AIMS: We hypothesized that the modified Diamond-Forrester (D-F) prediction model overestimates probability of coronary artery disease (CAD). The aim of this study was to update the prediction model based on pre-test information and assess the model's performance in predicting prognosis in an unselected, contemporary population suspected of angina. METHODS AND RESULTS: We included 3903 consecutive patients free of CAD and heart failure and suspected of angina, who were referred to a single centre for assessment in 2012-15. Obstructive CAD was defined from invasive angiography as lesion requiring revascularization, >70% stenosis or fractional flow reserve <0.8. Patients were followed (mean follow-up 33 months) for myocardial infarction, unstable angina, heart failure, stroke, and death. The updated D-F prediction model overestimated probability considerably: mean pre-test probability was 31.4%, while only 274 (7%) were diagnosed with obstructive CAD. A basic prediction model with age, gender, and symptoms demonstrated good discrimination with C-statistics of 0.86 (95% CI 0.84-0.88), while a clinical prediction model adding diabetes, family history, and dyslipidaemia slightly improved the C-statistic to 0.88 (0.86-0.90) (P for difference between models <0.0001). Quartiles of probability of CAD from the clinical prediction model provided good diagnostic and prognostic stratification: in the lowest quartiles there were no cases of obstructive CAD and cumulative risk of the composite endpoint was less than 3% at 2 years. CONCLUSION: The pre-test probability model recommended in current ESC guidelines substantially overestimates likelihood of CAD when applied to a contemporary, unselected, all-comer population. We provide an updated prediction model that identifies subgroups with low likelihood of obstructive CAD and good prognosis in which non-invasive testing may safely be deferred. Published on behalf of the European Society of Cardiology. All rights reserved.
AIMS: We hypothesized that the modified Diamond-Forrester (D-F) prediction model overestimates probability of coronary artery disease (CAD). The aim of this study was to update the prediction model based on pre-test information and assess the model's performance in predicting prognosis in an unselected, contemporary population suspected of angina. METHODS AND RESULTS: We included 3903 consecutive patients free of CAD and heart failure and suspected of angina, who were referred to a single centre for assessment in 2012-15. Obstructive CAD was defined from invasive angiography as lesion requiring revascularization, >70% stenosis or fractional flow reserve <0.8. Patients were followed (mean follow-up 33 months) for myocardial infarction, unstable angina, heart failure, stroke, and death. The updated D-F prediction model overestimated probability considerably: mean pre-test probability was 31.4%, while only 274 (7%) were diagnosed with obstructive CAD. A basic prediction model with age, gender, and symptoms demonstrated good discrimination with C-statistics of 0.86 (95% CI 0.84-0.88), while a clinical prediction model adding diabetes, family history, and dyslipidaemia slightly improved the C-statistic to 0.88 (0.86-0.90) (P for difference between models <0.0001). Quartiles of probability of CAD from the clinical prediction model provided good diagnostic and prognostic stratification: in the lowest quartiles there were no cases of obstructive CAD and cumulative risk of the composite endpoint was less than 3% at 2 years. CONCLUSION: The pre-test probability model recommended in current ESC guidelines substantially overestimates likelihood of CAD when applied to a contemporary, unselected, all-comer population. We provide an updated prediction model that identifies subgroups with low likelihood of obstructive CAD and good prognosis in which non-invasive testing may safely be deferred. Published on behalf of the European Society of Cardiology. All rights reserved.
Authors: Vijay Kunadian; Alaide Chieffo; Paolo G Camici; Colin Berry; Javier Escaned; Angela H E M Maas; Eva Prescott; Nicole Karam; Yolande Appelman; Chiara Fraccaro; Gill Louise Buchanan; Stephane Manzo-Silberman; Rasha Al-Lamee; Evelyn Regar; Alexandra Lansky; J Dawn Abbott; Lina Badimon; Dirk J Duncker; Roxana Mehran; Davide Capodanno; Andreas Baumbach Journal: Eur Heart J Date: 2020-10-01 Impact factor: 29.983
Authors: Zhen Sinead Wang; Jonathan Yap; Yi Ling Eileen Koh; Shaw Yang Chia; N Nivedita; Teck Wee Andrew Ang; Soo Chye Paul Goh; Cia Sin Lee; Lee Lim Joanna Tan; Chai Wah Ooi; Matthew Seow; Khung Keong Yeo; Siang Jin Terrance Chua; Ngiap Chuan Tan Journal: J Gen Intern Med Date: 2021-03-26 Impact factor: 6.473