Motoyama Hiroaki, Kobayashi Akira, Yokoyama Takahide, Shimizu Akira, Furusawa Norihiko, Sakai Hiroshi, Kitagawa Noriyuki, Ohkubo Yohei, Tsukahara Teruomi, Miyagawa Shin-ichi
First Department of Surgery, Shinshu University School of Medicine, 3-1-1 Asahi, Matsumoto, Nagano, 390-8621, Japan.
Langenbecks Arch Surg. 2014 Dec;399(8):1047-55. doi: 10.1007/s00423-014-1252-0. Epub 2014 Oct 22.
The aim of this study was to construct a prediction model for posthepatectomy liver failure (PHLF), as defined by the International Study Group of Liver Surgery, and evaluate its accuracy in hepatocellular carcinoma (HCC) patients with cirrhosis or chronic hepatitis.
A total of 277 consecutive hepatectomies for HCC between 2005 and 2013 were analyzed retrospectively. Multivariate logistic regression analysis was used to develop a predictive model for PHLF. The sensitivity, specificity, and area under the receiver operating characteristic (AUROC) curve were evaluated. The Hosmer-Lemeshow goodness-of-fit test was used to assess the model calibration. The constructed model was internally validated by k-fold cross-validation (k=5).
PHLF developed in 12.6% of hepatectomies. Multivariate analysis identified the following variables as predictors of PHLF: elevated preoperative serum bilirubin level, elevated preoperative international normalized ratio, and intraoperative packed red blood cell transfusion. The predictive model allowed discrimination between patients who developed PHLF and those who did not, with a sensitivity of 82.9%, specificity of 72.3%, and AUROC curve of 0.81 (95% CI, 0.74 to 0.89). The Hosmer-Lemeshow test indicated a good fit (P=0.545). The AUROC curve of the developed model was significantly greater than that of the model for end-stage liver disease (MELD) score (P=0.014), suggesting that the former model is better at predicting the PHLF than the latter one.
The developed model could be useful for predicting the occurrence of PHLF in HCC patients with underlying liver disease.
本研究旨在构建一个针对肝切除术后肝衰竭（PHLF）的预测模型，该模型依据国际肝脏手术研究组所定义的标准，并评估其在肝硬化或慢性肝炎的肝细胞癌（HCC）患者中的准确性。
回顾性分析了2005年至2013年间连续进行的277例HCC肝切除术。采用多因素逻辑回归分析来建立PHLF的预测模型。评估了该模型的敏感性、特异性以及受试者工作特征曲线下面积（AUROC）。使用Hosmer-Lemeshow拟合优度检验来评估模型校准情况。通过k折交叉验证（k = 5）对构建的模型进行内部验证。
12.6%的肝切除术患者发生了PHLF。多因素分析确定以下变量为PHLF的预测因素：术前血清胆红素水平升高、术前国际标准化比值升高以及术中输注浓缩红细胞。该预测模型能够区分发生PHLF的患者和未发生的患者，敏感性为82.9%，特异性为72.3%，AUROC曲线为0.81（95%可信区间，0.74至0.89）。Hosmer-Lemeshow检验显示拟合良好（P = 0.545）。所构建模型的AUROC曲线显著大于终末期肝病模型（MELD）评分的AUROC曲线（P = 0.014），表明前者在预测PHLF方面优于后者。
所构建的模型可能有助于预测患有潜在肝脏疾病的HCC患者发生PHLF的情况。
