Moris Dimitrios, Shaw Brian I, Ong Cecilia, Connor Ashton, Samoylova Mariya L, Kesseli Samuel J, Abraham Nader, Gloria Jared, Schmitz Robin, Fitch Zachary W, Clary Bryan M, Barbas Andrew S
Department of Surgery, Duke University Medical Center, Durham, NC, USA.
Hepatobiliary Surg Nutr. 2021 Jun;10(3):315-324. doi: 10.21037/hbsn.2020.03.12.
Selection of the optimal treatment modality for primary liver cancers remains complex, balancing patient condition, liver function, and extent of disease. In individuals with preserved liver function, liver resection remains the primary approach for treatment with curative intent but may be associated with significant mortality. The purpose of this study was to establish a simple scoring system based on Model for End-stage Liver Disease (MELD) and extent of resection to guide risk assessment for liver resections.
The 2005-2015 NSQIP database was queried for patients undergoing liver resection for primary liver malignancy. We first developed a model that incorporated the extent of resection (1 point for major hepatectomy) and a MELD-Na score category of low (MELD-Na =6, 1 point), medium (MELD-Na =7-10, 2 points) or high (MELD-Na >10, 3 points) with a score range of 1-4, called the Hepatic Resection Risk Score (HeRS). We tested the predictive value of this model on the dataset using logistic regression. We next developed an optimal multivariable model using backwards sequential selection of variables under logistic regression. We performed K-fold cross validation on both models. Receiver operating characteristics were plotted and the optimal sensitivity and specificity for each model were calculated to obtain positive and negative predictive values.
A total of 4,510 patients were included. HeRS was associated with increased odds of 30-day mortality [HeRS =2: OR =3.23 (1.16-8.99), P=0.025; HeRS =3: OR =6.54 (2.39-17.90), P<0.001; HeRS =4: OR =13.69 (4.90-38.22), P<0.001]. The AUC for this model was 0.66. The AUC for the optimal multivariable model was higher at 0.76. Under K-fold cross validation, the positive predictive value (PPV) and negative predictive value (NPV) of these two models were similar at PPV =6.4% and NPV =97.7% for the HeRS only model and PPV =8.4% and NPV =98.1% for the optimal multivariable model.
The HeRS offers a simple heuristic for estimating 30-day mortality after resection of primary liver malignancy. More complicated models offer better performance but at the expense of being more difficult to integrate into clinical practice.
对于原发性肝癌，选择最佳治疗方式仍然很复杂，需要平衡患者状况、肝功能和疾病范围。在肝功能良好的个体中，肝切除仍然是根治性治疗的主要方法，但可能伴随着显著的死亡率。本研究的目的是建立一个基于终末期肝病模型（MELD）和切除范围的简单评分系统，以指导肝切除的风险评估。
查询2005 - 2015年NSQIP数据库中因原发性肝恶性肿瘤接受肝切除的患者。我们首先开发了一个模型，该模型纳入了切除范围（肝大部切除术为1分）以及MELD - Na评分类别，低（MELD - Na = 6，1分）、中（MELD - Na = 7 - 10，2分）或高（MELD - Na > 10，3分），评分范围为1 - 4分，称为肝切除风险评分（HeRS）。我们使用逻辑回归在数据集中测试了该模型的预测价值。接下来，我们在逻辑回归下使用变量的向后顺序选择开发了一个最佳多变量模型。我们对这两个模型都进行了K折交叉验证。绘制了受试者工作特征曲线，并计算了每个模型的最佳敏感性和特异性，以获得阳性和阴性预测值。
共纳入4510例患者。HeRS与30天死亡率增加相关[HeRS = 2：OR = 3.23（1.16 - 8.99），P = 0.025；HeRS = 3：OR = 6.54（2.39 - 17.90），P < 0.001；HeRS = 4：OR = 13.69（4.90 - 38.22），P < 0.001]。该模型的AUC为0.66。最佳多变量模型的AUC更高，为0.76。在K折交叉验证下，这两个模型的阳性预测值（PPV）和阴性预测值（NPV）相似，HeRS单模型的PPV = 6.4%，NPV = 97.7%，最佳多变量模型的PPV = 8.4%，NPV = 98.1%。
HeRS为估计原发性肝恶性肿瘤切除术后30天死亡率提供了一种简单的启发式方法。更复杂的模型表现更好，但代价是更难应用于临床实践。
