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

Annika Eyth1, Felix Borngaesser1,2, Maíra I Rudolph1,3

  • 1Department of Anesthesiology, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, New York.

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Summary

A new model predicts which surgical patients need packed red blood cells (pRBC) during surgery, reducing waste. The TRANSFUSE tool accurately identifies high-risk patients, improving blood management strategies.

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Area of Science:

  • Transfusion Medicine
  • Surgical Outcomes
  • Predictive Analytics

Background:

  • Wasted packed red blood cells (pRBC) represent a significant loss of a scarce resource.
  • Efficient utilization of pRBC is crucial for patient blood management.

Purpose of the Study:

  • To develop and validate a predictive model for identifying surgical patients at high risk of intraoperative pRBC transfusion.
  • The goal is to minimize the waste of transfused blood products.

Main Methods:

  • A prognostic study utilizing hospital registry data from two quaternary hospital networks.
  • Stepwise backward regression was used to develop a computational model based on 24 preoperative predictors.
  • The model was validated internally and externally.

Main Results:

  • The developed model, TRANSFUSE, demonstrated high predictive accuracy (AUC 0.93) in identifying patients requiring intraoperative pRBC transfusion.
  • The model outperformed the established Transfusion Risk Understanding Scoring Tool.
  • Key predictors included nonambulatory status, ASA physical status, INR, redo/emergency surgery, and various patient health factors.

Conclusions:

  • The TRANSFUSE model accurately predicts intraoperative pRBC transfusion needs in surgical patients.
  • This tool can be used preoperatively to optimize pRBC orders and reduce wastage.
  • It offers a valuable, accessible method for improving blood management in surgical settings.