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In Silico Clinical Trials for Cardiovascular Disease
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Simulation, identification and statistical variation in cardiovascular analysis (SISCA) - A software framework for

Rudolf Huttary1, Leonid Goubergrits2, Christof Schütte1

  • 1Freie Universität Berlin, Dep. of Mathematics and Computer Science, D-14195 Berlin, Arnimallee 6, Germany.

Computers in Biology and Medicine
|June 4, 2017
PubMed
Summary

This study introduces SISCA, a cardiovascular modeling framework for patient-specific simulations. Results highlight the need for better clinical data management in cardiovascular research.

Keywords:
0D modelingCardiovascular simulationClinical data setCoarctation of aortaDisease-specific modelsDistributed parameter modelingLumped modelsMulti-compartment modelingMultiscale modelingPatient-specific modelsWindkessel elements

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Area of Science:

  • Cardiovascular Physiology
  • Computational Biology
  • Medical Informatics

Background:

  • Cardiovascular modeling faces challenges due to uncertain system parameters and natural variability.
  • Understanding cardiovascular diseases requires detailed analysis of parameter variations in health and disease.

Purpose of the Study:

  • To present SISCA, a novel software framework for cardiovascular system modeling.
  • To implement a multi-model statistical ensemble approach for patient-specific cardiovascular simulations.
  • To address challenges in data-driven modeling using clinical datasets.

Main Methods:

  • Developed SISCA, a MATLAB framework for cardiovascular system modeling.
  • Employed a multi-model statistical ensemble approach for dimension-reduced, multi-compartment models.
  • Utilized patient-specific clinical data (pre/post-surgery for coarctation of aorta) for modeling.

Main Results:

  • Simulations using SISCA reproduced measured pressures and flows reasonably well for stenosis and stent treatment.
  • Patient-specific modeling adapted a validated model using clinical metadata and MRI geometry.
  • Post-treatment data inconsistencies highlighted the need for improved clinical data conditioning and uncertainty management.

Conclusions:

  • SISCA provides a framework for statistical variation, system identification, and patient-specific cardiovascular modeling.
  • Effective patient-specific cardiovascular modeling necessitates rigorous attention to clinical data quality and uncertainty.
  • Further development is needed to enhance the reliability of cardiovascular models with real-world clinical data.