Related Experiment Video
Updated: Nov 1, 2025
Laparoscopic Anterior Right Hepatectomy: A Single-Center Experience
Published on: December 4, 2023
A simple scoring system to estimate perioperative mortality following liver resection for primary liver
Dimitrios Moris1, Brian I Shaw1, Cecilia Ong1
1Department of Surgery, Duke University Medical Center, Durham, NC, USA.
Background:
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.
Methods:
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.
Results:
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.
Conclusions:
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.
