Prognostic Evaluation of Patients Undergoing Living-Donor Liver Transplant By APACHE II and MELD Scores

摘要

Objectives: We hypothesized that the combination of APACHE II and Model for End-Stage Liver Disease systems would work satisfactorily in patients admitted to intensive care unit after living-donor liver transplant. Materials and Methods: Data were retrospectively collected from the database of our surgical team. The study included 38 patients (hepatitis B virus cirrhosis, 47.4%; hepatocellular carcinoma, 28.9%; other diseases, 23.7%). Laboratory values were obtained. Vital signs, Glasgow Coma scale scores, and urine output were abstracted. Variables included age, sex, acute physiology score, APACHE II score, APACHE II-predicted intensive care unit and hospital mortality, predicted length of intensive care unit, and hospital stay. Patients' actual length of intensive care unit and hospital stays, intensive care unit and hospital discharge status, and discharge location were recorded. Standardized mortality ratios were calculated. Discrimination and calibration of APACHE II were assessed. All patients were divided into 3 groups: Model for End-Stage Liver Disease score: >25, 18 to 25, and <18. Predicted hospital mortality was calculated and compared. Results: Mean APACHE II scores of survivors and nonsurvivors were 13.03 and 23.67. Mean risk of death was 7.05% and 25.07%. APACHE II scores and risk of death between survivors and nonsurvivors was significantly different ( P < .001). The cutoff value of APACHE II score and Model for End-Stage Liver Disease score in the receiving operating characteristic curve was 20 and 25. Patients with APACHE II scores greater than 20 or Model for End-Stage Liver Disease scores greater than 25 had higher predicted hospital mortality after living-donor liver transplant. Conclusions: The modified APACHE II model provides an accurate prognosis of patients receiving a living-donor liver transplant. The combined application of Model for End-Stage Liver Disease score and APACHE II score can improve the predictive accuracy.