Why the study?
The most appropriate model to estimate the clinical likelihood of obstructive CAD in patients with borderline ESC-PTP (5–15%) remained an evidence gap.
Does the CACS-CL model improve the estimation of obstructive CAD and avoid unnecessary testing compared to the RF-CL model in patients with stable chest pain and borderline pretest probability?
Cohort
Does the CACS-CL model improve the estimation of obstructive CAD and avoid unnecessary testing compared to the RF-CL model in patients with stable chest pain and borderline pretest probability?
The addition of coronary artery calcium score to clinical likelihood models may improve risk assessment and reduce unnecessary imaging in patients with stable chest pain and borderline pretest probability.
May avoid unnecessary testing by refining obstructive CAD likelihood in borderline cases; extends pretest probability models.
The 2019 European Society of Cardiology (ESC) guideline advocated an updated pretest probability (PTP) estimator, ESC-PTP model to facilitate decision-making in risk assessment of stable chest pain (SCP).1 For patients with borderline ESC-PTP (5–15%), clinical likelihood (CL) incorporating risk factors of coronary artery disease (CAD) other than age, sex, and type of SCP was introduced,1 but the most appropriate model to estimate CL of obstructive CAD in these patients still remains as a ‘gap in evidence’.1 Recently, Winther et al.2 developed two models for the estimation of CL: risk factor-weighted CL (RF-CL) model and coronary artery calcium score (CACS)-weighted CL (CACS-CL) model. Consequently, we aimed to validate and compare the RF-CL model and CACS-CL model and investigate whether the addition of CACS would effectively avoid unnecessary cardiovascular imaging testing (CIT) among patients with borderline ESC-PTP in a coronary computed tomography angiography (CCTA)-based SCP cohort. Details...
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Zhou et al. (2021) conducted a cohort in Stable chest pain. Coronary artery calcium score (CACS)-weighted clinical likelihood model vs. Risk factor-weighted clinical likelihood model was evaluated on Clinical likelihood of obstructive CAD and avoidance of unnecessary cardiovascular imaging testing. Coronary calcification improves the estimation for clinical likelihood of obstructive coronary artery disease and avoids unnecessary testing in patients with borderline pretest probability.
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