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

Abstract

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 816618 patients (273654 at MMC: mean [SD], age 57.5 [17.2] years; 161481 [59.0%] female; 542964 at BIDMC: mean [SD] age, 56.0 [17.1] years; 310272 [57.1%] female). Overall, 18662 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