Observational studyDrug design, development and therapy2025
Development and Validation of a Predictive Model for HBsAg Seroclearance After Peg-IFN-Based Therapy: A Multicentre Study.
Observational study in Drug design, development and therapy, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06196632 (Development and Validation of Logistic Regression Models to Predict Durable Functional Cure in Patients With Chronic Hepatitis B After Pegylated Interferon Alpha-2b Based Therapy), which is not on this map. Not yet cited in PubMed.
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Development and Validation of Logistic Regression Models to Predict Durable Functional Cure in Patients With Chronic Hepatitis B After Pegylated Interferon Alpha-2b Based Therapy
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16 authors.
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Abstract
Purpose: Early prediction of HBsAg seroclearance prior to the application of Peg-IFN-based therapy has important clinical implications. This study aims to construct a predictive model with baseline parameters for HBsAg seroclearance after Peg-IFN-based therapy in virally suppressed patients with HBeAg-negative chronic hepatitis B (CHB). Patients and Methods: From January 1, 2018 to May 1, 2023, we retrospectively enrolled 377 nucleos(t)ide analogue-suppressed patients with HBeAg-negative CHB who received a 48-week Peg-IFN-based therapy from 10 centers in China. A multivariate cox regression model was developed for predicting HBsAg seroclearance in a development cohort with 229 patients recruited from 5 centers, then validated in an independent validation cohort with 148 patients recruited from another 5 centers. This study is registered with ClinicalTrials.gov, number NCT06196632. Results: In the development and validation cohort, 17.9% (41/229) and 20.27% (30/148) of patients achieved HBsAg seroclearance, respectively. The best performing model was constructed by age (HR 0.962, 95% CI 0.928-0.997), baseline HBsAg (HR 0.998, 95% CI 0.997-0.999) and alanine aminotransferase (HR 1.008, 95% CI 1.003-1.012). It showed good predictive performance in predicting HBsAg seroclearance in both the development [area under the receiver operating characteristic curve (AUC) 0.842] and validation cohort (AUC 0.852). Using cut-off points of -2.7 and -1.3, it can identify HBeAg-negative CHB patients with high, intermediate and low incidence rate of HBsAg seroclearance. Conclusion: A model was constructed with baseline parameters for predicting HBsAg seroclearance after Peg-IFN-based therapy in virally suppressed patients with HBeAg-negative CHB. It showed good predictive value and can provide guidance for the clinical application of Peg-IFN-based therapy.
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