Development and Validation of a Risk Model to Predict Intraoperative Blood Transfusion

Importance Crossmatched packed red blood cells (pRBC) that are not transfused result in significant waste of this scarce resource. Efficient utilization should be part of a patient blood management strategy. Objective To develop and validate a prediction model to identify surgical patients at high risk of intraoperative pRBC transfusion. Design, Setting, and Participants This prognostic study used hospital registry data from 2 quaternary hospital networks from January 2016 to June 2021 (development: Montefiore Medical Center [MMC], Bronx, New York), June 2021 to February 2023 (internal validation: MMC), and January 2008 to June 2022 (external validation: Beth Israel Deaconess Medical Center [BIDMC], Boston, Massachusetts). Participants were patients aged 18 years or older undergoing surgery. Main Outcome and Measures The outcome was intraoperative transfusion of 1 or more pRBC units. Based on a priori–defined candidate predictors, stepwise backward regression was applied to develop a computational model of independent predictors for intraoperative pRBC transfusion. Results The development and validation cohorts consisted of 816 618 patients (273 654 at MMC: mean [SD], age 57.5 [17.2] years; 161 481 [59.0%] female; 542 964 at BIDMC: mean [SD] age, 56.0 [17.1] years; 310 272 [57.1%] female). Overall, 18 662 patients (2.3%) received at least 1 unit of pRBC. The final model contained 24 preoperative predictors: nonambulatory surgery; American Society of Anesthesiologists physical status; international normalized ratio; redo surgery; emergency surgery or surgery outside of regular working hours; estimated surgical duration of at least 120 minutes; surgical complexity; liver disease; hypoalbuminemia; thrombocytopenia; mild, moderate, or severe anemia; and surgery type. The area under the receiver operating characteristic curve (AUC) was 0.93 (95% CI, 0.92-0.93), suggesting high predictive accuracy and generalizability. Positive predictive value (PPV) and negative predictive value (NPV) were 8.9% (95% CI, 8.7%-9.2%) and 99.7% (95% CI, 99.7%-99.7%), respectively, with increased predictive values for operations with a higher a priori risk of pRBC transfusion. The model’s performance was confirmed in internal and external validation. The prediction tool outperformed the established Transfusion Risk Understanding Scoring Tool (AUC, 0.64 [0.63-0.64]; PPV, 2.6% [95% CI, 2.5%-2.6%]; NPV, 99.2% [95% CI, 99.1%-99.3%]) ( P < .001) and was noninferior to 3 machine learning–derived scores. Conclusions and Relevance In this prognostic study of surgical patients, the Transfusion Forecast Utility for Surgical Events (TRANSFUSE) model for predicting intraoperative pRBC transfusion was developed and validated. The instrument can be used independently of machine learning infrastructure availability to inform preoperative pRBC orders and to minimize waste of nontransfused red blood cell units.