Prediction of obstructive coronary artery disease and prognosis in patients with suspected stable angina. (18th December 2018)

• Record Type: Journal Article
• Authors: Reeh, Jacob; Therming, Christina Bachmann; Heitmann, Merete; Højberg, Søren; Sørum, Charlotte; Bech, Jan; Husum, Dorte; Dominguez, Helena; Sehestedt, Thomas; Hermann, Thomas; Hansen, Kim Wadt; Simonsen, Lene; Galatius, Søren; Prescott, Eva
• Is Part Of: European heart journal. Volume 40:Number 18(2019)
• Page Start: 1426
• Page End: 1435
• Publication Date: 2018-12-18
• Subjects: Angina pectoris -- Stable coronary artery disease -- Prediction model -- Stable chest pain -- Pre-test probability; Cardiology -- Periodicals; Heart -- Diseases -- Periodicals; 616.12005
• DOI: 10.1093/eurheartj/ehy806
• Languages: English
• ISSNs: 0195-668X

Abstract

Abstract: 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.