Albuminuria Predicts Adverse Renal Outcomes and Death After Liver Transplantation : a Retrospective Cohort Study
Bibliographic record
Abstract
Background: CKD is common after liver transplantation (LT); up to 20% have stage 4 or higher CKD (eGFR <30 mL/min/1.73m2) at 5 years. Albuminuria is a marker of CKD associated with an increased risk of end-stage renal disease (ESRD) anddeath in the general population. We examined this association in LT as it has not been studied previously. Methods: Retrospective cohort study of adults who underwent LT at Vancouver General Hospital, Vancouver, BC, from 1989-2011. Starting in 2011, patients had urine albumin excretion checked annually. Patients were included if they had u22651 urine albumin to creatinine ratio (ACR) measurement u22653 months after LT, and were followed until December 31, 2017. Cox multivariable regression was used to determine predictors of the primary combined outcome (death, doubling of serum creatinine, or ESRD) and the secondary outcome (sustained drop in eGFR u226530%). Results: 294 patients were included, with a median follow-up time of 71 months. Hepatitis C was the most common indication for LT (38.8%). At baseline (at time of first ACR): mean age was 57.2 years, 58.8% were male, median eGFR was 67 mL/min/1.73m2, and 86.4% were on a calcineurin inhibitor. Albuminuria was present in 105 (35.7%) patients (ACR 3-30 mg/mmol, N=77; ACR >30 mg/mmol, N=28). The primary outcome occurred in 20.4% (N=60), and was significantly associated with ACR >30 mg/mmol (HR 4.27, 95% CI 2.17-8.39, P<0.0001) in the multivariable model. A decline in eGFR u226530% occurred in 21.8% (N=64), and both ACR 3-30 mg/mmol and ACR >30 mg/mmol at baseline were associated with significantly increased risk (HR 2.16, 95% CI 1.18-3.96; P=0.01 and HR 7.77, 95% CI 4.14-14.57; P<0.0001, respectively). Conclusion: Severe albuminuria (ACR >30 mg/mmol) is associated with increased risk of loss of renal function and death after LT. Prospective studies are needed to confirm this association, and to determine if specific interventions can improve long term outcomes.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.019 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.022 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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; both teacher heads agree on what is shown here.
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".