Predicting clinical outcomes in chronic hepatitis B patients receiving nucleoside analogues and pegylated interferon alpha: a hematochemical and clinical analysis.
Humans
Hepatitis B, Chronic
/ drug therapy
Male
Interferon-alpha
/ therapeutic use
Antiviral Agents
/ therapeutic use
Female
Adult
Middle Aged
Polyethylene Glycols
/ therapeutic use
Recombinant Proteins
/ therapeutic use
Treatment Outcome
Nucleosides
/ therapeutic use
Hepatitis B Surface Antigens
/ blood
Drug Therapy, Combination
ROC Curve
Hepatitis B virus
/ drug effects
Retrospective Studies
Interferon alpha-2
Chronic Hepatitis B
HBsAg clearance
Nucleos(t)ide analogs
Pegylated interferon alpha
Predictive model
Journal
BMC infectious diseases
ISSN: 1471-2334
Titre abrégé: BMC Infect Dis
Pays: England
ID NLM: 100968551
Informations de publication
Date de publication:
13 Oct 2024
13 Oct 2024
Historique:
received:
08
04
2024
accepted:
03
10
2024
medline:
14
10
2024
pubmed:
14
10
2024
entrez:
13
10
2024
Statut:
epublish
Résumé
The best antiviral treatment for chronic hepatitis B (CHB) poses a complex challenge. The treatment effect of the combination of nucleoside analogues (NAs) and pegylated interferon alpha (PegIFN) was still in debate. We studied patients treated with NAs and PegIFN-2b at our institution from November 2019 to January 2022. Logistic regression identified independent factors influencing clinical cure. The predictive accuracy of the formula was assessed using the Receiver operating characteristic (ROC) curve at different time points (before therapy, 12 weeks, and 24 weeks into treatment). A total of 120 patients were enrolled in the final analysis. Among the cohort of patients under study, 71 (59.1%) patients had clinical cure while 49 (40.9%) patients did not. Hepatitis B surface antigen (HBsAg) at baseline and age were the powerful variables predicting the clearance of HBsAg. The area under the ROC (AUC) was 0.907 for pre-treatment predictive model, 0.958 for 12-week predictive model and 0.747 for 24-week predictive model. This study provided predictive formulas for clinical cure, offering valuable insights for CHB treatment. PegIFN and NAs exhibited efficacy. Future research that explores additional factors, such as HBV genotype, in a larger cohort study is needed.
Sections du résumé
BACKGROUND
BACKGROUND
The best antiviral treatment for chronic hepatitis B (CHB) poses a complex challenge. The treatment effect of the combination of nucleoside analogues (NAs) and pegylated interferon alpha (PegIFN) was still in debate.
METHODS
METHODS
We studied patients treated with NAs and PegIFN-2b at our institution from November 2019 to January 2022. Logistic regression identified independent factors influencing clinical cure. The predictive accuracy of the formula was assessed using the Receiver operating characteristic (ROC) curve at different time points (before therapy, 12 weeks, and 24 weeks into treatment).
RESULTS
RESULTS
A total of 120 patients were enrolled in the final analysis. Among the cohort of patients under study, 71 (59.1%) patients had clinical cure while 49 (40.9%) patients did not. Hepatitis B surface antigen (HBsAg) at baseline and age were the powerful variables predicting the clearance of HBsAg. The area under the ROC (AUC) was 0.907 for pre-treatment predictive model, 0.958 for 12-week predictive model and 0.747 for 24-week predictive model.
CONCLUSION
CONCLUSIONS
This study provided predictive formulas for clinical cure, offering valuable insights for CHB treatment. PegIFN and NAs exhibited efficacy. Future research that explores additional factors, such as HBV genotype, in a larger cohort study is needed.
Identifiants
pubmed: 39396949
doi: 10.1186/s12879-024-10057-0
pii: 10.1186/s12879-024-10057-0
doi:
Substances chimiques
Interferon-alpha
0
Antiviral Agents
0
Polyethylene Glycols
3WJQ0SDW1A
Recombinant Proteins
0
Nucleosides
0
Hepatitis B Surface Antigens
0
peginterferon alfa-2b
G8RGG88B68
Interferon alpha-2
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
1149Subventions
Organisme : Education of Zhejiang Province
ID : Y202045537
Informations de copyright
© 2024. The Author(s).
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