A Simple Modified Framingham Scoring System to Predict Obstructive Coronary Artery Disease

Development of simple non-invasive risk prediction model would help in early prediction of coronary artery disease (CAD) reducing the burden on public health. This paper demonstrates a risk prediction scoring system to predict obstructive coronary artery disease (OCAD) in CAD patients. A total of 13,082 patients, referred for coronary angiography (CAG) in TRUST trial, were included in the development of a multivariable diagnostic prediction model. External validation of the model used 1009 patients from PRECOMIN study. The occurrence of OCAD was observed in 73.1% and 75.1% patients in TRUST (development) and PRECOMIN study (validation) cohorts, respectively. Good discrimination and calibration were obtained in both development and validation datasets (C-statistics 0.686 and 0.677; Hosmer–Lemeshow χ2 = 5.19, p = 0.74 and χ2 = 8.60, p = 0.38, respectively). The simple risk prediction model and risk scoring system developed on the basis of routine clinical variables showed good performance for estimation of OCAD in relative high-risk patients with suspected CAD.