Faculty Bibliography

Three years ago the biology department at Hofstra University began to offer an innovative course for premeds and biology majors. "Applications of Basic Science to Cardiovascular Medicine," taught by a practicing cardiologist, demonstrates why students must master basic science in order to understand the mechanisms and treatment of disease.

Several studies have shown that combining the change in the ST-segment with another exercise variable improves the predictive value of stress testing. However, no method has been able to combine many stress test variables with the ST-segment change simultaneously and help the clinician better predict future cardiac events. Fuzzy Cluster Analysis (FCA) was used to combine 5 stress test variables with ST-segment deviation to classify each of 232 positive outpatient stress tests as mildly, moderately, or severely abnormal. Cardiac events were recorded in these 3 patient groups up to 96 months (mean 65 months) after the stress tests. Coronary angiography was performed on 159 of these patients within 1 month of their stress tests. FCA better separated the 3 event-free survival curves than classifying the stress tests by three ST-segment (0.5-1.5 mm, 2-2.5 mm, > 3 mm) groups (p < 0.05). At 2 years, 90% of the FCA mild group were compared with 70% for the 0.5-1.5 mm group (p < 0.01). Moderate and severe tests by FCA separated patients with an intermediate from those with a poor prognosis while the 2-2.5 mm and 3 mm or more ST-segment curves did not (p < 0.05). FCA showed overall better correlation with coronary score (r = 0.71) than did the graded ST-segment groups (r = 0.48). FCA predicted both mild and high-grade (triple-vessel and left main) coronary disease better than ST-segment alone. Thus FCA better predicts future cardiac events in patients with positive stress tests than the ST-segment alone. This combined with its usefulness in predicting the extent of coronary disease provides the basis of a clinical strategy for managing patients with positive stress tests

Fuzzy set theory is useful in the analysis of data having a graded degree of abnormality. Previous studies using sharp cutoff points between normality and abnormality have resulted in general guidelines for the interpretation of positive stress tests, but do not enable the clinician to simultaneously combine several stress test variables, each having a range of abnormality. In this study, positive stress test results from 109 patients were reviewed. An angiogram recorded within 1 month of the stress tests showed that 100 patients had coronary artery disease (CAD) (15 had left main CAD, and 27 had 3-vessel, 30 had 2-vessel, and 28 had 1-vessel disease) and 9 were normal. Six variables were selected for fuzzy cluster analysis: ST-segment change, difference between resting systolic and peak exercise systolic blood pressure, total treadmill time, peak exercise heart rate as a percentage of 100% predicted maximum for age, time to onset of angina, and duration of repolarization abnormalities. The analysis used a similarity measure to compute how closely each stress test resembled a prototypical mildly, moderately, or severely abnormal stress test. Stress tests classified by this method showed better correlation with the extent of CAD than the degree of ST-segment depression alone. Unlike tests with mild degrees of ST depression (0.5 to 1.5 mm), tests classified as mild by the method virtually excluded high-grade CAD.(ABSTRACT TRUNCATED AT 250 WORDS)