Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies
  1. Home
  2. Research Domains

Development and multinational validation of an algorithmic strategy for high Lp(a) screening

Arya Aminorroaya1, Lovedeep S Dhingra1, Evangelos K Oikonomou1

  • 1Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA.

Nature Cardiovascular Research
|August 28, 2024

Related Experiment Videos

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.5K
Pooled shRNA Library Screening to Identify Factors that Modulate a Drug Resistance Phenotype
14:51

Pooled shRNA Library Screening to Identify Factors that Modulate a Drug Resistance Phenotype

Published on: June 17, 2022

3.2K
MISSION LentiPlex Pooled shRNA Library Screening in Mammalian Cells
09:12

MISSION LentiPlex Pooled shRNA Library Screening in Mammalian Cells

Published on: December 21, 2011

18.6K

View abstract on PubMed

Summary

Related Concept Videos

  • Biomedical And Clinical Sciences
  • Oncology And Carcinogenesis
  • Predictive And Prognostic Markers
  • Development And Multinational Validation Of An Algorithmic Strategy For High Lp(a) Screening
  • This summary is machine-generated.

    Elevated lipoprotein (a) (Lp(a)) increases cardiovascular risk, but testing is rare. A new machine learning model, ARISE, effectively identifies individuals with high Lp(a) levels, improving screening efficiency.

    Area of Science:

    • Cardiovascular Medicine
    • Artificial Intelligence
    • Genetics and Genomics

    Background:

    • Elevated lipoprotein (a) (Lp(a)) is a significant, heritable risk factor for premature atherosclerotic cardiovascular disease.
    • Current Lp(a) testing rates are low (<0.5%), hindering the clinical application of emerging targeted therapies.
    • There is a critical need for improved screening strategies to identify individuals at risk due to elevated Lp(a).

    Purpose of the Study:

    • To develop and validate a machine learning model for targeted screening of elevated Lp(a) (≥150 nmol/L).
    • To assess the model's ability to reduce the number of individuals needing testing to identify those with elevated Lp(a).
    • To evaluate the model's performance across diverse, large-scale cohort studies.

    Main Methods:

    • Development of a machine learning model, ARISE (Algorithmic Risk Inspection for Screening Elevated Lp(a)), using data from the UK Biobank (N=456,815).
    • External validation of the ARISE model in three independent cohort studies: ARIC (N=14,484), CARDIA (N=4,124), and MESA (N=4,672).
    • Analysis of model performance based on reduction in the number needed to test (NNT) to identify individuals with elevated Lp(a).

    Main Results:

    • The ARISE model demonstrated consistent performance across internal and external validation cohorts.
    • ARISE reduced the number needed to test for elevated Lp(a) by up to 67.3%, depending on the probability threshold used.
    • The model effectively utilizes commonly available clinical features for screening purposes.

    Conclusions:

    • The ARISE machine learning model offers a promising tool for optimizing screening for elevated Lp(a).
    • Deployment of ARISE in electronic health records could significantly enhance the yield of Lp(a) testing in real-world clinical settings.
    • Improved screening facilitated by ARISE may facilitate earlier intervention and better management of cardiovascular risk associated with elevated Lp(a).

    Related Experiment Videos

    A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
    12:18

    A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

    Published on: January 11, 2020

    7.5K
    Pooled shRNA Library Screening to Identify Factors that Modulate a Drug Resistance Phenotype
    14:51

    Pooled shRNA Library Screening to Identify Factors that Modulate a Drug Resistance Phenotype

    Published on: June 17, 2022

    3.2K
    MISSION LentiPlex Pooled shRNA Library Screening in Mammalian Cells
    09:12

    MISSION LentiPlex Pooled shRNA Library Screening in Mammalian Cells

    Published on: December 21, 2011

    18.6K