Accuracy of blood-based biomarkers for staging liver fibrosis in chronic liver disease: A systematic review supporting the AASLD Practice Guideline
Bibliographic record
Abstract
BACKGROUND AND AIMS: Blood-based biomarkers have been proposed as an alternative to liver biopsy for noninvasive liver disease assessment in chronic liver disease. Our aims for this systematic review were to evaluate the diagnostic utility of selected blood-based tests either alone, or in combination, for identifying significant fibrosis (F2-4), advanced fibrosis (F3-4), and cirrhosis (F4), as compared to biopsy in chronic liver disease. APPROACH AND RESULTS: We included a comprehensive search of databases including Ovid MEDLINE(R), EMBASE, Cochrane Database, and Scopus through to April 2022. Two independent reviewers selected 286 studies with 103,162 patients. The most frequently identified studies included the simple aspartate aminotransferase-to-platelet ratio index and fibrosis (FIB)-4 markers (with low-to-moderate risk of bias) in HBV and HCV, HIV-HCV/HBV coinfection, and NAFLD. Positive (LR+) and negative (LR-) likelihood ratios across direct and indirect biomarker tests for HCV and HBV for F2-4, F3-4, or F4 were 1.66-6.25 and 0.23-0.80, 1.89-5.24 and 0.12-0.64, and 1.32-7.15 and 0.15-0.86, respectively; LR+ and LR- for NAFLD F2-4, F3-4, and F4 were 2.65-3.37 and 0.37-0.39, 2.25-6.76 and 0.07-0.87, and 3.90 and 0.15, respectively. Overall, the proportional odds ratio indicated FIB-4 <1.45 was better than aspartate aminotransferase-to-platelet ratio index <0.5 for F2-4. FIB-4 >3.25 was also better than aspartate aminotransferase-to-platelet ratio index >1.5 for F3-4 and F4. There was limited data for combined tests. CONCLUSIONS: Blood-based biomarkers are associated with small-to-moderate change in pretest probability for diagnosing F2-4, F3-4, and F4 in viral hepatitis, HIV-HCV coinfection, and NAFLD, with limited comparative or combination studies for other chronic liver diseases.
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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 | 0.040 | 0.172 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.013 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 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".