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doi: 10.1097/TP.0b013e3181aed477
Clinical and Translational Research

Independent Validation of a Model Predicting the Need for Packed Red Blood Cell Transfusion at Liver Transplantation

Massicotte, Luc1,4; Capitanio, Umberto2; Beaulieu, Danielle1; Roy, Jean-Denis1; Roy, André3; Karakiewicz, Pierre I.2

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Objectives. Orthotopic liver transplantation (OLT) may be associated with major blood loss and equally considerable transfusion requirements. We had developed previously a model capable of predicting the probability of packed red blood cell (PRBC) transfusion. We tested the ability of that model in predicting the need for PRBC transfusion after its conversion into the nomogram format, which represents a friendly tool to be used. Moreover, the nomogram was validated in an independent cohort of 109 prospectively gathered OLTs.

Materials and Methods. A total of 515 OLTs were performed by a group of 17 anesthesiologists and 7 hepatobiliary surgeons. The initial series of 406 OLTs were used for model development. The remaining 109 OLTs were used as an independent validation cohort. Logistic regression analyses addressed the relationship between the three previously identified predictors of the likelihood of PRBC transfusion and the actual rate of PRBC transfusion. The predictors consisted of plasma transfusion status, phlebotomy, and immediate preoperative hemoglobin value. The regression coefficients from the multivariable logistic regression model that included all three predictors were used to develop a nomogram predicting the individual probability of PRBC transfusion.

Results. In univariable models, transfusion of plasma (odds ratio [OR] 15.0, P<0.001) increased the rate of PRBC transfusion. Conversely, phlebotomy (OR 0.06, P<0.001) and a high starting hemoglobin level (OR 0.95, P<0.001) had a protective effect. In the multivariable model, all three variables reached independent predictor status (P<0.001). The bootstrap-adjusted area under curve (AUC) of the model was 89.8%.

Conclusion. Our nomogram represents the first model capable of predicting the individual risk of PRBC transfusion at OLT.

© 2009 Lippincott Williams & Wilkins, Inc.



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