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Volume 40 Issue 7
Jul.  2024
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Article Navigation >  Journal of Clinical Hepatology  > 2024  >  40(7): 1328-1333

Establishment of a noninvasive predictive model for antiviral therapy in patients with chronic hepatitis B virus infection and an age of ≤30 years

DOI: 10.12449/JCH240708
  • 1.

    Department of Liver Diseases，National Clinical Research Center for Infectious Disease，Shenzhen Third People’s Hospital，The Second Affiliated Hospital of Southern University of Science and Technology，Shenzhen，Guangdong 518116，China

  • 2.

    Department of Infectious Diseases，The First Hospital of Changsha，Changsha 410000，China

Research funding:

Shenzhen Key Medical Discipline Construction Fund (SZXK076)

More Information
  • Corresponding author: WANG Fang， kaixin919@163.com （ORCID： 0009-0003-0092-3403）
  • Received Date: 2023-11-08
  • Accepted Date: 2024-01-17
  • Published Date: 2024-07-25
    Abstract
  •   Objective  To predict whether antiviral therapy is required in patients with chronic hepatitis B virus （HBV） infection and an age of ≤30 years by establishing a noninvasive model， and to investigate the diagnostic value of this model.  Methods  A retrospective analysis was performed for the clinical data of 175 patients with chronic HBV infection who were admitted to Shenzhen Third People’s Hospital from January 2017 to January 2023 and met the inclusion criteria， and according to the results of liver biopsy， they were divided into treatment group with 41 patients （with indications for antiviral therapy） and observation group with 134 patients （without indications for antiviral therapy）. The two groups were analyzed in terms of the indicators including clinical data， imaging examinations， and serum biochemical parameters. The univariate and multivariate Logistic regression analyses were used to investigate the parameters affecting the indication for antiviral therapy， and different models for predicting the need for antiviral therapy were constructed based on related parameters. The receiver operating characteristic （ROC） curve was used to compare the diagnostic value of different models. The independent-samples t test was used for comparison of normally distributed continuous variables between groups， and the Mann-Whitney U rank sum test was used for comparison of non-normally distributed continuous variables between groups； the chi-square test or the Fisher’s exact test was used for comparison of categorical data between groups.  Results  There were significant differences between the treatment group and the observation group in alanine aminotransferase， ferritin， total cholesterol （CHOL）， triglyceride， platelet count， liver stiffness measured by sound touch elastography （STE）， and procollagen III N-terminal propeptide （PIIIP） （all P<0.05）. The multivariate Logistic regression analysis showed that CHOL （odds ratio ［OR］=0.4， 95% confidence interval ［CI］： 0.2‍ ‍—‍ ‍1.0）， STE （OR=1.5， 95%CI： 1.0‍ ‍—‍ ‍2.1）， and PIIIP （OR=1.1， 95%CI： 1.0‍ ‍—‍ ‍1.1） were independent predictive factors for the indications for antiviral therapy. Model 1 （STE+PIIIP+CHOL）， model 2 （STE+PIIIP）， model 3 （STE+CHOL）， model 4 （PIIIP+CHOL） had an area under the ROC curve of 0.908， 0.848， 0.725， and 0.725， respectively， while STE， PIIIP， and CHOL used alone had an AUC of 0.836， 0.725， and 0.634， respectively， suggesting that model 1 had the largest AUC， with a specificity of 77.34% and a sensitivity of 96.36%， and had a significant difference compared with STE， PIIIP， CHOL， and the models 2， 3， and 4 （Z=0.21， 3.08， 3.06， 3.23， 0.89， and 0.88， all P<0.05）.  Conclusion  The noninvasive model established based on CHOL， STE， and PIIIP has a good value in predicting the need for antiviral therapy in patients with chronic HBV infection and an age of ≤30 years.

     

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    Created with Highcharts 5.0.7Chart context menuAccess Area Distribution其他: 5.0 %其他: 5.0 %其他: 0.3 %其他: 0.3 %San Lorenzo: 0.2 %San Lorenzo: 0.2 %San Mateo: 0.1 %San Mateo: 0.1 %Tiruchi: 0.1 %Tiruchi: 0.1 %三门峡: 0.2 %三门峡: 0.2 %上海: 1.7 %上海: 1.7 %东京都: 0.2 %东京都: 0.2 %东莞: 0.1 %东莞: 0.1 %丽水: 1.5 %丽水: 1.5 %乌鲁木齐: 0.1 %乌鲁木齐: 0.1 %佛山: 0.2 %佛山: 0.2 %信阳: 0.3 %信阳: 0.3 %兰州: 0.1 %兰州: 0.1 %凉山: 0.1 %凉山: 0.1 %北京: 2.7 %北京: 2.7 %南宁: 0.5 %南宁: 0.5 %南昌: 0.1 %南昌: 0.1 %卡纳塔克: 0.2 %卡纳塔克: 0.2 %厦门: 0.3 %厦门: 0.3 %古吉拉特: 0.1 %古吉拉特: 0.1 %台州: 1.1 %台州: 1.1 %合肥: 0.1 %合肥: 0.1 %吉林: 0.3 %吉林: 0.3 %呼伦贝尔: 0.2 %呼伦贝尔: 0.2 %咸宁: 0.1 %咸宁: 0.1 %咸阳: 0.9 %咸阳: 0.9 %哈尔滨: 0.5 %哈尔滨: 0.5 %嘉兴: 0.5 %嘉兴: 0.5 %大连: 0.7 %大连: 0.7 %天津: 0.1 %天津: 0.1 %太原: 0.1 %太原: 0.1 %宁德: 0.1 %宁德: 0.1 %宁波: 1.0 %宁波: 1.0 %安康: 0.3 %安康: 0.3 %宜春: 0.3 %宜春: 0.3 %宿迁: 1.2 %宿迁: 1.2 %密蘇里城: 0.1 %密蘇里城: 0.1 %崇左: 0.2 %崇左: 0.2 %巴黎: 0.1 %巴黎: 0.1 %常州: 0.2 %常州: 0.2 %常德: 0.6 %常德: 0.6 %广州: 2.1 %广州: 2.1 %廊坊: 0.7 %廊坊: 0.7 %张家口: 2.0 %张家口: 2.0 %张家界: 0.1 %张家界: 0.1 %徐州: 0.3 %徐州: 0.3 %德阳: 0.6 %德阳: 0.6 %恩施: 0.1 %恩施: 0.1 %成都: 1.0 %成都: 1.0 %扬州: 0.3 %扬州: 0.3 %抚州: 0.5 %抚州: 0.5 %揭阳: 0.1 %揭阳: 0.1 %文昌: 0.2 %文昌: 0.2 %新余: 0.1 %新余: 0.1 %无锡: 0.5 %无锡: 0.5 %日照: 0.1 %日照: 0.1 %昆明: 0.4 %昆明: 0.4 %朝阳: 0.6 %朝阳: 0.6 %杭州: 1.1 %杭州: 1.1 %枣庄: 0.1 %枣庄: 0.1 %榆林: 0.6 %榆林: 0.6 %武汉: 0.2 %武汉: 0.2 %汕头: 0.1 %汕头: 0.1 %江门: 0.3 %江门: 0.3 %池州: 0.2 %池州: 0.2 %沈阳: 0.3 %沈阳: 0.3 %泉州: 0.2 %泉州: 0.2 %泰安: 0.1 %泰安: 0.1 %泰州: 0.2 %泰州: 0.2 %泸州: 0.1 %泸州: 0.1 %洛阳: 0.1 %洛阳: 0.1 %济南: 0.5 %济南: 0.5 %海得拉巴: 0.2 %海得拉巴: 0.2 %海西: 0.3 %海西: 0.3 %淄博: 0.4 %淄博: 0.4 %淮北: 0.3 %淮北: 0.3 %淮南: 0.1 %淮南: 0.1 %淮安: 0.2 %淮安: 0.2 %深圳: 1.6 %深圳: 1.6 %渭南: 0.1 %渭南: 0.1 %湖州: 0.6 %湖州: 0.6 %湘潭: 0.1 %湘潭: 0.1 %湛江: 0.2 %湛江: 0.2 %漳州: 1.2 %漳州: 1.2 %焦作: 0.2 %焦作: 0.2 %珠海: 0.2 %珠海: 0.2 %盐城: 0.3 %盐城: 0.3 %盘锦: 0.3 %盘锦: 0.3 %石家庄: 0.4 %石家庄: 0.4 %福州: 0.4 %福州: 0.4 %科泽科德: 0.1 %科泽科德: 0.1 %秦皇岛: 0.2 %秦皇岛: 0.2 %纽约: 0.1 %纽约: 0.1 %绍兴: 1.2 %绍兴: 1.2 %聊城: 0.1 %聊城: 0.1 %胡志明: 0.1 %胡志明: 0.1 %自贡: 0.1 %自贡: 0.1 %舟山: 0.3 %舟山: 0.3 %芒廷维尤: 16.1 %芒廷维尤: 16.1 %芝加哥: 1.3 %芝加哥: 1.3 %苏州: 0.7 %苏州: 0.7 %荆州: 0.1 %荆州: 0.1 %荆门: 0.5 %荆门: 0.5 %莆田: 0.1 %莆田: 0.1 %莫斯科: 0.3 %莫斯科: 0.3 %萍乡: 0.7 %萍乡: 0.7 %营口: 0.2 %营口: 0.2 %葫芦岛: 0.4 %葫芦岛: 0.4 %蚌埠: 0.3 %蚌埠: 0.3 %衡水: 0.5 %衡水: 0.5 %衢州: 0.3 %衢州: 0.3 %襄阳: 1.1 %襄阳: 1.1 %西宁: 6.6 %西宁: 6.6 %西安: 1.3 %西安: 1.3 %西雅图: 0.2 %西雅图: 0.2 %诺沃克: 0.3 %诺沃克: 0.3 %贵阳: 0.2 %贵阳: 0.2 %辽阳: 0.5 %辽阳: 0.5 %运城: 0.5 %运城: 0.5 %连云港: 1.3 %连云港: 1.3 %邯郸: 0.1 %邯郸: 0.1 %邵阳: 0.2 %邵阳: 0.2 %郑州: 0.7 %郑州: 0.7 %郴州: 1.0 %郴州: 1.0 %重庆: 1.5 %重庆: 1.5 %金华: 0.3 %金华: 0.3 %铁岭: 0.5 %铁岭: 0.5 %锦州: 0.2 %锦州: 0.2 %镇江: 0.2 %镇江: 0.2 %长春: 8.4 %长春: 8.4 %长沙: 0.8 %长沙: 0.8 %长治: 0.3 %长治: 0.3 %防城港: 0.1 %防城港: 0.1 %随州: 0.3 %随州: 0.3 %雅安: 0.2 %雅安: 0.2 %雷德蒙德: 0.1 %雷德蒙德: 0.1 %青岛: 0.8 %青岛: 0.8 %鞍山: 2.1 %鞍山: 2.1 %马尼萨: 0.2 %马尼萨: 0.2 %黄冈: 0.6 %黄冈: 0.6 %黄南: 0.1 %黄南: 0.1 %黄山: 0.1 %黄山: 0.1 %黔东南: 0.2 %黔东南: 0.2 %其他其他San LorenzoSan MateoTiruchi三门峡上海东京都东莞丽水乌鲁木齐佛山信阳兰州凉山北京南宁南昌卡纳塔克厦门古吉拉特台州合肥吉林呼伦贝尔咸宁咸阳哈尔滨嘉兴大连天津太原宁德宁波安康宜春宿迁密蘇里城崇左巴黎常州常德广州廊坊张家口张家界徐州德阳恩施成都扬州抚州揭阳文昌新余无锡日照昆明朝阳杭州枣庄榆林武汉汕头江门池州沈阳泉州泰安泰州泸州洛阳济南海得拉巴海西淄博淮北淮南淮安深圳渭南湖州湘潭湛江漳州焦作珠海盐城盘锦石家庄福州科泽科德秦皇岛纽约绍兴聊城胡志明自贡舟山芒廷维尤芝加哥苏州荆州荆门莆田莫斯科萍乡营口葫芦岛蚌埠衡水衢州襄阳西宁西安西雅图诺沃克贵阳辽阳运城连云港邯郸邵阳郑州郴州重庆金华铁岭锦州镇江长春长沙长治防城港随州雅安雷德蒙德青岛鞍山马尼萨黄冈黄南黄山黔东南

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    The first journal specializing in hepatobiliary and pancreatic diseases in China

    Supervisor：Ministry of Education of the People's Republic of China

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    Academic Support: Chinese Society of Hepatology，Chinese Medical Association

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