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Incorporating Coronary Calcification Into Pre-Test Assessment of the Likelihood of Coronary Artery Disease
Simon Winther1, Samuel Emil Schmidt2, Thomas Mayrhofer3
1Department of Cardiology, Gødstrup Hospital, Herning, Denmark.
Insights
A new tool combining clinical risk factors and coronary artery calcium score (CACS) improves prediction of obstructive coronary artery disease (CAD). This helps identify patients with low likelihood of CAD who may not require further testing.
Area of Science:
- Cardiology
- Diagnostic Imaging
- Preventive Medicine
Background:
- Declining prevalence of obstructive coronary artery disease (CAD) in symptomatic patients necessitates optimized diagnostic strategies.
- Current diagnostic approaches require refinement for individualized patient assessment.
Purpose of the Study:
- To develop and validate a simple, clinically applicable tool for estimating obstructive CAD likelihood.
- The tool integrates pre-test probability (PTP) models with clinical risk factors and coronary artery calcium score (CACS).
Main Methods:
- Development of risk factor-weighted clinical likelihood (RF-CL) and CACS-weighted clinical likelihood (CACS-CL) models in a large cohort (n=41,177).
- Validation of the models in independent European and North American cohorts (n=15,411).
- Comparison of model performance against an updated PTP table.
Main Results:
- RF-CL and CACS-CL models demonstrated superior prediction accuracy for obstructive CAD compared to the PTP model in validation cohorts.
- The CACS-CL model significantly increased the area under the receiver-operating characteristic curve (AUC) to 85 (95% CI: 84-86).
- The CACS-CL model reclassified 54% of patients to a low likelihood of CAD, compared to 11% with the PTP model.
Conclusions:
- A straightforward tool incorporating risk factors and CACS enhances prediction and discrimination of obstructive CAD in suspected cases.
- This tool effectively reclassifies patients, identifying those with low CAD likelihood who may avoid further diagnostic procedures.
Background:
The prevalence of obstructive coronary artery disease (CAD) in symptomatic patients referred for diagnostic testing has declined, warranting optimization of individualized diagnostic strategies.
Objectives:
This study sought to present a simple, clinically applicable tool enabling estimation of the likelihood of obstructive CAD by combining a pre-test probability (PTP) model (Diamond-Forrester approach using sex, age, and symptoms) with clinical risk factors and coronary artery calcium score (CACS).
Methods:
The new tool was developed in a cohort of symptomatic patients (n = 41,177) referred for diagnostic testing. The risk factor-weighted clinical likelihood (RF-CL) was calculated through PTP and risk factors, while the CACS-weighted clinical likelihood (CACS-CL) added CACS. The 2 calculation models were validated in European and North American cohorts (n = 15,411) and compared with a recently updated PTP table.
Results:
The RF-CL and CACS-CL models predicted the prevalence of obstructive CAD more accurately in the validation cohorts than the PTP model, and markedly increased the area under the receiver-operating characteristic curves of obstructive CAD: for the PTP model, 72 (95% confidence intervals [CI]: 71 to 74); for the RF-CL model, 75 (95% CI: 74 to 76); and for the CACS-CL model, 85 (95% CI: 84 to 86). In total, 38% of the patients in the RF-CL group and 54% in the CACS-CL group were categorized as having a low clinical likelihood of CAD, as compared with 11% with the PTP model.
Conclusions:
A simple risk factor and CACS-CL tool enables improved prediction and discrimination of patients with suspected obstructive CAD. The tool empowers reclassification of patients to low likelihood of CAD, who need no further testing.
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