| Abstract: | The aim of the study was to develop simple algorithm for non-invasive estimating probability of the presence of CAD to optimize indications for cardiac catheterization. A prospective collection of clinical, electrocardiography (ECG), exercise electrocardiography (EE), dobutamine stress echocardiography (SE) and catheterization data was performed. All patients (n = 551, 65% male) complaining of chest pain, without prior history of myocardial infarction undergone EE (regarded as positive on the basis of > or = 1 mm ST-depression) SE (ischemia was defined as new or worsening wall motion abnormalities using a 16-segment model) and coronary angiography (CA): CAD was defined as > or = 50% narrowing of at least one major vessel. Two algorithms were developed with the use of probability analysis by computer program which employs Bayes' theory. They incorporated pretest variables: (age, gender, chest pain classification according to Diamond), ECG and results of one or two non-invasive test: EE and (or) SE. The studied population was divided into 3 groups on the basis of pretest likelihood of CAD: 1. low (< 10%), 2. intermediate (10-70%, in man divided into intermediate--low.: 10-29% and intermediate--high: 30-70%) and 3. high (> 70%). RESULTS: The prevalence of CAD in studied population was 61%. The sensitivity of the algorithm is 96% and specificity was 44%. Sensitivity and specificity of EE and SE was respectively: 93%, 21% and 85%, 69%). CONCLUSIONS: 1. An algorithm derived in our study is simple and may be useful in decision making that relates to CA. 2. We showed that when the likelihood of CAD is high on the basis of initial evaluation, diagnostic non-invasive testing is not indicated before CA, when the probability is intermediate or low, implementation of first choice test should be different in women (SE) and men (EE). |
