Related Experiment Video
Updated: Aug 20, 2025
Identifying Coronary Artery Calcification on Non-gated Computed Tomography Scans
Published on: August 28, 2018
Coronary Calcium Scoring Improves Risk Prediction in Patients With Suspected Obstructive Coronary Artery Disease
Simon Winther1, Samuel E Schmidt2, Borek Foldyna3
1Department of Cardiology, Gødstrup Hospital, Herning, Denmark; Department of Clinical Medicine, Aarhus University, Aarhus, Denmark.
Insights
New clinical likelihood models using risk factors or coronary artery calcium scores significantly improve identification of obstructive coronary artery disease (CAD). These models better stratify risk for myocardial infarction and death compared to standard methods, identifying more patients who may not need further testing.
Area of Science:
- Cardiology
- Medical Diagnostics
- Risk Stratification
Background:
- Obstructive coronary artery disease (CAD) diagnosis relies on pretest probability (PTP) models.
- Risk factor-weighted clinical likelihood (RF-CL) and coronary artery calcium score-weighted clinical likelihood (CACS-CL) models offer improved identification of obstructive CAD.
Purpose of the Study:
- To assess the prognostic value of the RF-CL and CACS-CL models.
- To compare the risk stratification capabilities of RF-CL and CACS-CL models against standard PTP models for adverse cardiovascular events.
Main Methods:
- Utilized two cohorts: a Danish register (n=41,177) and a North American randomized study (n=3,952).
- Stratified incidences of myocardial infarction and death based on categories derived from RF-CL, CACS-CL, and PTP models.
- Employed Harrell's C-statistics to compare the predictive power of the three models.
Main Results:
- RF-CL and CACS-CL models down-classified a higher percentage of patients to a likelihood ≤5% of CAD (45% and 60%, respectively) compared to the PTP model (18%).
- Annualized event rates for myocardial infarction and death were low across all models, with RF-CL (0.51%) and CACS-CL (0.48%) showing slightly higher rates than PTP (0.37%).
- RF-CL (C-statistic=0.64) and CACS-CL (C-statistic=0.69) demonstrated superior predictive power compared to the PTP model (C-statistic=0.61).
Conclusions:
- Clinical likelihood models incorporating risk factors or CACS improve risk stratification for myocardial infarction and death compared to standard PTP models.
- Optimized RF-CL and CACS-CL models identify significantly more patients who may not benefit from further diagnostic testing (2.5 and 3.3 times more, respectively).
- These enhanced models provide better prognostic value in patients with suspected obstructive CAD.
Background:
In patients with suspected obstructive coronary artery disease (CAD), the risk factor-weighted clinical likelihood (RF-CL) model and the coronary artery calcium score-weighted clinical likelihood (CACS-CL) model improves the identification of obstructive CAD compared with basic pretest probability (PTP) models.
Objectives:
The aim of this study was to assess the prognostic value of the new models.
Methods:
The incidences of myocardial infarction and death were stratified according to categories by the RF-CL and CACS-CL and compared with categories by the PTP model. We used cohorts from a Danish register (n = 41,177) and a North American randomized study (n = 3,952). All patients were symptomatic and were referred for diagnostic testing because of clinical indications.
Results:
Despite substantial down-reclassification of patients to a likelihood ≤5% of CAD with either the RF-CL (45%) or CACS-CL (60%) models compared with the PTP (18%), the annualized event rates of myocardial infarction and death were low using all 3 models; RF-CL 0.51% (95% CI: 0.46-0.56), CACS-CL 0.48% (95% CI: 0.44-0.56), and PTP 0.37% (95% CI: 0.31-0.44), respectively. Overall, comparison of the predictive power of the 3 models using Harrell's C-statistics demonstrated superiority of the RF-CL (0.64 [95% CI: 0.63-0.65]) and CACS-CL (0.69 [95% CI: 0.67-0.70]) compared with the PTP model (0.61 [95% CI: 0.60-0.62]).
Conclusions:
The simple clinical likelihood models that include classical risk factors or risk factors combined with CACS provide improved risk stratification for myocardial infarction and death compared with the standard PTP model. Hence, the optimized RF-CL and CACS-CL models identify 2.5 and 3.3 times more patients, respectively, who may not benefit from further diagnostic testing.
More Related Videos
06:57Author Spotlight: Advancing Cardiovascular Imaging - Introducing the Spatially Weighted Calcium Score for Early Disease Detection
Published on: September 22, 2023
06:16Signal Acquisition, Score Interpretation, and Economics of a Non-Invasive Point-of-Care Test for Coronary Artery Disease
Published on: August 9, 2024
Related Concept Videos
01:25Imaging Studies for Cardiovascular System VI: Calcium -Scoring CT
01:28Imaging Studies for Cardiovascular System V: CT
01:30Acute Coronary Syndrome III: Diagnostic Studies
01:20Imaging Studies for Cardiovascular System III: X-Ray
Definition and Purpose
An X-ray, or radiograph, is a non-invasive method that uses ionizing radiation to take images of internal structures. It is mainly used in cardiac imaging to examine the heart, lungs, and major blood vessels, aiming to identify abnormalities in the heart's size, shape, and position, such as heart failure, congenital defects, and vascular...
01:30Coronary Artery Disease I: Introduction
01:26Coronary Artery Disease IV: Preventive Measures