The Economics of Liver Fibrosis Diagnosis: Systematic Review of Non‐Invasive Test Cost‐Effectiveness
Bibliographic record
Abstract
ABSTRACT Hepatic diseases progress silently, leading to fibrosis, cirrhosis, and hepatocellular carcinoma. Although liver biopsy remains the gold standard for fibrosis assessment, it is limited by invasiveness and sampling variability. Non‐invasive liver tests (NILTs) can mitigate biopsy‐related risks. However, some NILTs are costly, and economic/implementation evidence remains limited due to small samples and variability across healthcare systems. This systematic review aimed to evaluate economic studies comparing the costs and benefits of NILTs versus liver biopsy in chronic liver disease. Comprehensive searches were conducted up to February 21, 2024, identifying cost and economic evaluation studies comparing NILTs and biopsy for fibrosis detection in individuals with hepatitis B, hepatitis C, alcoholic liver disease (ALD), and non‐alcoholic fatty liver disease (NAFLD). Sources included PubMed, Embase, and Scopus, supplemented by searches in economic databases. Two reviewers independently screened, appraised, and extracted data. Of 478 studies identified, 17 met inclusion criteria, primarily from Europe and published after 2012. Most studies used cost‐utility analyses adopting a public healthcare perspective and a lifetime horizon. Chronic viral hepatitis and NAFLD were the most studied conditions, with Fibroscan, Enhanced Liver Fibrosis (ELF), and FibroTest as the predominant NILTs. Outcomes included Quality‐Adjusted Life Years (QALYs) and diagnostic accuracy. Incremental Cost‐Effectiveness Ratios (ICERs) ranged from US$28 868 to US$150 000, influenced by factors such as test cost, screening age, and sequential strategies. Despite heterogeneity, most studies conclude that NILTs are cost‐effective, particularly in combination, supporting their broader adoption in the management of chronic liver disease. Trial Registration: PROSPERO: CRD42023404278
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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 | 0.013 | 0.073 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.014 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".