Abstract

BACKGROUND A predictive model that can identify patients who are at increased risk of intraoperative blood transfusion could guide preoperative transfusion risk counseling, optimize health care resources, and reduce medical costs. Although previous studies have identified some predictors for particular populations, there is currently no existing model that uses preoperative variables to accurately predict blood transfusion during surgery, which could help anesthesiologists optimize intraoperative anesthetic management. METHODS We collected data from 582 patients who underwent elective liver resection at a university-affiliated tertiary hospital between January 1, 2018, and December 31, 2020. The data set was then randomly divided into a training set (n = 410) and a validation set (n = 172) at a 7:3 ratio. The least absolute shrinkage and selection operating regression model was used to select the optimal feature, and multivariate logistic regression analysis was applied to construct the transfusion risk model. The concordance index (C-index) and the area under the receiver operating characteristic (ROC) curve (AUC) were used to evaluate the discrimination ability, and the calibration ability was assessed with calibration curves. In addition, we used decision curve analysis (DCA) to estimate the clinical application value. For external validation, the test set data were employed. RESULTS The final model had 8 predictor variables for intraoperative blood transfusion, which included the following: preoperative hemoglobin level, preoperative prothrombin time >14 s, preoperative total bilirubin >21 μmol/L, respiratory diseases, cirrhosis, maximum lesion diameter >5 cm, macrovascular invasion, and previous abdominal surgery. The model showed a C-index of 0.834 (95% confidence interval, 0.789-0.879) for the training set and 0.831 (95% confidence interval, 0.766-0.896) for the validation set. The AUCs were 0.834 and 0.831 for the training and validation sets, respectively. The calibration curve showed that our model had good consistency between the predictions and observations. The DCA demonstrated that the transfusion nomogram was reliable for clinical applications when an intervention was decided at the possible threshold across 1%-99% for the training set. CONCLUSION We developed a predictive model with excellent accuracy and discrimination ability that can help identify those patients at higher odds of intraoperative blood transfusion. This tool may help guide preoperative counseling regarding transfusion risk, optimize health care resources, reduce medical costs, and optimize anesthetic management during surgery.

Abstract

OBJECTIVE To test in a prospective randomized study the hypothesis that use of thromboelastography (TEG) decreases blood transfusion during major surgery. MATERIAL AND METHODS Twenty-eight patients undergoing orthotopic liver transplantation were recruited over 2 years. Patients were randomized into 2 groups: those monitored during surgery using point-of-care TEG analysis, and those monitored using standard laboratory measures of blood coagulation. Specific trigger points for transfusion were established in each group. RESULTS In patients monitored via TEG, significantly less fresh-frozen plasma was used (mean [SD], 12. 8 [7. 0] units vs 21. 5 [12. 7] units). There was a trend toward less blood loss in the TEG-monitored patients; however, the difference was not significant. There were no differences in total fluid administration and 3-year survival. CONCLUSION Thromboelastography-guided transfusion decreases transfusion of fresh- frozen plasma in patients undergoing orthotopic liver transplantation, but does not affect 3-year survival.