Cost‐effectiveness of quantitative hepatitis B virus surface antigen testing in pregnancy in predicting vertical transmission risk
Bibliographic record
Abstract
Abstract Background & Aims Vertical transmission of hepatitis B virus (HBV) can occur despite immunoprophylaxis in mothers with high HBV DNA levels (>5–7 log10 IU/ml). Quantitative hepatitis B surface antigen (qHBsAg) testing could be used as a surrogate marker to identify high viral load carriers, but there is limited data in pregnancy. We conducted a prospective observational study to determine the cost‐effectiveness and utility of qHBsAg as a valid surrogate marker of HBV DNA. Methods Pregnant patients with chronic hepatitis B were recruited from a tertiary referral centre. HBV DNA levels and qHBsAg were assessed in the second to third trimester. Statistical analysis was performed by Spearman's rank correlation and student's t‐test. The cost‐effectiveness of qHBsAg as compared to HBV DNA testing was calculated. Results Ninety nine women with 103 pregnancies, median age 32 years, 65% Asian, 23% African and 12% other [Hispanic, Caucasian] were enrolled. Overall, 23% (23/99) were HBV e Ag (HBeAg)‐positive. A significant correlation between qHBsAg and HBV DNA levels was noted in HBeAg‐positive patients (r = 0.79, P < 0.05) but not in HBeAg‐negative patients (r = 0.17, P = 0.06). In receiver operating characteristic analysis, the optimal qHBsAg cut‐off values for predicting maternal viraemia associated with immunoprophylaxis failure (i.e., HBV DNA ≥7 log10 IU/ml) was 4.3 log10 IU/ml (accuracy 98.7%, sensitivity 94.7%, specificity 94.4%) (95% CI, 97–100%, P < 0.05). Use of HBV DNA as compared to qHBsAg costs approximately $20 000 more per infection prevented. Conclusion In resource poor regions, qHBsAg could be used as a more cost‐effective marker for high maternal viraemia, and indicate when anti‐HBV nucleos/tide analogue therapy should be used to prevent HBV immunoprophylaxis failure.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".