AUTHOR=Chen Tao , Shao Dujing , Zhao Jia , Xiu Mingwen , Li Yaoshuang , He Miao , Tan Yahang , An Yanchun , Zhang Xiangchen , Zhao Jia , Zhou Jia 

TITLE=Comparison of the RF-CL and CACS-CL models to estimate the pretest probability of obstructive coronary artery disease and predict prognosis in patients with stable chest pain and diabetes mellitus

JOURNAL=Frontiers in Cardiovascular Medicine

VOLUME=Volume 11 - 2024

YEAR=2024

URL=https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2024.1368743

DOI=10.3389/fcvm.2024.1368743

ISSN=2297-055X

ABSTRACT=The most appropriate tool to estimate pretest probability (PTP) of obstructive coronary artery disease (CAD) in patients with diabetes mellitus (DM) and stable chest pain (SCP) remains unknown. Thus, we sought to validate and compare two recent models, risk factor-weighted clinical likelihood (RF-CL) model and coronary artery calcium score (CACS)-weighted clinical likelihood (CACS-CL) model in these patients.Methods 1245 symptomatic patients with DM, who underwent CACS and coronary computed tomographic angiography (CCTA) scan, were identified and followed. PTP of obstructive CAD for each patient was estimated according to RF-CL model and CACS-CL model, respectively. Area under the receiver operating characteristic curve (AUC), net reclassification improvement (NRI) and integrated discrimination improvement (IDI) was used to assess the performance of models. The associations of major adverse cardiovascular event (MACE) with risk groups were evaluated by Cox proportional hazards regression.Compared to RF-CL model, CACS-CL model revealed a larger AUC (0.856 vs 0.782, p = 0.0016), positive IDI (12%, p < 0.0001) and NRI (34%, p < 0.0001), stronger association to MACE (hazard ratio: 0.26 vs 0.38) and less discrepancy between observed and predicted probabilities, resulting in a more effective risk assessment to optimize downstream clinical management.Among patients with DM and SCP, the addition of CACS in CACS-CL model provided a more accurate estimation for PTP and prediction of MACE. The application of CACS-CL model, instead of RF-CL model, might have more potential to avoid unnecessary and omissive cardiovascular imaging testing with minimal cost.