Metabolomics as a tool to predict the risk of decompensation or liver-related death in patients with compensated cirrhosis
Bibliographic record
Abstract
BACKGROUND AND AIMS: Patients with compensated cirrhosis with clinically significant portal hypertension (CSPH: HVPG > 10 mm Hg) have a high risk of decompensation. HVPG is, however, an invasive procedure not available in all centers. The present study aims to assess whether metabolomics can improve the capacity of clinical models in predicting clinical outcomes in these compensated patients. APPROACH AND RESULTS: This is a nested study from the PREDESCI cohort (an RCT of nonselective beta-blockers vs. placebo in 201 patients with compensated cirrhosis and CSPH), including 167 patients for whom a blood sample was collected. A targeted metabolomic serum analysis, using ultra-high-performance liquid chromatography-mass spectrometry, was performed. Metabolites underwent univariate time-to-event cox regression analysis. Top-ranked metabolites were selected using Log-Rank p -value to generate a stepwise cox model. Comparison between models was done using DeLong test. Eighty-two patients with CSPH were randomized to nonselective beta-blockers and 85 to placebo. Thirty-three patients developed the main endpoint (decompensation/liver-related death). The model, including HVPG, Child-Pugh, and treatment received ( HVPG/Clinical model ), had a C-index of 0.748 (CI95% 0.664-0.827). The addition of 2 metabolites, ceramide (d18:1/22:0) and methionine (HVPG/Clinical/Metabolite model), significantly improved the model's performance [C-index of 0.808 (CI95% 0.735-0.882); p =0.032]. The combination of these 2 metabolites together with Child-Pugh and the type of treatment received (Clinical/Metabolite model) had a C-index of 0.785 (CI95% 0.710-0.860), not significantly different from the HVPG-based models including or not metabolites. CONCLUSIONS: In patients with compensated cirrhosis and CSPH, metabolomics improves the capacity of clinical models and achieves similar predictive capacity than models including HVPG.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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".