Risk factors for developing acute kidney injury after heart transplant: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND Acute kidney injury (AKI) is a common and serious complication following heart transplantation, significantly impacting patient outcomes and survival rates. AKI after transplantation can lead to prolonged hospital stays, increased morbidity, and even mortality. AIM To identify and quantify significant risk factors associated with AKI following heart transplantation through a systematic review and meta-analysis. This study aims to distinguish predictive variables that may inform perioperative risk stratification and clinical decision-making. METHODS Electronic searches on MEDLINE, Google Scholar, ScienceDirect, ClinicalTrials.gov, and Cochrane databases were conducted from inception up till September 1. Included studies were randomized controlled trials, clinical trials, retrospective cohort, and observational studies. Exclusion criteria encompassed studies with pediatric populations, non-English publications, case reports, and studies lacking sufficient data on AKI outcomes. Statistical analysis was performed using RevMan 5.4, reporting dichotomous outcomes as odds ratios (OR) and continuous outcomes as mean differences (MD) with 95% confidence intervals (CI). Quality assessment of the included studies was performed using the New Castle Ottawa Scale. RESULTS Out of 1345 articles, 13 studies with 3330 patients were included. Significant risk factors included age [overall MD = 2.27 years (95%CI: 0.13 to 4.41)], body mass index (BMI) [MD = 1.42 (95%CI: 0.60 to 2.24)], diabetes [overall OR = 1.47 (95%CI: 1.16 to 1.85)], chronic kidney disease (CKD) [OR = 2.67 (95%CI: 1.73 to 4.14)], chronic obstructive pulmonary disorder (COPD) [OR = 0.49 (95%CI: 0.27 to 0.89)], previous thoracic surgery [(OR) = 1.27, 95%CI: (1.05 to 1.54)], cardio-pulmonary bypass time [(MD) = 17.10, 95%CI: (6.12 to 28.08)], mechanical ventilation duration [(MD) = 30.87 hours, 95%CI: (10.69 to 51.05)] and extracorporeal membrane oxygenation [(OR) = 2.31, 95%CI: (1.25 to 4.26)]. Factors not associated with AKI after heart transplantation included Recipients’ male sex (P = 0.55), donor sex (P = 0.11), hypertension (P = 0.13), smoking (P = 0.20), coronary artery disease (P = 0.90), pulmonary artery disease (P = 0.81), dilated cardiomyopathy (P = 0.79), ventilation duration (P = 0.24), ischemic time (P = 0.06), use of intra-aortic balloon pump (P = 0.14), LVAD transplantation (P = 0.83), and Inotropes use (P = 0.78). CONCLUSION Age, BMI, diabetes, CKD, COPD, previous thoracic surgery, prolonged CPB time, extended mechanical ventilation, and ECMO use are significant predictors of AKI following heart transplantation, necessitating vigilant monitoring and individualized risk assessment. Conversely, factors such as LVAD implantation and inotrope use showed no significant association, highlighting the need for further investigation into their roles. Future prospective studies are essential to validate these findings, elucidate underlying mechanisms, and develop targeted interventions to mitigate AKI risk and improve patient outcomes.
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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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.041 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".