Department of Infectious Diseases, Nanjing Drum Tower Hospital Clinical College of Nanjing University of Chinese Medicine, Nanjing, Jiangsu, China.
Department of Infectious Diseases, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, China; Institute of Viruses and Infectious Diseases, Nanjing University, Nanjing, Jiangsu, China.
Ann Hepatol. 2023 Nov-Dec;28(6):101134. doi: 10.1016/j.aohep.2023.101134. Epub 2023 Jul 11.
Assessment of liver inflammation plays a vital role in the management of patients with autoimmune hepatitis (AIH). We aimed to establish and validate a nomogram to predict severe liver inflammation in AIH patients.
AIH patients who underwent liver biopsy were included and randomly divided into a training set and a validation set. Independent predictors of severe liver inflammation were selected by the least absolute shrinkage and selection operator regression from the training set and used to conduct a nomogram. Receiver characteristic curves (ROC), calibration curves, and decision curve analysis (DCA) were adopted to evaluate the performance of nomogram.
Of the 213 patients, female patients accounted for 83.1% and the median age was 53.0 years. The albumin, gamma-glutamyl transpeptidase, total bilirubin, red cell distribution width, prothrombin time, and platelets were independent predictors of severe inflammation. An online AIHI-nomogram was established and was available at https://ndth-zzy.shinyapps.io/AIHI-nomogram/. The calibration curve revealed that the AIHI-nomogram had a good agreement with actual observation in the training and validation sets. The area under the ROCs of AIHI-nomogram were 0.795 in the training set and 0.759 in the validation set, showing significantly better performance than alanine aminotransferase and immunoglobulin G in the training and validation sets, as well in AIH patients with normal ALT in the training set. DCA indicated that the AIHI-nomogram was clinically useful.
This novel AIHI-nomogram provided an excellent prediction of severe liver inflammation in AIH patients and could be used for the better management of AIH.
评估肝脏炎症在自身免疫性肝炎（AIH）患者的管理中起着至关重要的作用。我们旨在建立和验证一个预测 AIH 患者严重肝脏炎症的列线图。
纳入接受肝活检的 AIH 患者，并将其随机分为训练集和验证集。从训练集中通过最小绝对收缩和选择算子回归选择严重肝脏炎症的独立预测因子，并用于构建列线图。采用受试者工作特征曲线（ROC）、校准曲线和决策曲线分析（DCA）评估列线图的性能。
在 213 例患者中，女性患者占 83.1%，中位年龄为 53.0 岁。白蛋白、γ-谷氨酰转肽酶、总胆红素、红细胞分布宽度、凝血酶原时间和血小板是严重炎症的独立预测因子。建立了一个在线 AIHI-列线图，并可在 https://ndth-zzy.shinyapps.io/AIHI-nomogram/ 上访问。校准曲线显示，AIHI-列线图在训练集和验证集中与实际观察结果具有良好的一致性。ROC 曲线下面积在训练集和验证集中分别为 0.795 和 0.759，在训练集和验证集中均明显优于丙氨酸氨基转移酶和免疫球蛋白 G，在训练集中丙氨酸氨基转移酶正常的 AIH 患者中也是如此。DCA 表明 AIHI-列线图具有临床实用性。
这个新的 AIHI-列线图可以很好地预测 AIH 患者的严重肝脏炎症，并可用于更好地管理 AIH。
