Incidence of Hepatic Decompensation After Nucleos(t)ide Analog Withdrawal: Results From a Large, International, Multiethnic Cohort of Patients With Chronic Hepatitis B (RETRACT-B Study)
Bibliographic record
Abstract
INTRODUCTION: Despite improvements in the management of chronic hepatitis B (CHB), risk of cirrhosis and hepatocellular carcinoma remains. While hepatitis B surface antigen loss is the optimal end point, safe discontinuation of nucleos(t)ide analog (NA) therapy is controversial because of the possibility of severe or fatal reactivation flares. METHODS: This is a multicenter cohort study of virally suppressed, end-of-therapy (EOT) hepatitis B e antigen (HBeAg)-negative CHB patients who stopped NA therapy (n = 1,557). Survival analysis techniques were used to analyze off-therapy rates of hepatic decompensation and differences by patient characteristics. We also examined a subgroup of noncirrhotic patients with consolidation therapy of ≥12 months before cessation (n = 1,289). Hepatic decompensation was considered related to therapy cessation if diagnosed off therapy or within 6 months of starting retreatment. RESULTS: Among the total cohort (11.8% diagnosed with cirrhosis, 84.2% start-of-therapy HBeAg-negative), 20 developed hepatic decompensation after NA cessation; 10 events were among the subgroup. The cumulative incidence of hepatic decompensation at 60 months off therapy among the total cohort and subgroup was 1.8% and 1.1%, respectively. The hepatic decompensation rate was higher among patients with cirrhosis (hazard ratio [HR] 5.08, P < 0.001) and start-of-therapy HBeAg-positive patients (HR 5.23, P < 0.001). This association between start-of-therapy HBeAg status and hepatic decompensation remained significant even among the subgroup (HR 10.5, P < 0.001). DISCUSSION: Patients with cirrhosis and start-of-therapy HBeAg-positive patients should be carefully assessed before stopping NAs to prevent hepatic decompensation. Frequent monitoring of viral and host kinetics after cessation is crucial to determine patient outcome.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".