OBJECTIVE: To compare 4 general severity classification scoring systems concerning prognosis of outcome in 123 liver transplant recipients. The compared scoring systems were: the mortality prediction model (admission model and 24 h model); the simplified acute physiology score; the acute physiology and chronic health evaluation (Apache II) and the acute organ systems failure score. DESIGN: Retrospective, consecutive sample. SETTING: Adult intensive care unit in a university hospital. PATIENTS: 123 adult liver allograft recipients after admission to the intensive care unit. MEASUREMENTS AND MAIN RESULTS: The scoring systems were calculated as described by the authors to classify the severity of illness after admission of the allograft recipients to the intensive care unit. The mean and median values of survivors and the group of patients, that died during hospital stay were compared. Receiver-operating characteristics were plotted for all scoring systems and the areas under the curves of receiver-operating characteristics were calculated. The predictive value of the 4 scoring systems was tested using a variety of sensitivity analyses. The mortality prediction model (24 h model) was found to have a high significance (p < 0.001) in predicting mortality and showed the greatest area under the curve (0.829). Simplified acute physiology score (p < 0.001) and acute physiology and chronic health evaluation (Apache II) (p < 0.01) had a high significance as well, but did not hit the level of prognosis of mortality prediction model, as shown in the area under the curves. Accordingly, sensitivity was highest in MPM-24 h (83%), followed by SAPS (72%) and Apache II (71%). MPM-24 h had a total misclassification rate of 22% (SAPS = 32%, Apache II = 33%). MPM-admission failed in predicting mortality (sensitivity = 52%). Organ systems failure score seemed not to be useful in liver transplant recipients. CONCLUSION: General disease classification systems, such as the mortality prediction model, simplified acute physiology score or acute physiology and chronic health evaluation are good mortality prediction models in patients after liver transplantation. We suggest that there is no need for improvement of a special scoring system.
OBJECTIVE: To compare 4 general severity classification scoring systems concerning prognosis of outcome in 123 liver transplant recipients. The compared scoring systems were: the mortality prediction model (admission model and 24 h model); the simplified acute physiology score; the acute physiology and chronic health evaluation (Apache II) and the acute organ systems failure score. DESIGN: Retrospective, consecutive sample. SETTING: Adult intensive care unit in a university hospital. PATIENTS: 123 adult liver allograft recipients after admission to the intensive care unit. MEASUREMENTS AND MAIN RESULTS: The scoring systems were calculated as described by the authors to classify the severity of illness after admission of the allograft recipients to the intensive care unit. The mean and median values of survivors and the group of patients, that died during hospital stay were compared. Receiver-operating characteristics were plotted for all scoring systems and the areas under the curves of receiver-operating characteristics were calculated. The predictive value of the 4 scoring systems was tested using a variety of sensitivity analyses. The mortality prediction model (24 h model) was found to have a high significance (p < 0.001) in predicting mortality and showed the greatest area under the curve (0.829). Simplified acute physiology score (p < 0.001) and acute physiology and chronic health evaluation (Apache II) (p < 0.01) had a high significance as well, but did not hit the level of prognosis of mortality prediction model, as shown in the area under the curves. Accordingly, sensitivity was highest in MPM-24 h (83%), followed by SAPS (72%) and Apache II (71%). MPM-24 h had a total misclassification rate of 22% (SAPS = 32%, Apache II = 33%). MPM-admission failed in predicting mortality (sensitivity = 52%). Organ systems failure score seemed not to be useful in liver transplant recipients. CONCLUSION: General disease classification systems, such as the mortality prediction model, simplified acute physiology score or acute physiology and chronic health evaluation are good mortality prediction models in patients after liver transplantation. We suggest that there is no need for improvement of a special scoring system.
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