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Evaluation of computational models for hemolysis estimation.

Lei Gu1, William A Smith

  • 1Department of Biomedical Engineering, Cleveland Clinic Lerner College of Medicine-CWRU, Cleveland, Ohio, USA.

ASAIO Journal (American Society for Artificial Internal Organs : 1992)
|June 23, 2005
PubMed
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This study compared computational fluid dynamics models for predicting red blood cell damage in medical devices. Results show existing models perform similarly, with potential improvements by accounting for repeated flow passage effects.

Area of Science:

  • Biomedical Engineering
  • Hemodynamics
  • Computational Biology

Background:

  • Mechanical erythrocyte damage, or hemolysis, is a concern in blood-contacting medical devices like heart valves and pumps.
  • Computational fluid dynamics (CFD) and mathematical models are used to estimate hemolysis, but lack comparative validation across diverse flow conditions.

Purpose of the Study:

  • To compare existing mathematical models for predicting blood damage.
  • To evaluate model performance against experimental hemolysis measurements in various flow geometries.
  • To assess the potential for improving hemolysis prediction models.

Main Methods:

  • Hemolysis testing of four devices: hemoresistometer, spinning disk, capillary tube, and concentric cylinder viscometer.
  • Computational simulation of blood flow and damage mechanisms within these devices.

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  • Comparison of predictions from multiple existing blood damage models against experimental data.

    • Main Results:

    • Existing blood damage prediction models demonstrated similar effectiveness across tested devices.
    • Experimental hemolysis measurements provided a benchmark for model evaluation.
    • A proposed repeated flow passage effect showed potential to enhance model accuracy.

    Conclusions:

    • Current models for predicting mechanical erythrocyte damage offer comparable performance.
    • Incorporating a repeated flow passage effect could improve the accuracy of blood damage predictions.
    • Enhanced models can more effectively predict hemolysis in diverse clinical applications.