1.
Epicardial Fat Volume Improves the Prediction of Obstructive Coronary Artery Disease Above Traditional Risk Factors and Coronary Calcium Score.
Zhou, J, Chen, Y, Zhang, Y, Wang, H, Tan, Y, Liu, Y, Huang, L, Zhang, H, Ma, Y, Cong, H
Circulation. Cardiovascular imaging. 2019;(1):e008002
Abstract
BACKGROUND Recent studies have demonstrated the tremendous potential of epicardial fat volume (EFV) to predict obstructive coronary artery disease. We aimed to develop a new model to estimate pretest probability of obstructive coronary artery disease using traditional risk factors with coronary calcium score and EFV and compare it with proposed models in Chinese patients who underwent coronary computed tomography angiography. METHODS The new models were derived from 5743 consecutive patients using multivariate logistic regression and validated in an internal cohort using invasive coronary angiography as the outcome and an external cohort with clinical outcome data. Hosmer-Lemeshow goodness-of-fit test, area under the receiver operating characteristic curve, integrated discrimination improvement and net reclassification improvement were calculated to validate and compare the performance of models. RESULTS EFV improved prediction above conventional risk factors and coronary calcium score (area under the receiver operating characteristic curve increased from 0.856 to 0.874, integrated discrimination improvement 0.0487, net reclassification improvement 0.1181, P<0.0001 for all). The final model included 5 predictors: age, sex, symptom, coronary calcium score, and EFV. Good internal validation and external validation of the new model were achieved, with positive net reclassification improvement and integrated discrimination improvement, excellent area under the receiver operating characteristic curve and favorable calibration. Further, the new model demonstrated a better prediction of clinical outcome, resulting in a more cost-effective risk stratification to optimize decision-making of downstream diagnosis and treatment. CONCLUSIONS Addition of EFV to conventional risk factors and coronary calcium score offered a more accurate and effective estimation for pretest probability of obstructive coronary artery disease, which may help to improve initial management of stable chest pain.
2.
Validation and comparison of four models to calculate pretest probability of obstructive coronary artery disease in a Chinese population: A coronary computed tomographic angiography study.
Zhou, J, Liu, Y, Huang, L, Tan, Y, Li, X, Zhang, H, Ma, Y, Zhang, Y
Journal of cardiovascular computed tomography. 2017;(4):317-323
Abstract
OBJECTIVE We sought to compare the performance of the updated Diamond-Forrester method (UDFM), Duke clinical score (DCS), Genders clinical model (GCM) and Genders extended model (GEM) in a Chinese population referred to coronary computed tomography angiography (coronary CTA). BACKGROUND The reliability of existing models to calculate the pretest proability (PTP) of obstructive coronary artery disease (CAD) have not been fully investigated, especially in a Chinese population. METHODS We identified 5743 consecutive patients with suspected stable CAD who underwent coronary calcium scoring (CCS) and coronary CCTA. Obstructive CAD was defined as with the presence of ≥50% diameter stenosis in coronary CTA or unassessable segments due to severe calcification. Area under the receiver operating characteristic curve (AUC), integrated discrimination improvement (IDI), net reclassification improvement (NRI) and Hosmer-Lemeshow goodness-of-fit statistic (H-L χ2) were assessed to validate and compare these models. RESULTS Overall, 1872 (32%) patients had obstructive CAD and 2467 (43%) had a CCS of 0. GEM demonstrated improved discrimination over the other models through the largest AUC (0.816 for GEM, 0.774 for GCM, 0.772 for DCS and 0.765 for UDFM). UDFM (-0.3255, p < 0.0001), DCS (-0.3149, p < 0.0001) and GCM (-0.2264, p < 0.0001) showed negative IDI compared to GEM. The NRI was significantly higher for GEM than the other models (0.7152, p < 0.0001, 0.5595, p < 0.0001 and 0.3195, p < 0.0001, respectively). All of the four models overestimated the prevalence of obstructive CAD, with unsatisfactory (p < 0.01 for all) calibration for UDFM (H-L χ2 = 137.82), DCS (H-L χ2 = 156.70), GCM (H-L χ2 = 51.17) and GEM (H-L χ2 = 29.67), respectively. CONCLUSION Although GEM was superior for calculating PTP in a Chinese population referred for coronary CTA, developing new models allowing for more accurate and operational estimation are warranted.