Risk Stratification for Chronic Kidney Disease After Liver Transplant for Metabolic Dysfunction-associated Steatohepatitis (MASH) Cirrhosis: Results From the NailMASH Consortium
Bibliographic record
Abstract
BACKGROUND: Chronic kidney disease (CKD) is a well-recognized complication in patients undergoing liver transplantation (LT), particularly those with metabolic dysfunction-associated steatohepatitis (MASH), a leading cause of cirrhosis in the modern era. This study sought to refine risk stratification for CKD events post-LT in cirrhosis patients with MASH by leveraging baseline renal function at transplant. METHODS: A total of 717 MASH cirrhosis patients who had LT (1997-2017) at 7 US centers (NailMASH Consortium) were analyzed. Patients were categorized by estimated glomerular filtration rate (eGFR) at transplant: low (LGFR, eGFR ≤30 mL/min/1.73 m²), medium (MGFR, eGFR >30-≤60 mL/min/1.73 m²), and high (HGFR, eGFR >60 mL/min/1.73 m²). Time-related eGFR intercepts, slopes, and assessments of advanced-stage CKD (aCKD) events, defined as 2 eGFR levels <30 mL/min/1.73 m² separated by ≥90 d, were examined. RESULTS: Post-LT, LGFR group showed increased eGFR, whereas the HGFR group experienced a decline. The 3-mo mark was identified as a "reset point," signifying a new reference level, beyond which a different rate of decline was observed. After 3 mo, mean eGFRs of the LGFR group approached MGFRs, whereas the mean eGFR of the HGFR group continued to decrease but remained higher than other groups during a 60-mo follow-up. LGFR patients had significantly higher aCKD probability than MGFR and HGFR groups. Subanalysis at 3 mo post-LT revealed more aCKD events in the LGFR group compared with MGFR and HGFR groups ( P < 0.0001). CONCLUSIONS: The study underscores renal impact of LT in MASH cirrhosis, indicating unique eGFR trajectories post-LT tied to baseline eGFR, with a reset point at 3 mo. Monitoring post-LT renal function, especially in those at aCKD risk, is crucial. Renal-sparing immunosuppression may help, regardless of baseline eGFR. Further studies are needed for interventions addressing renal dysfunction of patients with MASH post-LT.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".