Computational models in cardiology

Steven A. Niederer*, Joost Lumens, Natalia A. Trayanova

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

118 Citations (Web of Science)

Abstract

The treatment of individual patients in cardiology practice increasingly relies on advanced imaging, genetic screening and devices. As the amount of imaging and other diagnostic data increases, paralleled by the greater capacity to personalize treatment, the difficulty of using the full array of measurements of a patient to determine an optimal treatment seems also to be paradoxically increasing. Computational models are progressively addressing this issue by providing a common framework for integrating multiple data sets from individual patients. These models, which are based on physiology and physics rather than on population statistics, enable computational simulations to reveal diagnostic information that would have otherwise remained concealed and to predict treatment outcomes for individual patients. The inherent need for patient-specific models in cardiology is clear and is driving the rapid development of tools and techniques for creating personalized methods to guide pharmaceutical therapy, deployment of devices and surgical interventions.

Original languageEnglish
Pages (from-to)100-111
Number of pages12
JournalNature Reviews Cardiology
Volume16
Issue number2
DOIs
Publication statusPublished - Feb 2019

Keywords

  • CARDIAC-RESYNCHRONIZATION-THERAPY
  • MAGNETIC-RESONANCE IMAGES
  • PATIENT-SPECIFIC MODELS
  • BUNDLE-BRANCH BLOCK
  • ATRIAL-FIBRILLATION
  • HEART-FAILURE
  • MYOCARDIAL-INFARCTION
  • BIOPHYSICAL MODEL
  • COMPUTER-MODEL
  • FAILING HEART

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