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Imaging Studies for Cardiovascular System VI: Calcium -Scoring CT01:25

Imaging Studies for Cardiovascular System VI: Calcium -Scoring CT

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Related Experiment Video

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Hydra, a Computer-Based Platform for Aiding Clinicians in Cardiovascular Analysis and Diagnosis
07:51

Hydra, a Computer-Based Platform for Aiding Clinicians in Cardiovascular Analysis and Diagnosis

Published on: September 26, 2018

Cardiovascular risk can be represented by scaled rectangle diagrams.

Roger J Marshall1

  • 1Section of Epidemiology and Biostatistics, School of Population Health, University of Auckland, New Zealand. rj.marshall@auckland.ac.nz

Journal of Clinical Epidemiology
|July 29, 2009
PubMed
Summary

Graphical methods reveal discrepancies between predicted cardiovascular disease (CVD) risk and actual CVD events. Many high-risk individuals do not develop CVD, and many CVD events occur in those not identified as high risk.

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Area of Science:

  • Cardiology
  • Medical Informatics
  • Biostatistics

Background:

  • Cardiovascular disease (CVD) risk prediction models are crucial for public health.
  • Accurate graphical representation of risk versus actual outcomes is essential for clinical decision-making.
  • Discrepancies between predicted risk and observed events can impact patient management.

Purpose of the Study:

  • To demonstrate a graphical method for visualizing cardiovascular disease (CVD) risk levels against actual CVD occurrence.
  • To illustrate the performance of multivariate risk scores in predicting CVD events.
  • To highlight potential poor discrimination in CVD risk prediction models.

Main Methods:

  • Utilized a methodological approach employing graphical illustrations.
  • Analyzed data from three international studies (New Zealand, China, Scotland) on CVD risk and outcomes.
  • Determined CVD risk levels using multivariate risk scores.

Main Results:

  • Graphical representations revealed a frequent mismatch between predicted CVD risk levels and actual CVD event occurrence.
  • A significant proportion of individuals classified as high-risk did not experience a CVD event.
  • Conversely, many CVD events occurred in individuals not identified within the high-risk category.

Conclusions:

  • Scaled rectangles provide an effective visual tool to assess the concordance between modeled predicted risk and actual CVD occurrence.
  • The graphical method clearly illustrates poor discrimination in CVD risk prediction.
  • This visualization technique aids in understanding the limitations of current risk assessment tools.