title: Establishing a prediction model for coronary angiography based on coronary risk factors.
abstract: The aim of the present study was to establish an evidence-based effective prediction model for improving the accuracy and priority for undertaking coronary angiography. The sample population consisted of 2002 coronary angiography patients. Our data were taken from claim forms provided by the Taiwanese Bureau of National Health Insurance. The results were tested using chi-square automatic interaction detection to establish a prediction model using coronary risk factors. We found significant variation across homogeneous groups, with the probabilities of developing coronary heart disease (CHD) varying according to risk factors such as sex, hypertension, diabetes, age, and physical inactivity. The study also explored the influence of interactions among patient characteristics. The sensitivity, specificity, and positive predictive value of our study were 92.0%, 35.4%, and 76.5% respectively, indicating the diagnostic accuracy of the model is at least as high as the treadmill exercise test. The results suggest that the accuracy of a decision concerning the performance of cardiac angiography can be significantly enhanced by an evidence-based effective prediction model that takes interactions between risk factors into account. This model also helps to priortize patients waiting to undergo coronary angiography.
authors.affiliation: Department of Health Service Management, China Medical University, Taichung, Taiwan, ROC.
authors.forename: Tsochiang
authors.initials: T
authors.lastname: Ma
authors.forename: Gwo-Ping
authors.initials: GP
authors.lastname: Jong
authors.forename: Kuo-Chiang
authors.initials: KC
authors.lastname: Ueng
authors.forename: Pesus
authors.initials: P
authors.lastname: Chou
mesh_terms: D000368:Aged; D016009:Chi-Square Distribution; D017023:Coronary Angiography / Q000706:statistics & numerical data*; D003327:Coronary Disease / Q000000981:diagnostic imaging / Q000453:epidemiology*; D003663:Decision Trees; D005260:Female; D006801:Humans; D008297:Male; D011237:Predictive Value of Tests; D011446:Prospective Studies; D012307:Risk Factors; D012680:Sensitivity and Specificity