Does incorporation of thromboelastography improve bleeding prediction following adult cardiac surgery?

Abstract

Cardiopulmonary bypass (CPB) coagulopathy increases utilization of allogenic blood/blood products, which can negatively affect patient outcomes. Thromboelastography (TEG) is a point-of-care measurement of clot formation and fibrinolysis. We investigated whether the addition of TEG parameters to a clinically based bleeding model would improve the predictability of postoperative bleeding. A total of 439 patients' charts were retrospectively investigated for 8-h chest tube output (CTO) postoperatively. For model 1, the variables recorded were patient age, gender, body surface area, clopidogrel use, CPB time, first post-CPB fibrinogen serum level, first post-CPB platelet count, first post-CPB international normalized ratio, the total amount of intraoperative cell saver blood transfused, and postoperative first ICU hematocrit level. Model 2 had the model 1 variables, TEG angle, and maximum amplitude. The outcome was defined as 0-8-h CTO. The predictor variables were placed into a forward stepwise regression model for continuous outcomes. Analysis of variance with adjusted R was used to assess the goodness-of-fit of both predictive models. The predictive accuracy of the model was examined using CTO as a dichotomous variable (75th percentile, 480 ml) and receiver operating characteristic curves for both models. Advanced age, male gender, preoperative clopidogrel use for 5 days or less, greater cell saver blood utilization, and lower postoperative hematocrit levels were associated with increased 8-h CTO (P < 0.05). Adding TEG angle and maximum amplitude to model 1 did not improve CTO predictability. When TEG angle and maximum amplitude were added as predictor factors, the predictability of the bleeding model did not improve.

Fig. 1

Graphical representation of thromboelastography (TEG) parameters (R, K, angle, and maximum amplitude).

Fig. 2

Patient percentage distribution based on the type of cardiac surgery performed. AVR, aortic valve replacement; CABG, coronary artery bypass graft; CEA, carotid endarterectomy; MVR, mitral valve repair/replacement.

Fig. 3

Eight-hour chest tube output (CTO) in milliliters based on the type of cardiac surgery performed, where A = median CTO (ml), B = average CTO (ml), C = CTO (ml) [25–75th interquartile range (IQR)], x = standard deviation (SD), and n = number of patients. AVR, aortic valve replacement; CABG, coronary artery bypass graft; CEA, carotid endarterectomy; MVR, mitral valve repair/replacement.

Fig. 4

Total cell saver blood (CSB) utilization in milliliters based on the type of cardiac surgery performed, where A = median CTO (ml), B = average CTO (ml), C = CTO (ml) [25–75th interquartile range (IQR)], x = standard deviation (SD), and n = number of patients. AVR, aortic valve replacement; CABG, coronary artery bypass graft; CEA, carotid endarterectomy; MVR, mitral valve repair/replacement.

Fig. 5

Percentage of patients who received packed red blood cell (PRBC) and platelet transfusions in units and single-donor units, respectively, based on the type of cardiac surgery performed, where I = intraoperative period, T = total (intraoperatively + 72 h postoperatively), x = median, y = standard deviation (SD), and n = number of patients. AVR, aortic valve replacement; CABG, coronary artery bypass graft; CEA, carotid endarterectomy; MVR, mitral valve repair/replacement.

Fig. 6

Percentage of patients who received fresh frozen plasma (FFP) and cryoprecipitate (5 units represented as 1 unit) transfusions in units, based on the type of cardiac surgery performed, where I = intraoperative period, T = total (intraoperatively + 72 h postoperatively), x = median, y = standard deviation (SD), and n = number of patients. AVR, aortic valve replacement; CABG, coronary artery bypass graft; CEA, carotid endarterectomy; MVR, mitral valve repair/replacement.

Fig. 7

Receiver operating characteristic (ROC) curves of (a) model 1 and (b) model 2, predicting total chest tube output at least 480 ml (75th percentile; 0–8 h postoperatively). (a) Model 1 [area under the ROC curve (AUC) = 0.732, Bayesian information criterion (BIC) = 233.226, corrected Akaike information criterion (AICc) = 226.818, n = 188] contains clinical/laboratory data only. (b) Model 2 (AUC = 0.711, BIC = 228.13, AICc = 221.882, n = 174) contains clinical/laboratory and thromboelastography (TEG) data (P = 0.3743). The straight black line shown in the graph is drawn at a 45° angle tangent to the ROC curve. It demarcates a reasonable cutoff point at which false positives and false negatives produce similar costs.

Fig. 8

Predictability of model 1 and model 2 (slope = 0.95; P <0.0001). A slope of 1 means the two models agree 100% in their predictions.