Defining a Histologic Scoring System for Gestational Alloimmune Liver Disease

妊娠期同种免疫性肝病组织学评分系统的定义

阅读:1

Abstract

Gestational alloimmune liver disease (GALD) is a leading cause of neonatal acute liver failure (ALF) with unique histologic features but no established histologic scoring criteria. This study aimed to develop an accurate histologic scoring system to distinguish GALD from non-GALD neonatal ALF. A preliminary system using 6 histologic features characteristic of GALD was created. Four pathologists from 2 institutions applied this system to GALD (n=11) and non-GALD (n=20) neonatal ALF cases from 2008 to 2020. Four cases of Trisomy 21-associated transient myeloproliferative disorder were analyzed separately, as these patients can present with neonatal ALF and display GALD histologic features but are clinically distinguishable. Area under the receiver operating curve (AUROC) was fitted for stepwise combinations of features to determine the most accurate scoring system. GALD histologic features included extensive parenchymal fibrosis and neotubules, and a paucity of healthy hepatocytes, portal tract involvement, extramedullary hematopoiesis, and inflammation. A revised 3-feature system including parenchymal fibrosis, neotubules, and hepatocyte characterization established highest accuracy with an AUROC of 0.891 ( P <0.001). Importantly, there were no significant interinstitutional differences in scores assigned to GALD versus non-GALD cases. A 3-factor score of <2 had 100% sensitivity (95% CI: 74%-100%) to exclude GALD and a score >5 had 95% specificity (95% CI: 76%-100%) to diagnose GALD. This study establishes a highly accurate histologic scoring system to differentiate GALD from non-GALD neonatal ALF. Findings may aid in accurate diagnosis of index cases, reducing recurrence risk in subsequent pregnancies and lowering morbidity and mortality associated with GALD.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。