Predictors of patient survival following liver transplant in non-alcoholic steatohepatitis: A systematic review and meta-analysis
Bibliographic record
Abstract
Background Non-alcoholic steatohepatitis (NASH) is the second-leading indication for liver transplantation (LT) worldwide and is projected to become the leading indication. Our study aimed to determine clinical variables that predict post-LT survival in NASH. Methods A systematic review and meta-analysis was performed. On June 18, 2020 and April 28, 2022, Ovid MEDLINE ALL, Ovid Embase, Cochrane Database of Systematic Reviews, and Cochrane Central Register of Controlled Trials were searched. No date limits were applied. Inclusion criteria specified the type of study and our study's population/comparison and outcome/timepoints. Pediatric, animal, retransplantation-only, and studies classifying cryptogenic cirrhosis patients with body mass index (BMI) <30 as NASH were excluded. Studies with duplicate cohorts and missing information were excluded from the meta-analysis. Studies were appraised using the Newcastle–Ottawa Scale. This study was preregistered in PROSPERO (CRD42020196915). Findings Out of 8583 studies identified, 25 studies were included in the systematic review, while 5 studies were included in the meta-analysis. Our quantitative review suggested that the following variables were predictive of post-LT NASH patient survival: recipient age, functional status, pre-LT hepatoma, model for end-stage liver disease (MELD) score, diabetes mellitus (DM), pre-LT dialysis, hepatic encephalopathy, portal vein thrombosis, hospitalization/ICU at LT, and year of LT. Predictors of graft survival included recipient age, BMI, pre-LT dialysis, and DM. Our pooled meta-analyses included five predictors of patient survival. Increased patient mortality was associated with older recipient age (HR=2·07, 95%CI: 1·71-2·50, I 2 =0, τ 2 =0, p =0·40) and pretransplant DM (HR=1·18, 95%CI: 1·08-1·28, I 2 =0, τ 2 =0, p =0·76). Interpretation Our systematic review and meta-analysis aimed to synthesise predictive variables of mortality in LT NASH patients. Clinically, this might help to identify modifiable risk factors that can be optimized in the post-transplant setting to improve patient outcomes and optimises decision making in the resource-limited LT setting. Funding Toronto General and Western Hospital Foundation.
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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 | 0.014 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.040 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".