Renin–angiotensin system inhibitors and risk of hepatocellular carcinoma among patients with hepatitis B virus infection
Bibliographic record
Abstract
BACKGROUND: Hepatitis B virus (HBV) infection is a common cause of liver-related morbidity and mortality. Evidence suggests that angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin II receptor blockers (ARBs) decrease liver fibrosis, an intermediate step between liver injury and hepatocellular carcinoma (HCC). Our aim was to investigate the association between the use of ACEIs and ARBs on incident HCC and liver-related mortality among patients with HBV infection. METHODS: We conducted a population-based study on a new-user cohort of patients seen at 24 hospitals across China. We included adult patients with HBV infection who started ACEIs or ARBs (ACEIs/ARBs), or calcium channel blockers or thiazide diuretics (CCBs/THZs) from January 2012 to December 2022. The primary outcome was incident HCC; secondary outcomes were liver-related mortality and new-onset cirrhosis. We used propensity score matching and Cox proportional hazards regression to estimate the hazard ratio (HR) and 95% confidence intervals (CIs) of study outcomes. RESULTS: Among 32 692 eligible patients (median age 58 [interquartile range (IQR) 48-68] yr, and 18 804 male [57.5%]), we matched 9946 pairs of patients starting ACEIs/ARBs or CCBs/THZs. During a mean follow-up of 2.3 years, the incidence rate of HCC per 1000 person-years was 4.11 and 5.94 among patients who started ACEIs/ARBs and CCBs/THZs, respectively, in the matched cohort. Use of ACEIs/ARBs was associated with lower risks of incident HCC (HR 0.66, 95% CI 0.50-0.86), liver-related mortality (HR 0.77, 95% CI 0.64-0.93), and new-onset cirrhosis (HR 0.81, 95% CI 0.70-0.94). INTERPRETATION: In this cohort of patients with HBV infection, new users of ACEIs/ARBs had a lower risk of incident HCC, liver-related mortality, and new-onset cirrhosis than new users of CCBs/THZs.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".