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Breaking binary in cardiovascular disease risk prediction.

Yichi Zhang1, Akl C Fahed2,3

  • 1Department of Medicine, Massachusetts General Hospital, Boston, MA, USA.

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PubMed
Summary
This summary is machine-generated.

New tools like polygenic risk scores (PRS) and machine learning (ML) algorithms improve atherosclerotic cardiovascular disease (ASCVD) risk prediction. These advanced methods offer continuum models, moving beyond simple binary outcomes for better ASCVD risk stratification.

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Area of Science:

  • Cardiovascular Medicine
  • Genetics
  • Artificial Intelligence

Background:

  • Atherosclerotic cardiovascular disease (ASCVD) is a major global cause of mortality.
  • Traditional risk assessment models have limitations in precision.
  • Recent advancements in genetics, omics, and machine learning offer new avenues for risk stratification.

Purpose of the Study:

  • To explore the role of polygenic risk scores (PRS) and machine learning (ML) algorithms in enhancing ASCVD risk stratification.
  • To discuss the shift from binary risk prediction to continuum models.
  • To highlight the need for addressing disparities in PRS implementation.

Main Methods:

  • Integration of polygenic risk scores (PRS) derived from genomic data.
  • Application of composite machine learning (ML) algorithms.
  • Development of continuum-based risk models.

Main Results:

  • Polygenic risk scores (PRS) and ML algorithms provide more comprehensive ASCVD risk stratification.
  • These tools facilitate a move towards continuum models, offering nuanced risk assessment.
  • The study identifies the necessity of addressing socioeconomic and racial disparities within PRS.

Conclusions:

  • Polygenic risk scores (PRS) and machine learning (ML) represent a paradigm shift in ASCVD risk stratification.
  • Continuum models derived from these technologies offer superior risk assessment compared to binary predictions.
  • Equitable implementation of PRS is crucial to mitigate existing health disparities in cardiovascular disease.