• Rev Bras Cir Cardiovasc · Oct 2011

    Predictors of transfusion of packed red blood cells in coronary artery bypass grafting surgery.

    • Michel Pompeu Barros de Oliveira Sá, Evelyn Figueira Soares, Cecília Andrade Santos, Omar Jacobina Figueiredo, Renato Oliveira Albuquerque Lima, Fábio Gonçalves de Rueda, Rodrigo Renda de Escobar, Alexandre Magno Macário Nunes Soares, and Ricardo de Carvalho Lima.
    • Division of Cardiovascular Surgery of Pronto Socorro Cardiológico de Pernambuco – PROCAPE. University of Pernambuco – UPE, Brazil. michel_pompeu@yahoo.com.br
    • Rev Bras Cir Cardiovasc. 2011 Oct 1;26(4):552-8.

    ObjectivesFinding predictors of blood transfusion may facilitate the most efficient approach for the use of blood bank services in coronary artery bypass grafting procedures. The aim of this retrospective study is to identify preoperative and intraoperative patient characteristics predicting the need for blood transfusion during or after CABG in our local cardiac surgical service.Methods435 patients undergoing isolated first-time CABG were reviewed for their preoperative and intraoperative variables and analyzed postoperative data. Patients were 255 males and 180 females, with mean age 62.01 ± 10.13 years. Regression logistic analysis was used for identifying the strongest perioperative predictors of blood transfusion.ResultsBlood transfusion was used in 263 patients (60.5%). The mean number of transfused blood products units per patient was 2.27 ± 3.07 (0-23) units. The total number of transfused units of blood products was 983. Univariate analysis identified age >65 years, weight <70 Kg, body mass index <25 Kg/m2, hemoglobin <13mg/dL, hematocrit < 40% and ejection fraction <50%, use of cardiopulmonary bypass (CPB), not using an internal thoracic artery as a bypass, and multiple bypasses as significant predictors. The strongest predictors using multivariate analysis were hematocrit < 40% (OR 2.58; CI 1.62-4.15; P<0.001), CPB use (OR 2.00; CI 1.27-3.17; P=0.003) and multiple bypasses (OR 2.31; CI 1.31-4.08; P=0.036).ConclusionsThe identification of these risk factors leads to better identification of patients with a greater probability of using blood, allocation blood bank resources and cost-effectiveness use of blood products.

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