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A Model to Identify Gray Zone Patients With Chronic Hepatitis B Requiring Antiviral Therapy: A Multicenter
Xue-Yan Yang1,2,3, Xi-Dong Li4, Bai-Yun Wu1,2
1Department of Hepatology, Qilu Hospital of Shandong University.
Insights
A new non-invasive model helps identify chronic hepatitis B grey-zone patients who need antiviral therapy (AVT). This tool aids in managing patients with unclear staging, improving treatment decisions.
Area of Science:
- Hepatology
- Virology
- Clinical Medicine
Background:
- Chronic hepatitis B (CHB) patients not fitting specific natural history stages are in a 'grey-zone,' complicating management.
- Optimal antiviral therapy (AVT) decisions for these grey-zone CHB patients remain unclear.
Purpose of the Study:
- To develop and validate a non-invasive predictive model for identifying grey-zone CHB patients who require AVT.
Main Methods:
- Retrospective data from 200 grey-zone CHB patients were analyzed.
- Patients were divided into development (n=140) and validation (n=60) cohorts.
- Regression analyses identified variables for a nomogram predicting AVT need, validated by AUC, calibration, and decision curve analysis.
Main Results:
- The nomogram incorporated age, alanine aminotransferase, lymphocyte percentage, platelet count, and international normalized ratio.
- The model demonstrated good discriminability (AUC=0.755 in development, 0.707 in validation) and calibration.
- Scores >197 indicated high AVT probability, while scores ≤132 indicated low probability.
Conclusions:
- The developed nomogram is a promising non-invasive tool for identifying grey-zone CHB patients who require AVT.
- Accurate identification of these patients can guide appropriate management strategies.
Background:
Individuals who do not match any specific immune stage of chronic hepatitis B are classified into the gray zone, and appropriate management for these patients remains unclear. This study aimed to develop and validate a noninvasive model to identify gray zone patients requiring antiviral therapy (AVT).
Methods:
We retrospectively collected data on 200 gray zone patients not requiring AVT, according to assessment by noninvasive parameters from 2010 to 2023 in 6 hospitals, and randomized them into development (n = 140) and validation (n = 60) cohorts. Univariable and multivariable regression analyses were performed to identify independent variables for establishing a nomogram to predict the probability of requiring AVT by liver biopsy.
Results:
Seventy-eight patients (n = 39%) were identified as requiring AVT. The following were identified as independent variables for constructing the nomogram: age (odds ratio [OR], 1.06; 95% CI, 1.01-1.11), alanine aminotransferase (OR, 2.43; 95% CI, 1.08-5.59), lymphocyte percentage (OR, 6.43; 95% CI, 1.23-33.64), platelet count (OR, 0.99; 95% CI, .98-1.00), and international normalized ratio (per 0.01) (OR, 0.99; 95% CI, .98-.1.00). These variables showed good discriminability based on the development data set (area under the curve, 0.755) and validation data set (area under the curve, 0.707), calibration, and clinical applicability.
Conclusions:
Gray zone patients requiring AVT should be identified, and the model developed here is a promising tool.
Clinical Trials Registration:
ClinicalTrials.gov NCT06041022.
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