Abstract

IntroductionThe aim of this retrospective study is to analyse the risk factors of mortality in cirrhotic patients with blunt abdominal trauma (BAT) underwent laparotomy and the value of the Model for End-Stage Liver Disease (MELD) score to predict postoperative death is determined. Materials and methodsFrom July 1993 to June 2005, 34 cirrhotic patients with BAT were reviewed. Data are presented as mean±standard deviation (SD), frequency (percentage), or Pearson correlation coefficient. Predictors were compared by uni- and multiple logistic regression analysis and results were considered statistically significant if P<0.05. The prognostic value of the MELD score in predicting postoperative death was assessed using receiver operating characteristic (ROC) curve analysis. ResultsOf the 34 patients (27 men, 7 women; mean age, 49 years), the Injury Severity Score (ISS) ranged from 4 to 43 (mean: 14). Of the 34 patients, 12 were treated with nonoperative management (NOM) initially and 4 succeeded and 30 patients (88.2%) eventually required laparotomy. Of the 30 operative patients, 7 died of haemorrhagic shock and the other 6 died of multiple organ failure with a 43.3% mortality rate. Of the 17 survivors after laparotomy, 4 developed intra-abdominal complication, and 3 developed extra-abdominal complication with a 41.2% morbidity rate. On univariate analysis, the significant predictors of surgical mortality were shock at emergency department, damage control laparotomy, ISS and MELD score. On multiple logistic regression analysis, the significant predictors of operative mortality were shock at ED (P=0.021) and MELD score (P=0.012). Analysis by ROC curve identified cirrhotic patients with a MELD score equal to or above 17 as the best cut-off value for predicting postoperative death. ConclusionsLiver cirrhosis with BAT has a high operative rate, low salvage rate of NOM, high surgical mortality and morbidity rate. The MELD score can accurately predict postoperative death and a MELD score equal to or above 17 of our data is at high risk of postoperative death.

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