Ju Bing-Jie, Jin Ming, Tian Yang, Zhen Xiang, Kong De-Xing, Wang Wei-Lin, Yan Sheng
Department of Hepatobiliary and Pancreatic Surgery, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, Zhejiang Province, China.
World J Gastrointest Surg. 2021 Feb 27;13(2):127-140. doi: 10.4240/wjgs.v13.i2.127.
Post-hepatectomy liver failure (PHLF) increases morbidity and mortality after liver resection for patients with advanced liver fibrosis and cirrhosis. Preoperative liver stiffness using two-dimensional shear wave elastography (2D-SWE) is widely used to evaluate the degree of fibrosis. However, the 2D-SWE results were not accurate. A durometer measures hardness by quantifying the ability of a material to locally resist the intrusion of hard objects into its surface. However, the durometer score can only be obtained during surgery.
To measure correlations among 2D-SWE, palpation by surgeons, and durometer-measured objective liver hardness and to construct a liver hardness regression model.
We enrolled 74 hepatectomy patients with liver hardness in a derivation cohort. Tactile-based liver hardness scores (0-100) were determined through palpation of the liver tissue by surgeons. Additionally, liver hardness was measured using a durometer. Correlation coefficients for durometer-measured hardness and preoperative parameters were calculated. Multiple linear regression models were constructed to select the best predictive durometer scale. Receiver operating characteristic (ROC) curves and univariate and multivariate analyses were used to calculate the best model's prediction of PHLF and risk factors for PHLF, respectively. A separate validation cohort ( = 162) was used to evaluate the model.
The stiffness measured using 2D-SWE and palpation scale had good linear correlation with durometer-measured hardness (Pearson rank correlation coefficient 0.704 and 0.729, respectively, < 0.001). The best model for the durometer scale (hardness scale model) was based on stiffness, hepatitis B virus surface antigen, and albumin level and had an value of 0.580. The area under the ROC for the durometer and hardness scale for PHLF prediction were 0.807 ( = 0.002) and 0.785 ( = 0.005), respectively. The optimal cutoff value of the durometer and hardness scale was 27.38 (sensitivity = 0.900, specificity = 0.660) and 27.87 (sensitivity = 0.700, specificity = 0.787), respectively. Patients with a hardness scale score of > 27.87 were at a significantly higher risk of PHLF with hazard ratios of 7.835 ( = 0.015). The model's PHLF predictive ability was confirmed in the validation cohort.
Liver stiffness assessed by 2D-SWE and palpation correlated well with durometer hardness values. The multiple linear regression model predicted durometer hardness values and PHLF.
肝切除术后肝衰竭（PHLF）会增加晚期肝纤维化和肝硬化患者肝切除术后的发病率和死亡率。使用二维剪切波弹性成像（2D-SWE）测量的术前肝脏硬度被广泛用于评估纤维化程度。然而，2D-SWE的结果并不准确。硬度计通过量化材料局部抵抗硬物侵入其表面的能力来测量硬度。然而，硬度计评分只能在手术期间获得。
测量2D-SWE、外科医生触诊与硬度计测量的客观肝脏硬度之间的相关性，并构建肝脏硬度回归模型。
我们纳入了74例肝切除患者作为推导队列，其具有肝脏硬度数据。通过外科医生对肝组织的触诊确定基于触觉的肝脏硬度评分（0-100）。此外，使用硬度计测量肝脏硬度。计算硬度计测量的硬度与术前参数的相关系数。构建多元线性回归模型以选择最佳预测硬度计量表。分别使用受试者工作特征（ROC）曲线、单因素和多因素分析来计算最佳模型对PHLF的预测以及PHLF的危险因素。使用一个单独的验证队列（n = 162）来评估该模型。
使用2D-SWE测量的硬度和触诊量表与硬度计测量的硬度具有良好的线性相关性（Pearson等级相关系数分别为0.704和0.729，P < 0.001）。硬度计量表的最佳模型（硬度量表模型）基于硬度、乙肝表面抗原和白蛋白水平，其R²值为0.580。用于预测PHLF的硬度计和硬度量表的ROC曲线下面积分别为0.807（P = 0.002）和0.785（P = 0.005）。硬度计和硬度量表的最佳截断值分别为27.38（敏感性 = 0.900，特异性 = 0.660）和27.87（敏感性 = 0.700，特异性 = 0.787）。硬度量表评分> 27.87的患者发生PHLF的风险显著更高，风险比为7.835（P = 0.015）。该模型对PHLF的预测能力在验证队列中得到证实。
2D-SWE和触诊评估的肝脏硬度与硬度计硬度值具有良好的相关性。多元线性回归模型可预测硬度计硬度值和PHLF。
