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Machine learning for predicting preoperative red blood cell demand.

Yannan Feng1, Zhenhua Xu2, Xiaolin Sun1

  • 1Department of Transfusion Medicine, The First Medical Center of Chinese PLA General Hospital, Beijing, China.

Transfusion Medicine (Oxford, England)
|May 24, 2021
PubMed
Summary
This summary is machine-generated.

Machine learning accurately predicts red blood cell (RBC) needs for surgery, improving upon empirical methods. This AI model optimizes blood preparation, reducing waste and supply risks for perioperative patients.

Keywords:
machine learningprecision predictionpreoperative demandred blood cells

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Medicine
  • Transfusion Medicine

Background:

  • Current red blood cell (RBC) transfusion practices rely on empirical models, leading to suboptimal application.
  • There's a lack of precise quantitative standards for perioperative RBC needs.

Purpose of the Study:

  • To develop and evaluate a machine learning (ML) model for predicting preoperative RBC transfusion requirements.
  • To compare the accuracy of the ML model against clinician judgment.

Main Methods:

  • Retrospective analysis of 130,996 patient surgical records (January 2011 - June 2017).
  • Utilized ML algorithms to build an AI model predicting RBC demand based on preoperative variables.
  • Compared AI model predictions with clinician predictions.

Main Results:

  • The Light Gradient Boosting Machine (Lightgbm) algorithm demonstrated the best performance (AUC 0.908).
  • The AI model showed higher accuracy than clinicians for predicting 0, 2, and 4 units of RBCs.
  • Clinicians outperformed the AI model in predicting 1, 3, 5-10 units of RBCs.

Conclusions:

  • An ML-based model using the Lightgbm algorithm offers superior accuracy for preoperative RBC transfusion prediction compared to traditional methods.
  • This AI approach can mitigate risks associated with blood supply shortages and reduce costs from unnecessary testing.