• Rebecca A Schroeder, Carlos E Marroquin, Barbara Phillips Bute, Shukri Khuri, William G Henderson, and Paul C Kuo.
• Department of Anesthesiology, Durham Veterans Medical Center, Duke University School of Medicine, Durham, NC 27705, USA. Schro016@mc.duke.edu
• Ann. Surg. 2006 Mar 1;243(3):373-9.

ObjectiveTo determine if use of Model for End-Stage Liver Disease (MELD) scores to elective resections accurately predicts short-term morbidity or mortality.Summary Background DataMELD scores have been validated in the setting of end-stage liver disease for patients awaiting transplantation or undergoing transvenous intrahepatic portosystemic shunt procedures. Its use in predicting outcomes after elective hepatic resection has not been evaluated.MethodsRecords of 587 patients who underwent elective hepatic resection and were included in the National Surgical Quality Improvement Program Database were reviewed. MELD score, CTP score, Charlson Index of Comorbidity, American Society of Anesthesiology classification, and age were evaluated for their ability to predict short-term morbidity and mortality. Morbidity was defined as the development of one or more of the following complications: pulmonary edema or embolism, myocardial infarction, stroke, renal failure or insufficiency, pneumonia, deep venous thrombosis, bleeding, deep wound infection, reoperation, or hyperbilirubinemia. The analysis was repeated with patients divided according to their procedure and their primary diagnosis. Parametric or nonparametric analyses were performed as appropriate. Also, a new index was developed by dividing the patients into a development and a validation cohort, to predict morbidity and mortality in patients undergoing elective hepatic resection. ROC curves were also constructed for each of the primary indices.ResultsCTP and ASA scores were superior in predicting outcome. Also, patients undergoing resection of primary malignancies had a higher rate of mortality but no difference in morbidity.ConclusionMELD scores should not be used to predict outcomes in the setting of elective hepatic resection.

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