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Transfusion after total knee arthroplasty can be predicted using the machine learning algorithm.

Changwung Jo1, Sunho Ko1, Woo Cheol Shin2

  • 1Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea.

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Summary

This study developed a machine learning model to predict blood transfusion needs after total knee arthroplasty (TKA). The model uses six preoperative variables to identify patients at high risk, enabling proactive care to reduce complications.

Keywords:
ArthroplastyArtificial intelligenceMachine learningPredictionPredictive modelTransfusion

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Area of Science:

  • Orthopedic Surgery
  • Medical Informatics
  • Machine Learning in Healthcare

Background:

  • Blood transfusions following total knee arthroplasty (TKA) are linked to increased complication and infection risks.
  • Predictive models for transfusion risk after TKA are lacking, particularly those utilizing machine learning.

Purpose of the Study:

  • To identify key preoperative variables for predicting blood transfusion requirements after TKA.
  • To develop and validate a machine learning model for transfusion risk assessment.
  • To create a user-friendly, web-based system for clinical application.

Main Methods:

  • Retrospective review of 1686 TKA patients, collecting data on 43 preoperative variables.
  • Recursive feature elimination for variable selection; gradient boosting machine for model development.
  • External validation using data from an independent institution.

Main Results:

  • Six preoperative variables (Hb, platelet count, surgery type, tranexamic acid, age, body weight) were identified.
  • The predictive model achieved an AUC of 0.842 (95% CI 0.820-0.856) and 0.880 (95% CI 0.844-0.910) upon external validation.
  • A web-based risk-assessment system is available at http://safetka.net.

Conclusions:

  • A validated machine learning model effectively predicts transfusion risk after TKA using six preoperative variables.
  • The model offers a simple, reliable tool for clinicians to assess transfusion risk preoperatively.
  • This system can guide preventative measures for high-risk patients, potentially improving outcomes.