Related Experiment Video
Updated: Apr 11, 2026
Signal Acquisition, Score Interpretation, and Economics of a Non-Invasive Point-of-Care Test for Coronary Artery Disease
Published on: August 9, 2024
A New Integrated Clinical-Biohumoral Model to Predict Functionally Significant Coronary Artery Disease in Patients
Chiara Caselli1, Daniele Rovai1, Valentina Lorenzoni2
1Institute of Clinical Physiology, National Research Council, Pisa, Italy.
Insights
The Genders clinical model poorly predicts coronary artery disease (CAD). Integrating high-density lipoprotein (HDL) cholesterol, aspartate aminotransferase (AST), and high-sensitivity C-reactive protein (hs-CRP) improves CAD prediction accuracy.
Area of Science:
- Cardiology
- Biomarkers
- Diagnostic Accuracy
Background:
- Chronic angina-like chest pain is common.
- The Genders clinical model estimates coronary artery disease (CAD) probability using symptoms, age, and sex.
- There is a need to improve CAD prediction in patients with chronic chest pain.
Purpose of the Study:
- To investigate the incremental value of circulating biomarkers over the Genders model.
- To predict functionally significant CAD in patients with chronic chest pain.
Main Methods:
- 527 patients with chronic chest pain were enrolled in the EVINCI study.
- Clinical and biohumoral data were collected.
- 31 biomarkers were analyzed alongside clinical variables.
Main Results:
- Functionally significant CAD was present in 15.2% of patients.
- High-density lipoprotein (HDL) cholesterol, aspartate aminotransferase (AST), and high-sensitivity C-reactive protein (hs-CRP) were independent predictors of CAD.
- The integrated model (biomarkers + clinical variables) outperformed the Genders model (AUC 0.70 vs 0.58).
- The new model correctly reclassified 56% of patients from intermediate/high to low pretest probability.
Conclusions:
- The Genders model has low accuracy for predicting functionally significant CAD.
- A new model integrating HDL cholesterol, AST, and hs-CRP with clinical variables demonstrates higher predictive accuracy.
- This integrated model aids in reclassifying patients' pretest likelihood of CAD.
Background:
In patients with chronic angina-like chest pain, the probability of coronary artery disease (CAD) is estimated by symptoms, age, and sex according to the Genders clinical model. We investigated the incremental value of circulating biomarkers over the Genders model to predict functionally significant CAD in patients with chronic chest pain.
Methods:
In 527 patients (60.4 years, standard deviation, 8.9 years; 61.3% male participants) enrolled in the European Evaluation of Integrated Cardiac Imaging (EVINCI) study, clinical and biohumoral data were collected.
Results:
Functionally significant CAD-ie, obstructive coronary disease seen at invasive angiography causing myocardial ischemia at stress imaging or associated with reduced fractional flow reserve (FFR < 0.8), or both-was present in 15.2% of patients. High-density lipoprotein (HDL) cholesterol, aspartate aminotransferase (AST) levels, and high-sensitivity C-reactive protein (hs-CRP) were the only independent predictors of disease among 31 biomarkers analyzed. The model integrating these biohumoral markers with clinical variables outperformed the Genders model by receiver operating characteristic curve (ROC) (area under the curve [AUC], 0.70 [standard error (SE), 0.03] vs 0.58 [SE, 0.03], respectively, P < 0.001) and reclassification analysis (net reclassification improvement, 0.15 [SE, 0.07]; P = 0.04). Cross-validation of the ROC analysis confirmed the discrimination ability of the new model (AUC, 0.66). As many as 56% of patients who were assigned to a higher pretest probability by the Genders model were correctly reassigned to a low probability class (< 15%) by the new integrated model.
Conclusions:
The Genders model has a low accuracy for predicting functionally significant CAD. A new model integrating HDL cholesterol, AST, and hs-CRP levels with common clinical variables has a higher predictive accuracy for functionally significant CAD and allows the reclassification of patients from an intermediate/high to a low pretest likelihood of CAD.
More Related Videos
10:03Coronary Progenitor Cells and Soluble Biomarkers in Cardiovascular Prognosis after Coronary Angioplasty
Published on: January 28, 2020
08:51Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
Published on: September 20, 2024
Related Concept Videos
01:30Acute Coronary Syndrome III: Diagnostic Studies
01:25Imaging Studies for Cardiovascular System VI: Calcium -Scoring CT
01:30Coronary Artery Disease I: Introduction
01:19Acute Coronary Syndrome II: Pathophysiology and Clinical Manifestations
01:30Coronary Artery Disease III: Clinical Manifestations
01:26Coronary Artery Disease II: Pathophysiology