A New Integrated Clinical-Biohumoral Model to Predict Functionally Significant Coronary Artery Disease in Patients With Chronic Chest Pain

Abstract Background In patients with chronic angina-like chest pain, the probability of coronary artery disease (CAD) is estimated by symptoms, age, and sex according to the Genders clinical model. We investigated the incremental value of circulating biomarkers over the Genders model to predict functionally significant CAD in patients with chronic chest pain. Methods In 527 patients (60.4 years, standard deviation, 8.9 years; 61.3% male participants) enrolled in the European Ev aluation of In tegrated Cardiac I maging (EVINCI) study, clinical and biohumoral data were collected. Results Functionally significant CAD—ie, obstructive coronary disease seen at invasive angiography causing myocardial ischemia at stress imaging or associated with reduced fractional flow reserve (FFR P P  = 0.04). Cross-validation of the ROC analysis confirmed the discrimination ability of the new model (AUC, 0.66). As many as 56% of patients who were assigned to a higher pretest probability by the Genders model were correctly reassigned to a low probability class ( Conclusions The Genders model has a low accuracy for predicting functionally significant CAD. A new model integrating HDL cholesterol, AST, and hs-CRP levels with common clinical variables has a higher predictive accuracy for functionally significant CAD and allows the reclassification of patients from an intermediate/high to a low pretest likelihood of CAD.