Incidence and Risks for Nonalcoholic Fatty Liver Disease and Steatohepatitis Post-liver Transplant: Systematic Review and Meta-analysis
Bibliographic record
Abstract
BACKGROUND: The true incidence and unique risk factors for recurrent and de novo nonalcoholic fatty liver (NAFLD) and nonalcoholic steatohepatitis (NASH) post-liver transplant (LT) remain poorly characterized. We aimed to identify the incidence and risk factors for recurrent and de novo NAFLD/NASH post-LT. METHODS: MEDLINE via PubMed, Embase, Scopus, and CINAHL were searched for studies from 2000 to 2018. Risk of bias was adjudicated using the Newcastle-Ottawa Scale. RESULTS: Seventeen studies representing 2378 patients were included. All were retrospective analyses of patients with post-LT liver biopsies, with the exception of 2 studies that used imaging for outcome assessment. Seven studies evaluated occurrence of recurrent NAFLD/NASH, 3 evaluated de novo occurrence, and 7 evaluated both recurrent and de novo. In studies at generally high or moderate risk of bias, mean 1-, 3-, and ≥5-year incidence rates may be 59%, 57%, and 82% for recurrent NAFLD; 67%, 40%, and 78% for de novo NAFLD; 53%, 57.4%, and 38% for recurrent NASH; and 13%, 16%, and 17% for de novo NASH. Multivariate analysis demonstrated that post-LT body mass index (summarized odds ratio = 1.27) and hyperlipidemia were the most consistent predictors of outcomes. CONCLUSIONS: There is low confidence in the incidence of recurrent and de novo NAFLD and NASH after LT due to study heterogeneity. Recurrent and de novo NAFLD may occur in over half of recipients as soon as 1 year after LT. NASH recurs in most patients after LT, whereas de novo NASH occurs rarely. NAFLD/NASH after LT is associated with metabolic risk factors.
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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 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.029 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".