标准肝重量模型在成年脂肪肝供体中的应用：一项前瞻性队列研究。

Standard liver weight model in adult deceased donors with fatty liver: A prospective cohort study.

机构信息

Department of Liver Surgery, Liver Transplantation Centre, West China Hospital of Sichuan University, Chengdu 610041, Sichuan Province, China.

Operating Room, West China Hospital, Sichuan University/West China School of Nursing, Sichuan University, Chengdu 610041, Sichuan Province, China.

出版信息

World J Gastroenterol. 2021 Oct 21;27(39):6701-6714. doi: 10.3748/wjg.v27.i39.6701.

DOI:10.3748/wjg.v27.i39.6701

PMID:34754162

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8554397/

Abstract

BACKGROUND

Standard liver weight (SLW) is frequently used in deceased donor liver transplantation to avoid size mismatches with the recipient. However, some deceased donors (DDs) have fatty liver (FL). A few studies have reported that FL could impact liver size. To the best of our knowledge, there are no relevant SLW models for predicting liver size.

AIM

To demonstrate the relationship between FL and total liver weight (TLW) in detail and present a related SLW formula.

METHODS

We prospectively enrolled 212 adult DDs from West China Hospital of Sichuan University from June 2019 to February 2021, recorded their basic information, such as sex, age, body height (BH) and body weight (BW), and performed abdominal ultrasound (US) and pathological biopsy (PB). The chi-square test and kappa consistency score were used to assess the consistency in terms of FL diagnosed by US relative to PB. Simple linear regression analysis was used to explore the variables related to TLW. Multiple linear regression analysis was used to formulate SLW models, and the root mean standard error and interclass correlation coefficient were used to test the fitting efficiency and accuracy of the model, respectively. Furthermore, the optimal formula was compared with previous formulas.

RESULTS

Approximately 28.8% of DDs had FL. US had a high diagnostic ability (sensitivity and specificity were 86.2% and 92.9%, respectively; kappa value was 0.70, < 0.001) for livers with more than a 5% fatty change. Simple linear regression analysis showed that sex (R, 0.226; < 0.001), BH (R, 0.241; < 0.001), BW (R, 0.441; < 0.001), BMI (R, 0.224; < 0.001), BSA (R, 0.454; < 0.001) and FL (R, 0.130; < 0.001) significantly impacted TLW. In addition, multiple linear regression analysis showed that there was no significant difference in liver weight between the DDs with no steatosis and those with steatosis within 5%. Furthermore, in the context of hepatic steatosis, TLW increased positively (non-linear); compared with the TLW of the non-FL group, the TLW of the groups with hepatic steatosis within 5%, between 5% and 20% and more than 20% increased by 0 g, 90 g, and 340 g, respectively. A novel formula, namely, -348.6 + (110.7 x Sex [0 = Female, 1 = Male]) + 958.0 x BSA + (179.8 x FL [0 = No, 1 = Yes]), where FL was diagnosed by US, was more convenient and accurate than any other formula for predicting SLW.

CONCLUSION

FL is positively correlated with TLW. The novel formula deduced using sex, BSA and FL is the optimal formula for predicting SLW in adult DDs.

摘要

背景

标准肝重（SLW）常用于尸体供肝移植，以避免与受体的大小不匹配。然而，一些尸体供者（DD）有脂肪肝（FL）。一些研究报告称，FL 可能会影响肝大小。据我们所知，目前尚无相关的 SLW 模型来预测肝大小。

目的

详细展示 FL 与总肝重（TLW）之间的关系，并提出相关的 SLW 公式。

方法

我们前瞻性地纳入了 2019 年 6 月至 2021 年 2 月期间来自四川大学华西医院的 212 名成年 DD，记录了他们的基本信息，如性别、年龄、身高（BH）和体重（BW），并进行了腹部超声（US）和病理活检（PB）。使用卡方检验和 Kappa 一致性评分评估 US 诊断的 FL 与 PB 之间的一致性。使用简单线性回归分析探讨与 TLW 相关的变量。使用多元线性回归分析制定 SLW 模型，并使用均方根标准误差和组内相关系数分别测试模型的拟合效率和准确性。此外，还对最优公式与以往公式进行了比较。

结果

大约 28.8%的 DD 有 FL。US 对肝脂肪变性超过 5%的肝脏具有较高的诊断能力（灵敏度和特异性分别为 86.2%和 92.9%，kappa 值为 0.70，<0.001）。简单线性回归分析显示，性别（R，0.226；<0.001）、BH（R，0.241；<0.001）、BW（R，0.441；<0.001）、BMI（R，0.224；<0.001）、BSA（R，0.454；<0.001）和 FL（R，0.130；<0.001）均显著影响 TLW。此外，多元线性回归分析显示，无脂肪变性的 DD 与脂肪变性 5%以内的 DD 之间的肝重量无显著差异。此外，在肝脂肪变性的情况下，TLW 呈正（非线性）增加；与非 FL 组相比，脂肪变性 5%以内、5%-20%和超过 20%的 TLW 分别增加了 0 g、90 g 和 340 g。一种新的公式，即-348.6+（110.7 x Sex[0=Female，1=Male]）+958.0 x BSA+（179.8 x FL[0=No，1=Yes]），其中 FL 是通过 US 诊断的，用于预测 SLW 比任何其他公式都更方便和准确。