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.

A simple prediction model to estimate obstructive coronary artery disease.

BACKGROUND: A simple noninvasive model to predict obstructive coronary artery disease (OCAD) may promote risk stratification and reduce the burden of coronary artery disease (CAD). This study aimed to develop pre-procedural, noninvasive prediction models that better estimate the probability of OCAD among patients with suspected CAD undergoing elective coronary angiography (CAG). METHODS: We included 1262 patients, who had reliable Framingham risk variable data, in a cohort without known CAD from a prospective registry of patients referred for elective CAG. We investigated pre-procedural OCAD (≥50% stenosis in at least one major coronary vessel based on CAG) predictors. RESULTS: A total of 945 (74.9%) participants had OCAD. The final modified Framingham scoring (MFS) model consisted of anemia, high-sensitivity C-reactive protein, left ventricular ejection fraction, and five Framingham factors (age, sex, total and high-density lipoprotein cholesterol, and hypertension). Bootstrap method (1000 times) revealed that the model demonstrated a good discriminative power (c statistic, 0.729 ± 0.0225; 95% CI, 0.69-0.77). MFS provided adequate goodness of fit (P = 0.43) and showed better performance than Framingham score (c statistic, 0.703 vs. 0.521; P < 0.001) in predicting OCAD, thereby identifying patients with high risks for OCAD (risk score ≥ 27) with ≥70% predictive value in 68.8% of subjects (range, 37.2-87.3% for low [≤17] and very high [≥41] risk scores). CONCLUSION: Our data suggested that the simple MFS risk stratification tool, which is available in most primary-level clinics, showed good performance in estimating the probability of OCAD in relatively stable patients with suspected CAD; nevertheless, further validation is needed.