Development and validation of a noninvasive prediction model of autoimmune hepatitis in patients with liver diseases
Development and validation of a noninvasive prediction model of autoimmune hepatitis in patients with liver diseases
复制标题
肝病患者自身免疫性肝炎无创预测模型的开发和验证
DOI：
10.1080/00365521.2023.2249571
复制
发表时间：
2023
影响因子：
1.9
通讯作者：
Yong
中科院分区：
文献类型：
--
作者：
Li Wang;Yi;An;Zhi;Hong;Ping Zhu;Li;Y. Zhong;Zhi;Shuang;Yong
抽象的背景没有用于诊断自身免疫性肝炎，但我们开发了一种非侵入性预测模型，以优化从2017年1月至2019年1月的1739年患者进行诊断的诊断。实验室和组织学数据是回顾性的。 ISTIC回归分析，包括ALT，IgG，ALP/AST，ALB，ANA，AMA，HBSAG，年龄和性别的分析，以建立非侵入性预测模型，以诊断性自身免疫性肝炎的良好预测，其AUROC为0.967（95％CI：0.7776-0.891 ins in Intery ci：95％）in 19）在外部验证中。 ALP/AST，ALB，ANA，AMA，HBSAG，年龄和性别是诊断出意外肝病患者自身免疫性肝炎的预测因素。
Abstract Background and Aims There is no golden standard for the diagnosis of autoimmune hepatitis which still dependent on liver biopsy currently. So, we developed a noninvasive prediction model to help optimize the diagnosis of autoimmune hepatitis. Methods From January 2017 to December 2019, 1739 patients who had undergone liver biopsy were seen in the second hospital of Nanjing, of which 128 were here for consultation. Clinical, laboratory, and histologic data were obtained retrospectively. Multivariable logistic regression analysis was employed to create a nomogram model that predicting the risk of autoimmune hepatitis. Internal and external validation was both performed to evaluate the model. Results A total of 1288 patients with liver biopsy were enrolled (1184 from the second hospital of Nanjing, the remaining 104 from other centers). After the univariate and multivariate logistic regression analysis, nine variables including ALT, IgG, ALP/AST, ALB, ANA, AMA, HBsAg, age, and gender were selected to establish the noninvasive prediction model. The nomogram model exhibits good prediction in diagnosing autoimmune hepatitis with AUROC of 0.967 (95% CI: 0.776–0.891) in internal validation and 0.835 (95% CI: 0.752–0.919) in external validation. Conclusions ALT, IgG, ALP/AST, ALB, ANA, AMA, HBsAg, age, and gender are predictive factors for the diagnosis of autoimmune hepatitis in patients with unexplained liver diseases. The predictive nomogram model built by the nine predictors achieved good prediction for diagnosing autoimmune hepatitis.