Acute renal failure following lung transplantation: risk factors, mortality, and long-term consequences
Bibliographic record
Abstract
OBJECTIVE: Acute renal failure (ARF) frequently complicates lung transplantation. This study determined the prevalence, predictive factors, and consequences of ARF on long-term renal function and survival. METHODS: One hundred and seventy-four lung transplantation recipients were divided into two groups based on the presence or absence of ARF defined as a 50% decrease in creatinine clearance from baseline (group I: 67 patients with ARF; group II: 107 patients without ARF). Multivariate analysis compared pre-operative, operative, and post-operative risk factors to assess predictive factors. Renal function over time was assessed by two-way repeated measures analysis of variance (ANOVA). RESULTS: ARF developed in 67 (39%) of patients. Multivariate analysis identified aprotinin (OR 2.20 (1.11; 4.36), p=0.02) and double lung transplantation (OR 2.61 (1.32; 5.15), p=0.006) as risk factors for post-operative renal failure. At 5 years following transplant, creatinine clearance was similar between the two groups (group I CrCl: 73 ml s(-1); group II CrCl: 53 ml s(-1); p=0.54). Survival at 5 years was the same in the two groups. Multivariate analysis associated age at the time of transplantation (HR 1.030 (1.004; 1.057), p=0.02) and intensive care unit (ICU) length of stay (HR 1.029 (1.008; 1.051), p=0.007) with decreased survival. CONCLUSIONS: The use of aprotinin and double lung transplantation are associated with ARF following lung transplantation. Age at the time of transplantation and a longer intensive care stay predict decreased survival. ARF after lung transplantation is not predictive of late renal dysfunction or decreased long-term survival.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.000 | 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".