Risk of liver-related events in metabolic dysfunction–associated steatohepatitis (MASH) patients with fibrosis: A comparative analysis of various risk stratification criteria
Bibliographic record
Abstract
BACKGROUND AND AIMS: International regulatory agencies recommend testing drug therapy for patients with noncirrhotic high-risk metabolic dysfunction-associated steatohepatitis (MASH) because they are at risk of liver-related events (LRE). We aimed to compare the risk of LRE in patients with MASLD stratified for F2-F4 fibrosis and MASH. APPROACH AND RESULTS: Overall, 1938 consecutive patients with biopsy-proven MASLD were enrolled. High-risk MASH was defined as MASH with F2-F4 fibrosis. LSM was measured by transient elastography. LRE were recorded during follow-up. Cox multivariate models were used to assess the association between high-risk MASH or F2-F4 fibrosis without MASH, of LSM (≥8 or ≥10 Kpa), and of AGILE 3+ with LRE. The diagnostic performance for the prediction of LRE was assessed using the area under the receiver operating characteristic curves. The observed 5-year actuarial rate of LRE was 0.4%, 0.2%, 5.1%, and 6.6% in patients with F0-F1 fibrosis without MASH, F0-F1 fibrosis with MASH, F2-F4 fibrosis without MASH, and high-risk MASH, respectively. At multivariate Cox regression analysis using F0-F1 fibrosis without MASH as a reference, both F2-F4 fibrosis without MASH [adjusted HR (aHR) 9.96] and high-risk MASH (aHR 10.14) were associated with LRE. In the 1074 patients with available LSM, LSM ≥ 10 kPa (aHR 6.31) or AGILE 3+ > 0.67 (aHR 27.45) independently predicted the development of LRE and had similarly acceptable 5-year area under the receiver operating characteristic to high-risk MASH and F2-F4 fibrosis (0.772, 0.818, 0.739, and 0.780, respectively). CONCLUSIONS: The risk of LRE is similar in patients with high-risk MASH and with F2-F4 fibrosis without MASH. The use of LSM ≥ 10 kPa or AGILE 3+ > 0.67 could be an accurate option to identify patients with MASLD worthy to be included in clinical trials.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".