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Derivation of a risk index for the prediction of massive blood transfusion in liver transplantation

✍ Scribed by Stuart A. McCluskey; Keyvan Karkouti; Duminda N. Wijeysundera; Karen Kakizawa; Mohammed Ghannam; Ahmed Hamdy; David Grant; Gary Levy


Publisher
John Wiley and Sons
Year
2006
Tongue
English
Weight
172 KB
Volume
12
Category
Article
ISSN
1527-6465

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✦ Synopsis


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 (2.0), platelet count (/=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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