<![CDATA[Introduction. In patient with stable coronary artery disease (CAD), severity of stenosis is closely related to prognosis. It is known that several clinical parameters and recently-developed strain echocardiography can predict severity of stenosis. Assessment of clinical parameters, altogether with strain echocardiography is expected to make better prediction. This study aim to determine whether clinical factors, i.e. age, sex, diabetes, typical angina, and history of myocardial infarction, and strain echocardiography parameter, i.e. global longitudinal strain (GLS), can predict severity of coronary artery stenosis measured with Gensini score,and to further develop a prediction model based on significant parameters. Methods. This is a cross-sectional study taken at Dr. Cipto Mangunkusumo National Central General Hospital during period March – May 2019. Patient with stable CAD scheduled to undergo coronary angiography is recruited consecutively. Bivariate analysis using chi-square is performed to each predictor. Significant predictors are further analysed using backward stepwise logistic regression. A prediction model is then developed based on significant predictors by multivariate analysis. Results. The study group include 93 subjects. Significant predictors on bivariate analysis include diabetes melitus (OR 2.79; 95% CI:1.08-7.23), history of myocardial infartion (OR 4.04; 95% CI:1.51-10.80), typical angina (OR 5.01; 95% CI:1.91-13.14), and GLS ≥-18.8 (OR 30.51; 95% CI:10.38-89.72). Significant predictors on multivariate analysis are typical angina (OR 4.48; 95% CI:1.39-14.47) and GLS ≥18.8 (OR 17.30; 95% CI:5.38-55.66). Predicton model is not developed because there are only two significant predictors. Conclusions. Typical angina and GLS are predictors of stenosis severity in patient with stable CAD. Age, sex, diabetes, and history of myocardial infarction are not significant predictors. A prediction model can not developed because there are only 2 significant predictors.]]>
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