Cirrhosis regression is associated with improved clinical outcomes in patients with nonalcoholic steatohepatitis
Bibliographic record
Abstract
BACKGROUND AND AIMS: Surrogate endpoints that predict complications are necessary for assessment and approval of NASH therapies. We assessed associations between histologic and noninvasive tests (NITs) of fibrosis with liver-related complications in patients with NASH cirrhosis. APPROACH AND RESULTS: Patients with compensated cirrhosis due to NASH were enrolled in two placebo-controlled trials of simtuzumab and selonsertib. Liver fibrosis at baseline and week 48 (W48) was staged by NASH Clinical Research Network (CRN) and Ishak classifications and a machine learning (ML) approach, hepatic collagen and alpha-smooth muscle actin (α-SMA) expression were quantified by morphometry, liver stiffness (LS) was measured by transient elastography, and serum NITs (enhanced liver fibrosis [ELF], NAFLD fibrosis score [NFS], and Fibrosis-4 index [FIB-4]) were calculated. Cox regression determined associations between these parameters at baseline and their changes over time with adjudicated liver-related clinical events. Among 1,135 patients, 709 (62%) had Ishak stage 6 fibrosis, and median ELF and LS were 10.66 and 21.1 kPa, respectively. During a median follow-up of 16.6 months, 71 (6.3%) had a liver-related event; associated baseline factors included Ishak stage 6 fibrosis, and higher hepatic collagen, α-SMA expression, ML-based fibrosis parameters, LS, ELF, NFS, and FIB-4. Cirrhosis regression observed in 16% (176/1,135) between BL and W48 was associated with a lower risk of events versus nonregression (1.1% [2/176] vs. 7.2% [69/957]; HR, 0.16; 95% CI, 0.04, 0.65 [p = 0.0104]). Conversely, after adjustment for baseline values, increases in hepatic collagen, α-SMA, ML-based fibrosis parameters, NFS, and LS were associated with an increased risk of events. CONCLUSIONS: In patients with compensated cirrhosis due to NASH, regression of fibrosis is associated with a reduction in liver-related complications. These data support the utility of histologic fibrosis regression and NITs as clinical trial endpoints for NASH cirrhosis.
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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 | 0.000 | 0.000 |
| 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".