• Ana Tomás-Biosca, Antonio Martinez-Simon, Jorge Guridi, Cristina Honorato-Cia, Elena Cacho-Asenjo, Sonia Tejada Solís, Bartolomé Bejarano, Maria V Becerra-Castro, and Jorge M Nuñez-Cordoba.
• Departments of Neurosurgery, Neurology, and Neurosciences.
• J Neurosurg Anesthesiol. 2023 Jan 1; 35 (1): 747974-79.

BackgroundThe identification of factors associated with perioperative red blood cell (RBC) transfusion provides an opportunity to optimize the patient and surgical plan, and to guide perioperative crossmatch and RBC orders. We examined the association among potential bleeding risk factors and RBC requirements to develop a novel predictive model for RBC transfusion in patients undergoing brain tumor surgery.MethodsThis retrospective study included 696 adults who underwent brain tumor surgery between 2008 and 2018. Multivariable logistic regression with backward stepwise selection for predictor selection was used during modeling. Model performance was evaluated using area under the receiver operating characteristic curve, and calibration was evaluated with Hosmer-Lemeshow goodness-of-fit χ 2 -estimate.ResultsPreoperative hemoglobin level was inversely associated with the probability of RBC transfusion (odds ratio [OR]: 0.50; 95% confidence interval [CI]: 0.39-0.63; P <0.001). The need for RBC transfusion was also greater in patients who had a previous craniotomy (OR: 2.71; 95% CI: 1.32-5.57; P =0.007) and in those with larger brain tumor volume (OR: 1.01; 95% CI: 1.00-1.02; P =0.009). The relationship between number of planned craniotomy sites and RBC transfusion was not statistically significant (OR: 2.11; 95% CI: 0.61-7.32; P =0.238). A predictive model for RBC requirements was built using these 4 variables. The area under the receiver operating characteristic curve was 0.79 (95% CI: 0.70-0.87; P <0.001) showing acceptable calibration for predicting RBC transfusion requirements.ConclusionsRBC requirements in patients undergoing brain tumor surgery can be estimated with acceptable accuracy using a predictive model based on readily available preoperative clinical variables. This predictive model could help to optimize both individual patients and surgical plans, and to guide perioperative crossmatch orders.Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.

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