OBJECTIVE: To compare the performance of five risk models (Diamond-Forrester, the updated Diamond-Forrester, Morise, Duke, and a new model designated COronary Risk SCORE (CORSCORE) in predicting significant coronary artery disease (CAD) in patients with chest pain suggestive of stable angina pectoris. METHODS: Retrospective cohort for creation of CORSCORE by means of logistic regression analysis. Prospective cohort for validation of the five risk models using receiver operating characteristics (ROC) curve analysis, net reclassification improvement (NRI), and integrated discrimination improvement (IDI). Significant CAD was defined as lumen area diameter reduction ≥50% at coronary angiography. All risk models include information on age, sex, and symptoms. In addition the Duke, Morise, and CORSCORE models include information on tobacco use and hypercholesterolemia. Duke and Morise also include information on diabetes. History of myocardial infarction is used by the Duke and CORSCORE models whereas hypertension is included in the Morise and CORSCORE models. The Duke model includes information on electrocardiogram (ECG) changes and the Morise model includes information on family history, body mass index, obesity, and oestrogen status. RESULTS: 4781 retrospective and 633 prospective consecutive patients referred for coronary angiography were included. The area under the ROC for the updated Diamond-Forrester, Duke, and CORSCORE was significantly larger than for the Diamond-Forrester (p≤0.001). The IDI was significantly higher for the Duke as compared to all other models (p≤0.006). CONCLUSION: The Duke, updated Diamond-Forrester, and CORSCORE risk models are most efficient in predicting CAD in a contemporary cohort of patients with symptoms suggestive of angina. The updated Diamond-Forrester may most operational in daily clinical practice since it is calculated from the lowest number of clinical variables.
OBJECTIVE: To compare the performance of five risk models (Diamond-Forrester, the updated Diamond-Forrester, Morise, Duke, and a new model designated COronary Risk SCORE (CORSCORE) in predicting significant coronary artery disease (CAD) in patients with chest pain suggestive of stable angina pectoris. METHODS: Retrospective cohort for creation of CORSCORE by means of logistic regression analysis. Prospective cohort for validation of the five risk models using receiver operating characteristics (ROC) curve analysis, net reclassification improvement (NRI), and integrated discrimination improvement (IDI). Significant CAD was defined as lumen area diameter reduction ≥50% at coronary angiography. All risk models include information on age, sex, and symptoms. In addition the Duke, Morise, and CORSCORE models include information on tobacco use and hypercholesterolemia. Duke and Morise also include information on diabetes. History of myocardial infarction is used by the Duke and CORSCORE models whereas hypertension is included in the Morise and CORSCORE models. The Duke model includes information on electrocardiogram (ECG) changes and the Morise model includes information on family history, body mass index, obesity, and oestrogen status. RESULTS: 4781 retrospective and 633 prospective consecutive patients referred for coronary angiography were included. The area under the ROC for the updated Diamond-Forrester, Duke, and CORSCORE was significantly larger than for the Diamond-Forrester (p≤0.001). The IDI was significantly higher for the Duke as compared to all other models (p≤0.006). CONCLUSION: The Duke, updated Diamond-Forrester, and CORSCORE risk models are most efficient in predicting CAD in a contemporary cohort of patients with symptoms suggestive of angina. The updated Diamond-Forrester may most operational in daily clinical practice since it is calculated from the lowest number of clinical variables.
Authors: James J Jang; Manjushri Bhapkar; Adrian Coles; Sreekanth Vemulapalli; Christopher B Fordyce; Kerry L Lee; James E Udelson; Udo Hoffmann; Jean-Claude Tardif; W Schuyler Jones; Daniel B Mark; Vincent L Sorrell; Andrey Espinoza; Pamela S Douglas; Manesh R Patel Journal: Circ Cardiovasc Imaging Date: 2019-02 Impact factor: 7.792
Authors: Tessa S S Genders; Adrian Coles; Udo Hoffmann; Manesh R Patel; Daniel B Mark; Kerry L Lee; Ewout W Steyerberg; M G Myriam Hunink; Pamela S Douglas Journal: JACC Cardiovasc Imaging Date: 2017-06-14
Authors: Peter McKavanagh; Lisa Lusk; Peter A Ball; Tom R Trinick; Ellie Duly; Gerard M Walls; Clare Orr; Mark T Harbinson; Patrick M Donnelly Journal: Int J Cardiovasc Imaging Date: 2013-06-04 Impact factor: 2.357
Authors: Simon Winther; Samuel Emil Schmidt; Niels Ramsing Holm; Egon Toft; Johannes Jan Struijk; Hans Erik Bøtker; Morten Bøttcher Journal: Int J Cardiovasc Imaging Date: 2015-09-03 Impact factor: 2.357