Jia Zhou1,2, Yundai Chen3, Ying Zhang1, Hao Wang4,5, Yahang Tan3,6, Yujie Liu1, Lingyu Huang2, Hong Zhang7, Yanhe Ma7, Hongliang Cong1,2. 1. Department of Cardiology (J.Z., Y.Z., Y.L., H.C.), Tianjin Chest Hospital, China. 2. Institute of Cardiovascular Diseases (J.Z., L.H., H.C.), Tianjin Chest Hospital, China. 3. Department of Cardiology, General Hospital of Chinese People's Liberation Army (Y.C., Y.T.). 4. Beijing Key Laboratory of Clinical Epidemiology, School of Public Health (H.W.). 5. School of Medical and Health Sciences, Edith Cowan University, Perth, Australia (H.W.). 6. Department of Cardiology, Beijing Chaoyang Hospital, Capital Medical University, China (Y.T.). 7. Department of Radiology (H.Z., Y.M.), Tianjin Chest Hospital, China.
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.
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.
Authors: Regitse H Christensen; Bernt Johan von Scholten; Christian S Hansen; Magnus T Jensen; Tina Vilsbøll; Peter Rossing; Peter G Jørgensen Journal: Cardiovasc Diabetol Date: 2019-08-30 Impact factor: 9.951
Authors: Satish Kenchaiah; Jingzhong Ding; J Jeffrey Carr; Matthew A Allison; Matthew J Budoff; Russell P Tracy; Gregory L Burke; Robyn L McClelland; Andrew E Arai; David A Bluemke Journal: J Am Coll Cardiol Date: 2021-06-01 Impact factor: 27.203