Background: Substantial differences exist between different guideline-recommended pretest probability (PTP) models for the detection of obstructive coronary artery disease (CAD). This study was... Show moreBackground: Substantial differences exist between different guideline-recommended pretest probability (PTP) models for the detection of obstructive coronary artery disease (CAD). This study was performed to study the performance of the 2021 American Heart Association/American College of Cardiology (AHA/ACC) guideline-recommended PTP (AHA/ACC-PTP) model in assessing the likelihood of obstructive CAD compared with previously proposed models. Methods and Results: Symptomatic patients (N=50 561) referred for coronary computed tomography angiography were included. The reference standard was invasive coronary angiography with optional fractional flow reserve measurements. The AHA/ACC-PTP values based on sex and age were calculated and compared with the 2019 European Society of Cardiology guideline PTP values based on sex, age, and symptoms as well as the risk factor-weighted clinical likelihood values based on sex, age, symptoms, and risk factors. The AHA/ACC-PTP maximum values overestimated by a factor of 2.6 the actual prevalence of CAD. Compared with the AHA/ACC-PTP model (area under the receiver-operating curve, 71.5 [95% CI, 70.7-72.2]), inclusion of typicality of symptoms in the European Society of Cardiology guideline PTP improved discrimination of CAD (area under the receiver-operating curve, 75.5 [95% CI, 74.7-76.3]). Inclusion of both symptoms and risk factors in the risk factor-weighted clinical likelihood model further improved discrimination (area under the receiver-operating curve, 77.7 [95% CI, 77.0-78.5]). The proportion of patients classified as very low PTP was lower using the AHA/ACC-PTP (5%) compared with the European Society of Cardiology guideline PTP (19%) and the risk factor-weighted clinical likelihood (49%) models. Conclusions: The new AHA/ACC-PTP model overestimates the prevalence of obstructive CAD substantially if type of symptoms and risk factors are not taken into account. Inclusion of both symptoms and risk factors improves model performance and identifies more patients with very low likelihood of CAD in whom further testing can be deferred. Show less
BACKGROUND The prevalence of obstructive coronary artery disease (CAD) in symptomatic patients referred for diagnostic testing has declined, warranting optimization of individualized diagnostic... Show moreBACKGROUND 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. (C) 2020 by the American College of Cardiology Foundation. Show less
