Fuzzy Cluster Analysis: A New Method to Predict Future Cardiac Events in Patients With Positive Stress Tests

Abstract

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.