End-of-treatment HBsAg, HBcrAg and HBV RNA predict the risk of off-treatment ALT flares in chronic hepatitis B patients
Bibliographic record
Abstract
BACKGROUND/PURPOSE(S): Since ALT flares after therapy withdrawal are associated with adverse outcomes, risk stratification is of major importance. We aimed to study whether off-treatment flares are related with virological outcomes, and if serum levels of novel biomarkers at end-of-treatment (EOT) can predict flares. METHODS: Chronic hepatitis B patients who participated in three global randomised trials of peginterferon-based therapy were studied (99-01, PARC, ARES). HBV RNA, HBsAg and HBcrAg were quantified at EOT. Associations between EOT biomarker levels and flares were assessed as continuous data and after categorisation. Flares were defined as ALT ≥5xULN during six months after therapy cessation. RESULTS: We included 344 patients; 230 HBeAg-positive and 114 HBeAg-negative. Patients were predominantly Caucasian (77.0%) and had genotype A/B/C/D in 23.3/7.3/13.4/52.3%. Flares were observed in 122 patients (35.5%). Flares were associated with lower rates of sustained response (3.5% vs 26.8% among patients with and without a flare; p < 0.001). Higher HBsAg (OR 1.586, 95%CI 1.231-2.043), HBV RNA (OR 1.695, 95%CI 1.371-2.094) and HBcrAg (OR 1.518, 95%CI 1.324-1.740) levels were associated with higher risk of flares (p < 0.001). Combinations of biomarkers further improved risk stratification, especially HBsAg + HBV RNA. Findings were consistent in multivariate analysis adjusted for potential predictors including HBeAg-status and EOT-response (HBV DNA <200 IU/mL). CONCLUSION: Off-treatment ALT flares were not associated with favourable virological outcomes. Higher EOT serum HBsAg, HBcrAg and HBV RNA were associated with a higher risk of flares after therapy withdrawal. These findings can be used to guide decision-making regarding therapy discontinuation and off-treatment follow-up. TRIAL REGISTRATION: ClinicalTrials.gov: NCT00114361, NCT00146705, NCT00877760.
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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) | 0.000 | 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".