A clinical prediction model adding risk factors to age, gender, and symptoms improved discrimination for obstructive CAD over a basic model (C-statistic 0.88 vs 0.86; P<0.0001).
Cohort (n=3,903)
No
Does an updated clinical prediction model improve the prediction of obstructive CAD and prognosis compared to the modified Diamond-Forrester model in patients with suspected stable angina?
The modified Diamond-Forrester model substantially overestimates CAD likelihood in contemporary patients, whereas an updated clinical prediction model provides better diagnostic and prognostic stratification, allowing safe deferral of non-invasive testing in low-risk subgroups.
Absolute Event Rate: 0.88% vs 0.86%
p-value: p=<0.0001
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.
Reeh et al. (Mon,) conducted a cohort in Suspected stable angina (n=3,903). Clinical prediction model (age, gender, symptoms, diabetes, family history, dyslipidaemia) vs. Basic prediction model (age, gender, symptoms) was evaluated on Discrimination for obstructive CAD (C-statistic) (p=<0.0001). A clinical prediction model adding risk factors to age, gender, and symptoms improved discrimination for obstructive CAD over a basic model (C-statistic 0.88 vs 0.86; P<0.0001).