Analysis of High Risk Factors and Construction of A Nomogram Prediction Model for Hepatitis C in Hepatitis C Virus-IgG Anti body-Positive Patients
Objective To analyze the high-risk factors for hepatitis C virus(HCV)diagnosis in patients with hepatitis C virus antibody-immunoglobulin G(HCV-IgG)antibody positive,and to establish a nomogram prediction model to provide a basis for the clinical diagnosis and treatment decision-making of hepatitis C in primary medical institutions.Methods The demographic characteristics and indicators related to hepatitis C of HCV-IgG antibody positive patients who underwent HCV-RNA,HCV-IgG anti-body,liver function,and routine blood detection from January 2022 to October 2023 were retrospectively collected.Logistic regression was used to analyze the high-risk factors for the diagnosis of hepatitis C in HCV-IgG antibody-positive patients,and a nomogram model was constructed to assess the predictive performance and compliance of the model using the consistency coefficient and calibration curve,respec-tively.Results 394 patients with HCV-IgG antibody positive for HCV-RNA positive rate was 30.2％,multivariate logistic analysis showed that HCV-IgG ≥ 5.0 S/CO,aspartate aminotransferase(AST)＞35 U/L and clinical symptoms related to hepatitis were independent risk factors for HCV-RNA positive in the HCV-IgG antibody positive population,and the OR values were 233.926(95％CI:31.814-1 720.046),4.079(95％CI:2.105-7.904),5.295(95％CI:1.505-18.634).The nomogram used to predict HCV-RNA positivity had a precision of 0.923.The value of sensitivity was 99.2％and specificity was 74.5％.Conclusion The nomogram model based on HCV-IgG antibody,AST and hepatitis-related clinical symptoms has high accuracy and can be used to guide clinicians in determining the risk of confirmed HCV diagnosis in HCV-IgG antibody-positive individuals.
hepatitis Chepatitis C virus ribonucleic acidhepatitis C virus antibody-immuno-globulin Gaspartate aminotransferasenomogram
万方数据
维普
