• Chaiyut Thanapaisal and Puthipong Saksaen.
• Department of Surgery, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand. chaiyut06@yahoo.com
• J Med Assoc Thai. 2012 Nov 1;95 Suppl 11:S25-33.

ObjectiveTo assess the ability of the Acute Physiology and Chronic Health Evaluation (APACHE II) system and Trauma-Injury Severity Scoring (TRISS) system in predicting group mortality in intensive care unit (ICU) trauma patients.Material And MethodThe trauma patients admitted to ICU at Srinagarind Hospital between June 2008 and December 2010 were studied. For each patient, demographic data, mechanism of injury and surgical status were collected. The probability of death was calculated for each patient based on the APACHE II and TRISS equations. The ability to predict group mortality for APACHE II and TRISS was assessed by receiver operating characteristic curve analysis, two by two decision matrices and calibration curve analysis.ResultsOne hundred and thirty-two trauma patients were admitted to the ICU. Twenty-seven (20%) patients died and hundred and five (80%) survived. There were significant differences between survivors and non-survivors in Glasgow Coma Scale, Revised Trauma Score, Injury Severity Score and APACHE II score. By receiver operating characteristic (ROC) curve analysis, the areas under the curves (+/- SEM) of APACHE II and TRISS were 0.89 +/- 0.04 and 0.83 +/- 0.04, respectively. Using two by two decision matrices with a decision criterion of 0.5, the sensitivities, specificities and percentages correctly classified were 44.4%, 98.1% and 87.1%, respectively for APACHE II and 25.9%, 98.1% and 83.3%, respectively, for TRISS. From the calibration curves, the r2 value was 0.99 (p = 0.0001) for APACHE II and 0.98 (p = 0.0001) for TRISS.ConclusionBoth APACHE II and TRISS scores were shown to accurately predict group mortality in ICU trauma patients. APACHE II and TRISS may be utilized for quality assurance in ICU trauma patients. However, neither APACHE II nor TRISS provides sufficient confidence for prediction of outcome of individual patients.

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