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. 2025 Apr 1;8(4):e255522.
doi: 10.1001/jamanetworkopen.2025.5522.

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

Annika Eyth  1 , Felix Borngaesser  1   2 , Maíra I Rudolph  1   3 , Béla-Simon Paschold  4   5 , Tina Ramishvili  1 , Lars Kaiser  4   5 , Christopher W Tam  1 , Karuna Wongtangman  1   6 , Greta Eikermann  7 , Shweta Garg  1   8 , Michael H Karasick  9 , Michael E Kiyatkin  1 , Milan M Kinkhabwala  10 , Stephen J Forest  11 , Jonathan Leff  1 , Ling Zhang  1 , Philipp Fassbender  1 , Ibraheem Karaye  1 , Andrea U Steinbicker  3 , Maximilian S Schaefer  4   5   12 , Matthias Eikermann  1   13 , Se-Chan Kim  1   14
Affiliations

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

Annika Eyth et al. JAMA Netw Open. .

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 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.

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Conflict of interest statement

Conflict of Interest Disclosures: Prof Steinbicker reported receiving grants from the German Research Society (FerrOs, STE1895/9-1) and working as a committee member of the interdisciplinary Working Community for Clinical hemotherapy (IAKH), Germany, during the conduct of the study. Dr Schaefer reported receiving grants from Merck and Fisher & Paykel as well as personal fees from Mindray Medical outside the submitted work. No other disclosures were reported.

Figures

Figure 1.
Figure 1.. Study Flow
Data from Montefiore Medical Center in the Bronx, New York, were divided chronologically into the development cohort (January 2016 to June 2021) (A) and internal validation cohort (June 2021 to February 2023) (B). C, Data from Beth Israel Deaconess Medical Center in Boston, Massachusetts, were used as an external validation cohort (January 2008 to June 2022). ASA indicates American Society of Anesthesiologists. aMultiple criteria may apply.
Figure 2.
Figure 2.. Summary Figure
A, The variables included in the model are summarized with corresponding score point values. The exact definitions of each predictor can be found in eTables 3 and 4 in Supplement 1. B, Probability of intraoperative packed red blood cell transfusion as percentages (0.003%-96.5%) in reference to the total score value that a patient can receive (0-62). ASA indicates American Society of Anesthesiologists; INR, international normalized ratio.
Figure 3.
Figure 3.. Model Discrimination
AUC indicates area under the receiver operating characteristic curve; TRUST, Transfusion Risk Understanding Scoring Tool.
See this image and copyright information in PMC

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