Jove
Visualize
Contact Us

Related Concept Videos

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

Imaging Studies for Cardiovascular System VI: Calcium -Scoring CT

639
Calcium-Scoring CT ScanA calcium-scoring CT scan, also known as coronary artery calcium (CAC) scan, detects calcium deposits in the coronary arteries. This test assesses the risk of coronary artery disease (CAD), which can lead to cardiovascular events such as angina, heart failure, and sudden cardiac arrest.A calcium-scoring CT scan is generally recommended for individuals at intermediate risk of CAD without symptoms. It includes:Men aged 40-75 and women aged 50-75: Especially those with a...
639
Acute Coronary Syndrome III: Diagnostic Studies01:30

Acute Coronary Syndrome III: Diagnostic Studies

353
Diagnosing acute coronary syndrome or ACS begins with a thorough patient history. Notable symptoms include central, crushing chest pain radiating to the left arm, neck, jaw, or back, along with shortness of breath, sweating (diaphoresis), nausea, vomiting, dizziness, and palpitations.It is crucial to note any history of cardiac illnesses and assess risk factors, including age, gender, smoking, hypertension, diabetes, hyperlipidemia, and a sedentary lifestyle.During physical examination, vital...
353
Acute Coronary Syndrome I: Introduction01:30

Acute Coronary Syndrome I: Introduction

1.3K
Acute Coronary Syndrome (ACS) encompasses a spectrum of heart conditions caused by sudden obstruction of coronary arteries, typically resulting from the rupture of an atherosclerotic plaque and subsequent thrombus (blood clot) formation. This obstruction can lead to partial or complete blockage of blood flow, causing varying degrees of myocardial ischemia or infarction.ACS includes the following clinical entities:Unstable Angina (UA)Non-ST-Elevation Myocardial Infarction (NSTEMI)ST-Elevation...
1.3K
Coronary Artery Disease I: Introduction01:30

Coronary Artery Disease I: Introduction

1.4K
Coronary Artery Disease (CAD): An Overview with Scientific InsightsCoronary Artery Disease (CAD), often referred to as C-A-D, is a prevalent blood vessel disorder classified under the broader category of atherosclerosis. Atherosclerosis is a pathological process characterized by the hardening and narrowing of arteries due to the accumulation of atherosclerotic plaques. These plaques are composed of cholesterol, fatty substances, inflammatory cells, calcium, and fibrin, reducing blood flow to...
1.4K
Blood Studies for Cardiovascular System II: CRP, Hcy, and Cardiac Natriuretic Peptide Markers01:19

Blood Studies for Cardiovascular System II: CRP, Hcy, and Cardiac Natriuretic Peptide Markers

668
Cardiac biomarkers are critical in diagnosing, prognosing, and managing cardiovascular diseases. Routine measurement of specific biomarkers such as B-type natriuretic peptide (BNP), C-reactive protein (CRP), and homocysteine (Hcy) is common practice in clinical settings to evaluate heart function and predict cardiovascular events.
These markers indicate stress or strain on the heart muscle:
Natriuretic Peptides (BNP)
Cardiac myocytes produce these hormones in response to ventricular stretching...
668
Coronary Artery Disease IV: Preventive Measures01:26

Coronary Artery Disease IV: Preventive Measures

786
Effective preventive measures for coronary artery disease (CAD) focus on controlling modifiable risk factors, including cholesterol abnormalities and lifestyle changes.Cholesterol ManagementFirst, the Mediterranean diet and the American Heart Association advocate for maintaining low-density lipoprotein (LDL) cholesterol levels below 100 mg/dL, with a more stringent recommendation of below 70 mg/dL for individuals at high risk. LDL cholesterol, often termed "bad cholesterol," can lead to the...
786

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Inhibition of Annexin A2 Facilitates PHB2-Mediated Mitophagy in Cardiomyocytes to Alleviate Cardiac Injury and Remodeling After Infarction.

Circulation·2026
Same author

Analysis of the Correlation Between Cuproptosis and Instability of Atherosclerotic Plaques.

Biomedicines·2025
Same author

Metabolic Modulation in Dilated Cardiomyopathy: From Pathophysiology to Therapy.

Reviews in cardiovascular medicine·2025
Same author

Blocking nuclear receptor Nr4a3 unlocks the senescence barrier to promote direct cardiac reprogramming.

Science advances·2025
Same author

Coupling of USP10 de-ubiquitination and chaperone-mediated autophagy causes cardiac sodium channel degradation and cardiac arrhythmias.

Cardiovascular research·2025
Same author

Cardioneuroablation for coronary artery vasospasm: a case report.

European heart journal. Case reports·2025
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

Related Experiment Video

Updated: Mar 7, 2026

Signal Acquisition, Score Interpretation, and Economics of a Non-Invasive Point-of-Care Test for Coronary Artery Disease
06:16

Signal Acquisition, Score Interpretation, and Economics of a Non-Invasive Point-of-Care Test for Coronary Artery Disease

Published on: August 9, 2024

1.0K

A Novel Risk Score Based on Lipid-Related Biomarkers for Acute Coronary Syndromes: A Multicenter Machine Learning

Jingjing Wan1,2, Yinhua Luo1,2, Yuanhong Li3

  • 1Department of Cardiology, Zhongnan Hospital of Wuhan University; Institute of Myocardial Injury and Repair, Wuhan University, 430071 Wuhan, Hubei, China.

Reviews in Cardiovascular Medicine
|March 6, 2026
PubMed
Summary
This summary is machine-generated.

This study developed a machine learning model using lipid biomarkers to predict acute coronary syndrome (ACS) in hospitalized patients. The model effectively stratified patients into risk groups, aiding early identification.

Keywords:
acute coronary syndromehigh-density lipoprotein cholesterol (HDL-C) ratiomachine learningrisk stratificationtriglyceride glucose index

More Related Videos

Coronary Progenitor Cells and Soluble Biomarkers in Cardiovascular Prognosis after Coronary Angioplasty
10:03

Coronary Progenitor Cells and Soluble Biomarkers in Cardiovascular Prognosis after Coronary Angioplasty

Published on: January 28, 2020

5.9K
Cell-free Biochemical Fluorometric Enzymatic Assay for High-throughput Measurement of Lipid Peroxidation in High Density Lipoprotein
07:29

Cell-free Biochemical Fluorometric Enzymatic Assay for High-throughput Measurement of Lipid Peroxidation in High Density Lipoprotein

Published on: October 12, 2017

9.8K

Related Experiment Videos

Last Updated: Mar 7, 2026

Signal Acquisition, Score Interpretation, and Economics of a Non-Invasive Point-of-Care Test for Coronary Artery Disease
06:16

Signal Acquisition, Score Interpretation, and Economics of a Non-Invasive Point-of-Care Test for Coronary Artery Disease

Published on: August 9, 2024

1.0K
Coronary Progenitor Cells and Soluble Biomarkers in Cardiovascular Prognosis after Coronary Angioplasty
10:03

Coronary Progenitor Cells and Soluble Biomarkers in Cardiovascular Prognosis after Coronary Angioplasty

Published on: January 28, 2020

5.9K
Cell-free Biochemical Fluorometric Enzymatic Assay for High-throughput Measurement of Lipid Peroxidation in High Density Lipoprotein
07:29

Cell-free Biochemical Fluorometric Enzymatic Assay for High-throughput Measurement of Lipid Peroxidation in High Density Lipoprotein

Published on: October 12, 2017

9.8K

Area of Science:

  • Biomedical informatics
  • Cardiovascular medicine
  • Machine learning applications

Background:

  • Acute coronary syndrome (ACS) poses a significant health challenge, necessitating improved predictive tools for hospitalized patients.
  • Early identification and risk stratification are crucial for timely intervention and management of ACS.
  • Current predictive models may benefit from integration of advanced analytical techniques and novel biomarkers.

Purpose of the Study:

  • To develop and validate an explainable machine learning (ML) model for predicting ACS in hospitalized individuals.
  • To utilize lipid-related biomarkers as key predictors within the ML model.
  • To assess the model's capability in stratifying patients into distinct risk categories (low, intermediate, high).

Main Methods:

  • Retrospective analysis of 10,127 hospitalized patients across three medical centers.
  • Development and comparison of eight ML models to predict ACS incidence.
  • Random patient cohort division into training (70%) and testing (30%) sets for model validation.
  • Utilized Light Gradient Boosting Machine (LightGBM) for its superior predictive performance.

Main Results:

  • The LightGBM model demonstrated strong predictive performance for ACS, with an area under the curve (AUC) of 0.829 in the training set.
  • The final predictive model, incorporating top 10 features including lipid markers and clinical data, achieved a C-index of 0.781 on the test set.
  • The model effectively stratified patients into low-, intermediate-, and high-risk groups, indicating significant discriminatory capacity.

Conclusions:

  • An explainable ML-based risk-stratification model using lipid biomarkers can effectively predict ACS in hospitalized patients.
  • The developed model shows promise in identifying patients at high risk for ACS, facilitating targeted clinical management.
  • Lipid-related biomarkers, when analyzed through advanced ML techniques, offer valuable insights for cardiovascular risk assessment.