A209 NON-INVASIVE ASSESSMENT OF LIVER FIBROSIS USING APRI, FIB-4, AND TRANSIENT ELASTOGRAPHY IN CHRONIC HEPATITIS B PATIENTS
Bibliographic record
Abstract
Abstract Background Accurate assessment of liver fibrosis is important to identify patients with chronic hepatitis B (CHB) who require antiviral therapy. As liver biopsy is invasive and costly, non-invasive tests of liver fibrosis are increasingly being used. Aims We aimed to evaluate the performance of the aspartate aminotransferase-to-platelet ratio index (APRI), Fibrosis 4 index (FIB-4), and transient elastography (TE) in predicting fibrosis in patients with CHB. Methods We retrospectively analyzed a prospectively enrolled cohort of consecutive adults with CHB who underwent liver biopsy for routine clinical indications (ALT > ULN and HBV DNA > 2,000 IU/ml) from January 2018 to December 2019. Demographic information, routine biochemistry, HBV serology including HBV DNA, abdominal ultrasound, fibrosis stage by liver biopsy and TE data were collected. Positive predictive values (PPV) and negative predictive values (NPV) were calculated using published cut-off values with liver biopsy as the reference standard. Results Fifty-five patients of Asian ethnicity (mean age 46 years, 65% male) were included. Most patients were HBeAg-negative (67%) and treatment-naïve (80%). Eleven (20%) patients had advanced fibrosis (F3-F4 METAVIR) and 4 (7%) patients had cirrhosis (F4). APRI <0.50 had a NPV of 73% for significant fibrosis (F2-F4) and APRI >1.50 had a PPV of 33% for significant. All 4 patients with cirrhosis were misclassified as having no cirrhosis with an APRI <1. FIB-4 <1.45 had a NPV of 90% for advanced fibrosis (F3-F4). No patient, including 11 patients with advanced fibrosis, had a FIB-4 above the cut-off value to detect advanced fibrosis (>3.25). TE data was available for 38 patients. TE <7.25 kPa had a NPV of 78% for significant fibrosis and TE >12.4 kPa had a PPV of 50% for cirrhosis. Conclusions In Asian patients with CHB and a low prevalence of advanced fibrosis or cirrhosis, APRI, FIB-4, and TE performed well in excluding those with advanced fibrosis but were unable to accurately identify those with significant/advanced fibrosis and cirrhosis. Further studies with larger numbers of CHB patients are needed to confirm our results. Funding Agencies None
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".