This Article Full Text Full Text (PDF) All Versions of this Article: 106/6/685    most recent 01.CIR.0000024410.15081.FDv1 Alert me when this article is cited Alert me if a correction is posted Citation Map Services Email this article to a friend Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Request Permissions Citing Articles Citing Articles via Google Scholar Google Scholar Articles by Lauer, M. S. Articles by Hammer, P. L. Search for Related Content PubMed PubMed Citation Articles by Lauer, M. S. Articles by Hammer, P. L. Related Collections Exercise testing Epidemiology

(Circulation. 2002;106:685.)
© 2002 American Heart Association, Inc.

Clinical Investigation and Reports

Use of the Logical Analysis of Data Method for Assessing Long-Term Mortality Risk After Exercise Electrocardiography

Michael S. Lauer, MD; Sorin Alexe, MS; Claire E. Pothier Snader, MA; Eugene H. Blackstone, MD; Hemant Ishwaran, ScD; Peter L. Hammer, PhD

From the Departments of Cardiology (M.S.L, C.E.S.P.), Cardiothoracic Surgery (E.H.B.), and Epidemiology and Biostatistics (E.H.B., H.I.) of the Cleveland Clinic Foundation, Cleveland, Ohio; and the Center for Operations Research (RUTCOR) (S.A., P.L.H.), Rutgers University, Piscataway, NJ.

Correspondence to Michael S. Lauer, MD, FACC, Director of Clinical Research, Department of Cardiology, Cleveland Clinic Foundation, Desk F25, 9500 Euclid Ave, Cleveland, OH 44195. E-mail Lauerm{at}ccf.org

Background— Logical Analysis of Data is a methodology of mathematical optimization on the basis of the systematic identification of patterns or "syndromes." In this study, we used Logical Analysis of Data for risk stratification and compared it to regression techniques.

Methods and Results— Using a cohort of 9454 patients referred for exercise testing, Logical Analysis of Data was applied to identify syndromes based on 20 variables. High-risk syndromes were patterns of up to 3 findings associated with >5-fold increase in risk of death, whereas low-risk syndromes were associated with >5-fold decrease. Syndromes were derived on a randomly derived training set of 4722 patients and validated in 4732 others. There were 15 high-risk and 26 low-risk syndromes. A risk score was derived based on the proportion of possible high risk and low risk syndromes present. A value 0, meaning the same or a greater proportion of high-risk syndromes, was noted in 979 patients (21%) in the validation set and was predictive of 5-year death (11% versus 1%, hazard ratio 8.3, 95% CI 5.9 to 11.6, P<0.0001), accounting for 67% of events. Calibration of expected versus observed death rates based on Logical Analysis of Data and Cox regression showed that both methods performed very well.

Conclusion— Using the Logical Analysis of Data method, we identified subsets of patients who had an increased risk and who also accounted for the majority of deaths. Future research is needed to determine how best to use this technique for risk stratification.

Key Words: risk factors • mortality • statistics