kidney injury (AKI) following cardiac surgery is a well-described phenomenon, usually associated with hemodynamic changes ultimately leading to ischemic injury to the kidneys. In this study, we assessed the occurrence of AKI in a cohort of patients undergoing elective cardiac surgery at a single center. Methods: Patients undergoing elective cardiac surgery (coronary artery bypass grafting (CABG) and/or valve repair) between the years 2016 and 2022 were retrospectively included in the study. Results: During the study, 167 patients underwent CABG, valve replacement, or both procedures. The majority were male (85.0%). Post-operative AKI was observed in 27.5% of patients, with 2.4% requiring continuous renal replacement therapy (CRRT)/dialysis. The majority of AKI cases were staged as Kidney Disease: Improving Global Outcomes (KDIGO) stage 1. Among patients needing CRRT/dialysis, 1.8% recovered renal function within 3 months, with 0.6% experiencing 30-day mortality. In univariate analysis, factors associated with AKI included older age (P = 0.003), severe anemia (P < 0.0001), pre-operative creatinine elevation (P < 0.0001), complex surgeries (P < 0.0001), blood product transfusion (P < 0.0001), longer cross-clamp (XC) and cardiopulmonary bypass (CPB) times (P < 0.0001), and inotropes usage (P < 0.0001). Classical risk factors like diabetes mellitus (DM) and hypertension did not show significant differences. The majority of these factors (severe anemia, age, pre-operative creatinine, post-operative inotrope usage, and cross-clamp times) were consistently significant (P < 0.05) in logistic regression analysis. Conclusion: Post-operative AKI following cardiac surgery is frequent, with significant associations seen especially with pre-operative anemia. Future investigations focusing on the specific causes of anemia linked to AKI development are essential, considering the high prevalence of hemoglobinopathy traits in our population.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".