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Adjusted preoperative variables to predict perioperative red blood cell transfusion in coronary artery bypass
Liangyuan Lu1,2, Ji Che3, Wuxiang Xie4
1Department of Anesthesiology, Beijing An Zhen Hospital, Capital Medical University, Beijing Institute of Heart, Lung and Blood Vessel Diseases, 2 An Zhen Road, Chaoyang District, Beijing, 100029, Beijing, China.
Insights
Predicting red blood cell (RBC) transfusions in coronary artery bypass grafting (CABG) patients perioperatively is crucial. A new comprehensive model effectively predicts these transfusions using adjusted preoperative variables.
Area of Science:
- Cardiovascular Surgery
- Transfusion Medicine
- Predictive Analytics
Background:
- Predicting perioperative blood transfusions in coronary artery bypass grafting (CABG) patients remains challenging.
- Identifying key variables for accurate transfusion prediction is essential for optimizing patient care.
Purpose of the Study:
- To develop a comprehensive predictive model for perioperative red blood cell (RBC) transfusions.
- To utilize adjusted preoperative variables for predicting RBC transfusions in isolated CABG patients.
Main Methods:
- Analysis of perioperative data from 1253 isolated CABG patients (April 2018 - March 2019).
- Logistic regression used to build separate intraoperative and postoperative RBC transfusion prediction models.
- A combined model integrated significant variables from both models; Area Under the Receiver Operating Characteristic Curve (AUC) assessed model performance.
Main Results:
- Overall RBC transfusion rate was 29.05% (intraoperative: 6.9%, postoperative: 26.7%).
- Prediction models identified 8 (intraoperative), 12 (postoperative), and 13 (perioperative) significant variables.
- AUC values of 0.87 (intraoperative), 0.82 (postoperative), and 0.83 (perioperative) indicated moderate discriminatory power.
Conclusions:
- A comprehensive model integrating intraoperative and postoperative predictive variables is feasible.
- This model demonstrates effectiveness in predicting perioperative RBC transfusions in CABG patients.
Background:
The variables for predicting blood transfusion perioperatively are not completely clear in coronary artery bypass grafting (CABG) patients.
Objectives:
To construct a comprehensive model to predict perioperative RBC transfusion in patients undergoing isolated CABG using adjusted preoperative variables.
Methods:
Perioperative data of 1253 patients who underwent isolated CABG by the same surgical team were collected from April 2018 to March 2019. Logistic regression analyses were used to establish equations to construct two models for predicting intraoperative and postoperative RBC transfusions, respectively. All significant variables included in the two models were combined to form a comprehensive model to predict perioperative RBC transfusion. Area under the receiver operating characteristic (ROC) curve (AUC) was used to evaluate the discriminatory power of the models.
Results:
The total RBC transfusion rate for CABG patients during hospitalization was 29.05%. The rate of intraoperative and postoperative RBC transfusions was 6.9% and 26.7%, respectively. Eight variables in a total of 30 risk factors constituted the intraoperative prediction model, 12 variables constituted the postoperative prediction model, and 13 variables for the combined model. The AUC of the three models were 0.87, 0.82, and 0.83, respectively, demonstrating moderate discriminatory power for RBC transfusion during the intraoperative, postoperative, and perioperative periods.
Conclusion:
The comprehensive model combined with all variables of predicting intraoperative and postoperative RBC transfusion is feasible for predicting perioperative RBC transfusion.
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