The utility of the MELD score in predicting mortality following liver resection for metastasis

Preventing Futile Liver Resection: A Risk-Based Approach to Surgical Selection in Major Hepatectomy for Colorectal Cancer

BACKGROUND

Early recurrence following liver resection for metastatic colorectal cancer generally portends poor survival. We sought to identify factors associated with early disease recurrence after major hepatectomy for metastatic colorectal cancer in order to improve patient selection and prevent futile hepatectomy.

METHODS

Sequential major (four or more segments) liver resections performed for metastatic colorectal cancer between 1995 and 2019 were selected from our prospectively maintained database. Univariate analyses, multivariable regression modelling, and survival analyses were used to identify predictors of futile resection (recurrence within 6 months of hepatectomy).

RESULTS

Of 259 patients included, the median age was 61.3 years (interquartile range [IQR] 15.3) and the median number of liver tumors was 3.0 (IQR 2.0); 78.0% of patients received prehepatectomy chemotherapy. Surgeries were right (56.4%), left (19.3%), and extended hepatectomy (24.3%). Futile resection occurred in 26 (12.6%) patients. Margin positivity was similar in the futile resection group compared with the non-futile resection group (11.5% vs. 11.4%). Extrahepatic disease that disappeared with chemotherapy was present in 23.1% of patients with a futile resection and 7.2% of those without (p = 0.019). After multivariable regression, the factors predictive of futile resection were extrahepatic disease (odds ratio [OR] 5.6; p = 0.004), more than three liver lesions (OR 4.9; p = 0.001), and extended hepatectomy (OR 2.6; p = 0.038). Notably, 70.8% of futile recurrences occurred within the liver remnant and 20.8% were pulmonary metastases. Overall survival was 11.7 months (95% confidence interval [CI] 7.1-16.2) for the futile resection cohort versus 45.6 (95% CI 39.1-52.1) for non-futile hepatectomies (p < 0.001).

CONCLUSIONS

Futile hepatic resection can be predicted based on preoperative factors and carries a poor prognosis. Improved risk stratification for futility will aid in patient selection and treatment discussions.

A simple scoring system to estimate perioperative mortality following liver resection for primary liver malignancy-the Hepatectomy Risk Score (HeRS)

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.

Development of an Integer-based Risk Score to Predict 90-Day Mortality After Hepatectomy in Patients With Hepatocellular Carcinoma

BACKGROUND

The incidence of liver cancer has more than tripled since 1980. Hepatectomy represents the major curative treatment for liver cancer. The risk factors associated with 90-day mortality after hepatectomy are not well understood and there are currently no good prediction models for this outcome. The objectives of the current study were to identify risk factors of 90-day mortality after hepatectomy in patients with hepatocellular carcinoma and to develop an integer-based risk score using the National Cancer Database.

METHODS

Hepatectomies recorded in the National Cancer Database during 2004-2012 were reviewed for 90-day mortality. Risk factors were identified by multivariate logistic regression models. An integer-based risk score was developed using the β coefficients derived from the logistic regression model and tested for discriminatory ability. According to the total risk score, patients were grouped into 4 risk groups.

RESULTS

The overall 90-day mortality was 10.2%. Ten risk factors were identified, which included sex, age, race/ethnicity, insurance status, education, annual hospital volume, stage, tumor grade, Charlson-Deyo Score, and surgical procedure. The risk of 90-day mortality was stratified into 4 groups. The calculated 90-day mortality rates were 2.47%, 5.88%, 12.58%, and 24.67% for low-risk, medium-risk, high-risk, and excessive-risk groups, respectively. An area under the receiver operating characteristic curve of 0.69 was obtained for model discrimination.

CONCLUSIONS

The integer-based risk score we developed could easily quantify each patient's risk level and predict 90-day mortality after hepatectomy. The stratified risk score could be a useful addition to perioperative risk management and a tool to improve 90-day mortality after hepatectomy.

Cancers Metastatic to the Liver

The liver is a common site of metastatic cancer spread, and metastatic lesions are the most common malignant liver tumors. Diagnosis of liver metastases often is established based on clinical assessment, laboratory tests, and appropriate imaging. Surgical resection is the treatment of choice for resectable colorectal and neuroendocrine liver metastases. Long-term survival outcome data after treatment of hepatic metastases of noncolorectal non-neuroendocrine tumors are less robust. The treatment strategy for patients with liver metastases should be determined case by case in a multidisciplinary setting.

Comparison of score-based prediction of 90-day mortality after liver resection

BACKGROUND

Indications for liver surgery are expanding fast and complexity of procedures increases. Preoperative mortality risk assessment by scoring systems is debatable. A previously published externally validated Mortality Risk Score allowed easy applicable and precise prediction of postoperative mortality. Aim of the study was to compare the performance of the Mortality Risk Score with the standard scores MELD and P-POSSUM.

METHODS

Data of 529 patients undergoing liver resection were analysed. Mortality Risk Score, the labMELD Score and the P-POSSUM Scores (PS, OS, P-POSSUM mortality %) were calculated. The ROC curves of the three scoring systems were computed and the areas under the curve (C-index) were calculated using logistic regression models. Comparisons between the ROC curves were performed using the corresponding Wald tests.

RESULTS

Internal validation confirmed that the risk model was predictive for a 90-day mortality rate with a C-index of 0.8421. The labMELD Score had a C-index of 0.7352 and the P-POSSUM system 0.6795 (PS 0.6953, OS 0.5413). The 90-day mortality rate increased with increasing labMELD values (p < 0.0001). Categorized according to the Mortality Risk Score Groups the labMELD Score showed a linear increase while the POSSUM Scores showed variable results.

CONCLUSIONS

By accurately predicting the risk of postoperative mortality after liver surgery the Mortality Risk Score should be useful at the selection stage. Prediction can be adjusted by use of the well-established labMELD Score. In contrast, the performance of standard P-POSSUM Scores is limited.

Comparative Analysis of the Discriminatory Performance of Different Well-Known Risk Assessment Scores for Extended Hepatectomy

The aim of this study was to assess and compare the discriminatory performance of well-known risk assessment scores in predicting mortality risk after extended hepatectomy (EH). A series of 250 patients who underwent EH (≥5 segments resection) were evaluated. Aspartate aminotransferase-to-platelet ratio index (APRI), albumin to bilirubin (ALBI) grade, predictive score developed by Breitenstein et al., liver fibrosis (FIB-4) index, and Heidelberg reference lines charting were used to compute cut-off values, and the sensitivity and specificity of each risk assessment score for predicting mortality were also calculated. Major morbidity and 90-day mortality after EH increased with increasing risk scores. APRI (86%), ALBI (86%), Heidelberg score (81%), and FIB-4 index (79%) had the highest sensitivity for 90-day mortality. However, only the FIB-4 index and Heidelberg score had an acceptable specificity (70% and 65%, respectively). A two-stage risk assessment strategy (Heidelberg–FIB-4 model) with a sensitivity of 70% and a specificity 86% for 90-day mortality was proposed. There is no single specific risk assessment score for patients who undergo EH. A two-stage screening strategy using Heidelberg score and FIB-4 index was proposed to predict mortality after major liver resection.

Fat-free muscle area measured by magnetic resonance imaging predicts overall survival of patients undergoing radioembolization of colorectal cancer liver metastases

OBJECTIVES

To investigate the clinical potential of fat-free muscle area (FFMA) to predict outcome in patients with liver-predominant metastatic colorectal cancer (mCRC) undergoing radioembolization (RE) with ⁹⁰Yttrium microspheres.

METHODS

Patients with mCRC who underwent RE in our center were included in this retrospective study. All patients received liver magnetic resonance imaging including standard T2-weighted images. The total erector spinae muscle area and the intramuscular adipose tissue area were measured at the level of the origin of the superior mesenteric artery and subtracted to calculate FFMA. Cutoff values for definition of low FFMA were 3644 mm² in men and 2825 mm² in women. The main outcome was overall survival (OS). For survival analysis, the Kaplan-Meier method and Cox regressions comparing various clinic-oncological parameters which potentially may affect OS were performed.

RESULTS

Seventy-seven patients (28 female, mean age 60 ± 11 years) were analyzed. Mean time between MRI and the following RE was 17 ± 31 days. Median OS after RE was 178 days. Patients with low FFMA had significantly shortened OS compared to patients with high FFMA (median OS: 128 vs. 273 days, p = 0.017). On multivariate Cox regression analysis, OS was best predicted by FFMA (hazard ratio (HR) 2.652; p < 0.001). Baseline bilirubin (HR 1.875; p = 0.030), pattern of tumor manifestation (HR 1.679; p = 0.001), and model of endstage liver disease (MELD) score (HR 1.164; p < 0.001) were also significantly associated with OS.

CONCLUSIONS

FFMA was associated with OS in patients receiving RE for treatment of mCRC and might be a new prognostic biomarker for survival prognosis.

KEY POINTS

• Fat-free muscle area (FFMA) as a measure of lean muscle area predicts survival in metastatic colorectal liver cancer following radioembolization. • FFMA can easily be assessed from routine pre-interventional liver magnetic resonance imaging. • FFMA might be a new promising biomarker for assessment of sarcopenia.

Update on Liver Failure Following Hepatic Resection: Strategies for Prediction and Avoidance of Post-operative Liver Insufficiency

Liver resection is increasingly used for a variety of benign and malignant conditions. Despite advances in preoperative selection, surgical technique and perioperative management, posthepatectomy liver failure (PHLF) is still a leading cause of morbidity and mortality following liver resection. Given the devastating physiological consequences of PHLF and the lack of effective treatment options, identifying risk factors and preventative strategies for PHLF is paramount. In the past, a major limitation to conducting high quality research on risk factors and prevention strategies for PHLF has been the absence of a standardized definition. In this article, we describe relevant definitions for PHLF, discuss risk factors and prediction models, and review advances in liver assessment tools and PHLF prevention strategies.

Quantitative Imaging Features and Postoperative Hepatic Insufficiency: A Multi-Institutional Expanded Cohort

BACKGROUND

Post-hepatectomy liver insufficiency (PHLI) is a significant cause of morbidity and mortality after liver resection. Quantitative imaging analysis using CT scans measures variations in pixel intensity related to perfusion. A preliminary study demonstrated a correlation between quantitative imaging features of the future liver remnant (FLR) parenchyma from preoperative CT scans and PHLI. The objective of this study was to explore the potential application of quantitative imaging analysis in PHLI in an expanded, multi-institutional cohort.

STUDY DESIGN

We retrospectively identified patients from 5 high-volume academic centers who developed PHLI after major hepatectomy, and matched them to control patients without PHLI (by extent of resection, preoperative chemotherapy treatment, age [±5 years], and sex). Quantitative imaging features were extracted from the FLR in the preoperative CT scan, and the most discriminatory features were identified using conditional logistic regression. Percent remnant liver volume (RLV) was defined as follows: (FLR volume)/(total liver volume) × 100. Significant clinical and imaging features were combined in a multivariate analysis using conditional logistic regression.

RESULTS

From 2000 to 2015, 74 patients with PHLI and 74 matched controls were identified. The most common indications for surgery were colorectal liver metastases (53%), hepatocellular carcinoma (37%), and cholangiocarcinoma (9%). Two CT imaging features (FD1_4: image complexity; ACM1_10: spatial distribution of pixel intensity) were strongly associated with PHLI and remained associated with PHLI on multivariate analysis (p = 0.018 and p = 0.023, respectively), independent of clinical variables, including preoperative bilirubin and %RLV.

CONCLUSIONS

Quantitative imaging features are independently associated with PHLI and are a promising preoperative risk stratification tool.

Primary Malignancy is an Independent Determinant of Morbidity and Mortality after Liver Resection

Although outcomes after liver resection have improved, there remains considerable perioperative morbidity and mortality with these procedures. Studies suggest a primary liver cancer diagnosis is associated with poorer outcomes, but the extent to which this is attributable to a higher degree of hepatic dysfunction is unclear. To better delineate this, we performed a matched pair analysis of primary versus metastatic malignancies using a national database. The American College of Surgeons National Surgical Quality Improvement Program (2005–2013) was analyzed to select elective liver resections. Diagnoses were sorted as follows: 1) primary liver cancers and 2) metastatic neoplasms. A literature review identified factors known to impact hepatectomy outcomes; these variables were evaluated by a univariate analysis. The most predictive factors were used to create similar groups from each diagnosis category via propensity matching. Multivariate regression was used to validate results in the wider study population. Outcomes were compared using chi-squared test and Fisher exact test. Matched groups of 4838 patients were similar by all variables, including indicators of liver function. A number of major complications were significantly more prevalent with a primary diagnosis; overall major morbidity rates in the metastatic and primary groups were 29.3 versus 41.6 per cent, respectively. The mortality rate for primary neoplasms was 4.6 per cent (vs 1.6%); this represents a risk of death nearly three-times greater (95% confidence interval = 2.20–3.81, P < 0.0001) in cancers of hepatic origin. Hepatectomy carries substantially higher perioperative risk when performed for primary liver cancers, independent of hepatic function and resection extent. This knowledge will help to improve treatment planning, patient education, and resource allocation in oncologic liver resection.