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 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 | 0.003 |
| 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 | 0.001 |
| 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".