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  • Title: Derivation of a risk index for the prediction of massive blood transfusion in liver transplantation.
    Author: McCluskey SA, Karkouti K, Wijeysundera DN, Kakizawa K, Ghannam M, Hamdy A, Grant D, Levy G.
    Journal: Liver Transpl; 2006 Nov; 12(11):1584-93. PubMed ID: 16952177.
    Abstract:
    Massive blood transfusion (MBT) remains a serious and common occurrence in liver transplantation surgery. This retrospective cohort study was undertaken to identify preoperative predictors of MBT and to develop a risk index for MBT in liver transplantation. Data were retrospectively collected on all liver transplantations carried out at a single institution between January 1998 and March 2004. Multivariable logistic regression analysis was used to identify independent predictor variables of MBT, defined as >/=6 units of red blood cell concentrate (RBC) in the first 24 hours of surgery. The model was internally validated by bootstrapping. Of the 460 liver transplant recipients, 193 (42%) received >/=6 units of RBC within 24 hours of surgery. Unadjusted analyses identified 12 preoperative predictors of MBT: age, height, gender, repeat transplantation, etiology of liver failure, and preoperative laboratory values (hemoglobin concentration, platelet count, international normalized ratio for prothrombin activity [INR], albumin, total bilirubin, and creatinine). In multivariable logistic regression, 7 independent predictors of MBT were identified: age (>40 years), hemoglobin concentration (</=10.0 g/dL), INR (1.2-1.99, and >2.0), platelet count (</=70 x 10(9)/L), creatinine (>/=110 micromol/L for female subjects and >/=120 micromol/L for male subjects), albumin (< 28 g/L), and repeat transplantation. The area under the receiver-operating characteristic curve (ROC) for the model was 0.82. By using the regression beta coefficients to derive weights for each of these predictors, a risk index was developed that assigned each patient a score between 0 and 8. The ROC for this risk index was 0.79. MBT in liver transplantation surgery can be accurately predicted by 7 readily available preoperative predictors.
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