Risk assessment in patients undergoing liver resection

Nanjing, China

Risk assessment in patients undergoing liver resection

De-Cai Yu, Wei-Bo Chen, Chun-Ping Jiang and Yi-Tao Ding

Nanjing, China

BACKGROUND:Liver resection is still a risky procedure with high morbidity and mortality. It is signif i cant to predict the morbidity and mortality with some models after liver resection.

DATA SOURCES:The MEDLINE/PubMed, Web of Science, Google Scholar, and Cochrane Library databases were searched using the terms "hepatectomy" and "risk assessment" for relevant studies before August 2012. Papers published in English were included.

RESULTS:Thirty-four original papers were included fi nally. Some models, such as MELD, APACHE II, E-PASS, or POSSUM, widely used in other populations, are useful to predict the morbidity and mortality after liver resection. Some special models for liver resection are used to predict outcomes after liver resection, such as mortality, liver dysfunction, transfusion, or acute renal failure. However, there is no good scoring system to predict or classify surgical complications because of shortage of internal or external validation.

CONCLUSION:It is important to validate the models for the major complications after liver resection with further internal or external databases.

(Hepatobiliary Pancreat Dis Int 2013;12:473-479)

hepatectomy;risk factors; risk assessment; mortality; morbidity

Introduction

With advances in diagnostic, anesthetic and surgical techniques, liver resection remains important in the multidisciplinary approach for liver neoplasm.[1,2]However, liver resection is still a risky procedure with high morbidity and mortality. In the different populations, the rate of 30-day major complications ranges from 13.9% to 43.0% with a median of 22.5%, and the mortality rate ranges from 1.9% to 6.0% with a median of 3.0%.[2-10]The characteristic complications included liver insuff i ciency, biliary leakage, ascites, and age-related complications. The incidence of bile leakage, as one of the most representative complications after liver resection, remains constant (mean 5.5%), ranging from 4.8% to 7.6% in the past three decades, although the total mortality or morbidity is decreasing gradually.[11-18]Therefore, predicting the occurrence of these complications may be very useful in optimization of clinical practice.

As mentioned above, it is very important to stratify accurately the risk for the patients undergoing liver resection, and then provide the corresponding perioperative management strategies for these patients. Although the risk factors mentioned above have been identif i ed, it is challenging to determine the risk factors for the outcomes after liver resection. In fact, surgeons mainly rely on either subjective assessment to predict how well the patient will fare postoperatively, which hinders effective communications due to their subjective nature. Although there are more and more risk scoring models for major complications after liver resection, they are not applied widely because of lack of good external validation. Therefore, all risk scoring systems for outcomes after liver resection were reviewed and discussed.

Study selection and data collection

The MEDLINE/PubMed, Web of Science, Google Scholar, and Cochrane Library databases were searchedfor relevant studies published before August 2012 using such terms as "hepatectomy" and "risk assessment". The data included cohort characteristics, populations with general information, general conditions, specif i c conditions, risk factors, models, outcomes, and quality review elements. We also searched online for available tools or the model and documented their locations. Only papers published in English language were included. The papers included randomized controlled trials, controlled clinical trials, cohort studies, case control studies, and non-randomized case series in the fi eld. Liver resections associated with extrahepatic biliary resection, reconstruction or intraoperative bile duct exploration were excluded from the study. The patients who had undergone other procedures such as hepatectomy, pancreatectomy or colectomy were also excluded from the study. In addition, reviews or systematic reviews, case reports, and consensus statements of experts were also excluded. At last, 34 original papers were included.

Models predicting different outcomes after liver resection

MELD predicting liver failure, mortality or long-term survival after liver resection

Model for end-stage liver disease (MELD) is considered as the gold standard for the selection of candidates with liver transplantation.[19]MELD can predict independently the early outcomes of patients after liver resection and the Child-Turcotte-Pugh (CTP) score. In patients with MELD scores <9, 9-10, and >10, the irreversible postoperative liver failure (IPLF) rates were 0.4%, 3.8%, and 20.3% respectively. Moreover, serum sodium level identif i ed lowrisk patients (sodium ≥140 mEq/L) without IPLF.[20]

Besides, preoperative MELD score, an increase of MELD score between post-operative days (PODs) 3 and 5, is also considered as an independent predictor of IPLF, which alerts physicians to prescribe an intensive treatment or salvage transplantation.[21]In addition to liver failure, MELD also accurately predicts other morbidities,[22]perioperative mortality, and long-term survival in patients with cirrhosis undergoing hepatic resection for hepatocellular carcinoma (HCC).[23]However, Schroeder et al[24]found that CTP as well as physical scores of the American Society of Anesthesiologists (ASA) were superior to MELD in predicting outcomes of patients after elective hepatic resection. Especially in patients without cirrhosis, perioperative MELD score fails to predict perioperative outcomes after liver resection for HCC.[25]Hence, other predictive parameters are needed for this group of patients.

Therefore, patients with CTP class A cirrhosis or MELD scores of ≤8 have a low mortality or morbidity after liver resection, whereas those with CTP class C cirrhosis or MELD scores >14 are not suitable for surgical interventions.[3]If MELD score is ≥9, other treatment modalities should be considered.[26]Other biochemical measures such as ICG R15 clearance and monoethylglycinxylid (MEGX) are also used to predict outcomes of patients after liver resection.[27]

General systematic scorings for predicting mortality or morbidity

With the increase of preoperative APACHE II score, the postoperative morbidity and mortality are increased signif i cantly. The patients with low, middle, and high scores had a postoperative morbidity of 34%, 54%, and 80%, and a postoperative mortality of 0, 3% and 20%, respectively, which suggest that APACHE II score can be used to evaluate the risk of postoperative morbidity and mortality after elective liver surgery.[28]

Estimation of physiologic ability and surgical stress (E-PASS) is also applied to predict the morbidity and mortality of patients after elective gastrointestinal surgery. The E-PASS score appears to effectively predict the mortality of patients after liver resection, but it is a poor predictor for complications because E-PASS score is not signif i cantly different in patients with or without complications.[29]Another study showed that E-PASS could predict postoperative liver failure and in-hospital mortality.[30]

In addition, physiologic and operative severity score for the enumeration of mortality and morbidity (POSSUM) can predict the outcomes of patients after liver resection for HCC.[31]Preoperative risk score (PRS) is validated as a signif i cant determinant of systemic complications in elderly patients with HCC.[32]Moreover, the inf l ammation-based Glasgow prognostic score (GPS) has been used to predict postoperative complications and cancer-specif i c survival for a variety of cancers.[33,34]Multivariate analysis revealed that GPS is an independent risk factor for pulmonary complications, and for blood transfusion during elective liver resection of HCC.[35]

Outcomes predicted with different models

Mortality predicted with different models

The results from American National Inpatients Samples from 1998 to 2005 showed an overall in-hospital mortality of 6.52%.[36]Obviously, liver resection is still a procedure that is associated with a high mortality. Accurate risk stratif i cation of perioperative risk of patients would helpto make any clinical decision.

Simons and colleagues[36]developed a simple risk score system to estimate the risk of in-hospital death after resection of HCC. They combined age, gender, Charlson comorbidity score, procedure type, and teaching hospital status. The total possible score was 22. They graded the patients into 4 groups: low, scores from 0 to 4; low to moderate, scores from 5 to 9; moderate to high, scores from 10 to 14; and high, scores from 15 to 22. The estimated mortality was 1.48%, 3.98%, 10.33%, and 28.30% respectively; the predictive mortality estimated by this scoring system is signif i cantly correlated with the observed mortality.

Patients with MELD score >8 had a higher mortality (4.0% vs 0.6%,P=0.004).[6]The 50-50 criteria (16.45; 3.50-77.25) and post-hepatectomy liver failure (PHLF) (13.80; 4.27-44.61) were identif i ed as powerful, independent predictors of mortality.[5]APACHE II score may be considered to evaluate the risk of postoperative morbidity and mortality before elective liver surgery.[28]In addition, some general scoring systems such as E-PASS, mEPASS, P-POSSUM, and CLD could effectively predict the mortality of patients after liver resection.[29,30,39]

Liver dysfunction predicted with some models

Major liver resection is the important procedure for the treatment of liver neoplasm. Whether a major liver resection is safe or not mainly depends on the hepatic reserve of patients, especially those with hepatitis or cirrhosis. However, no effective tool or scoring system is available for the evaluation of hepatic reserve.

For the evaluation of liver function, ICG R15, MEGX, MELD, and PHLF are good parameters.[5]Moreover, glucose metabolism is an important liver function. Thus impaired glucose tolerance is another index for the evaluation of liver function. In patients with low insulinogenic index (II) and low redox tolerance index (RTI), the mortality after liver resection reached 33.3%, but when it was positive the mortality was only 3.2%. Therefore, the preoperative evaluation of surgical risk by Z score with II and RTI seems to be useful in predicting the prognosis of patients after liver resection.[40]

Reserved liver volume is another parameter for liver function after liver resection. Therefore, most of models are based on hepatic metabolic indices and reserved liver volume. In combination with gammaglutamyltranspeptidase, and prothrombin activity, the partial hepatic resection rate (PHRR) is an independent parameter for predicting liver failure.[41]Combined with preoperative serum HA concentrations, the estimated remnant percentage of total liver volume could be useful in predicting postoperative complications after a major liver resection of HCC.[42]Together with preoperative K (ICG R15) value, standard remnant liver volume appears to be a good predictor for postoperative liver dysfunction in patients who undergo a liver resection of HCC.[43]

Models for transfusion

As one of the most hemorrhagic procedures, liver resection remains the high risk in abdominal surgery. Blood loss is generally associated with major complications and prognosis of patients undergoing liver resection.[44]It is signif i cant to identify patients preoperatively who are at a high-risk of massive intraoperative blood loss. Intraoperative blood loss of more than 1500 mL is associated independently with a prothrombin activity <70%, the non-peripheral location of the tumor, the involvement of hepatic veins, body mass index ≥23.0 kg/m2, and major liver resection. This predictive scoring system is useful to screen patients with possible massive intraoperative blood loss.[45]In addition, operative time and tumor size are also considered to be independent risk factors for blood loss.[46]

To some degree, blood transfusion is proportional to blood loss. Besides the risk factors for blood loss, preoperative biliary drainage, coronary artery disease, preoperative hemoglobin and platelets levels predicted the probability of perioperative red blood cell transfusion.[47,48]Glasgow prognostic score is also associated with blood transfusion during elective resection of HCC.[35]

Acute renal failure (ARF) predicted with some models

In patients with liver cirrhosis, some syndromes such as hepatic renal syndrome, hepatoencephalopathy, occur. Longer operative time and blood loss affect renal function. A study[49]found that postoperative ARF occurred in 15.1% of patients, and ARF was highly associated with mortality. With regard to prediction score, preoperatively elevated ALT, pre-existing cardiovascular disease, chronic renal failure, and diabetes were the strongest predictors of ARF. With these predictors, the newly assembled prediction score (either full or the reduced models) could accurately predict postoperative ARF after liver resection. This study thus suggests that these scores can be used for early identif i cation of patients at high risk ARF and for decision-making of protective kidney intervention.[49]

Summary

All models predicting the outcomes of patients after liver resection are summarized in Table. It is noteworthy that there was no uniformity in the presentationand def i nition of complications among the studies. Furthermore, the appropriate format for a scoring system to classify surgical complications is still under debate. Therefore, it is important to validate the models for the major complications after liver resection with internal or external database.

Table.Models predicting outcomes after liver resection (Listed in alphabetical order of models)

M&M: morbidity and mortality.

Acknowledgements:We are grateful to Professors Prabhakar K Baliga, Kenneth D Chavin, and Tuo-Yu Geng at the Medical University of South Carolina (MUSC) for revising the manuscript.Contributors:YDC participated in study design, paper review, data collection and analysis, and wrote the paper. JCP participated in paper search and review, and wrote the paper. CWB participated in paper search and review. DYT conceived the study, participated in its design, and gave fi nal approval of the version to be published. All authors have read and approved the fi nal manuscript. DYT is the guarantor.

Funding:This study was supported by the Grants for Key Medical Department in Jiangsu Province, Outstanding Medical Researchers in Jiangsu Province, and the National Natural Science Foundation of China (81201621).

Ethical approval:Not needed.

Competing interest:No benef i ts in any form have been received or will be received from a commercial party related directly or indirectly to the subject of this article.

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Received October 9, 2012

Accepted after revision March 31, 2013

AuthorAff i liations:Department of Hepatobiliary Surgery, Drum Tower Hospital, the Aff i liated Medical School of Nanjing University (Yu DC, Jiang CP and Ding YT); Institute of Hepatobiliary Surgery, Nanjing University (Yu DC, Chen WB, Jiang CP and Ding YT), Nanjing 210008, China

Yi-Tao Ding, MD, Department of Hepatobiliary Surgery, Drum Tower Hospital, the Aff i liated Medical School of Nanjing University; Institute of Hepatobiliary Surgery, Nanjing University, Nanjing 210008, China (Tel: 86-25-83304616; Fax: 86-25-83317016; Email: drdingyitao @sina.com)

© 2013, Hepatobiliary Pancreat Dis Int. All rights reserved.

10.1016/S1499-3872(13)60075-2