PLATELET TRANSFUSION PREDICTION IN CARDIAC SURGERY

Abstract

Impaired platelet function from cardiopulmonary bypass and the use of preoperative anti-platelet agents are important factors in cardiac surgery blood loss. Platelet transfusions are commonly used in the postoperative phase to mitigate bleeding, but subject’s patients to the risk of infection, alloimmunization and lung injury. Guidelines exist for platelet administration; however, there is significant transfusion variability amongst centers. The lack of evidence based criteria for the administration of platelet transfusion in cardiac surgery creates this heterogeneity. In order to enhance our understanding of platelet transfusion, we intended to identify clinical risk factors associated with platelet transfusion and to subsequently develop a platelet transfusion prediction model for cardiac surgical procedures. The Cardiac Surgery Blood Utilization Database (CSBUD) is our hospitals proprietary software used to capture demographic and transfusion specific data for each patient undergoing cardiac surgery. The CSBUD was interrogated (2013-2018) for all consecutive patients (n=2648). The data was randomly divided into a development set (n=1661) and validation set (n=987). Univariable analysis was used to identify covariates for model inclusion (p < 0.25). A multivariable logistic regression model was used to assess treatment effects. A supervised backward stepwise elimination technique (p < 0.10) was employed to identify independent predictors for the final model. The independent predictor coefficients were used to derive a platelet transfusion risk score. The analysis identified preoperative platelet count (OR=0.985, 95%CI: 0.981-0.989, p<0.001), ADP inhibitor (OR=2.507, 95% CI: 1.064-5.904, p=0.036) and GPIIb/IIIa (OR=8.915, 95% CI: 1.061-74.859, p=0.044) use; emergent surgery (OR=1.179, 95% CI: 1.079-2.411, p<0.001), aortic surgery (OR=7.349, 95% CI: 4.017-13.445, p<0.001) and RBC transfusion (OR=15.44, 95% CI: 9.736-24.497, p<0.001) as being independently associated with platelet transfusion. The risk score was divided into low, medium and high categories with a combined (development set and validation set) transfusion rate of 3%, 32% and 86%, respectively (Table 1). No significant differences existed between the development and validation sets. The model demonstrated excellent discrimination (Figure 1). Our analysis identified six independent clinical factors predictive of platelet transfusions. This enabled us to develop a highly discriminative platelet transfusion prediction model, which classifies the probability of a platelet transfusion into low, medium and high-risk categories. To our knowledge this is the first report of such a predictive model and we believe this will help clinicians to tailor their perioperative management accordingly.View Large Image Figure ViewerDownload Hi-res image Download (PPT)