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 > or =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.
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 > or =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.
Authors: Marie-Luise Brennan; Anupama Reddy; W H Wilson Tang; Yuping Wu; Danielle M Brennan; Amy Hsu; Shirley A Mann; Peter L Hammer; Stanley L Hazen Journal: Circulation Date: 2010-06-21 Impact factor: 29.690
Authors: Gabriela Alexe; Sorin Alexe; David E Axelrod; Tibérius O Bonates; Irina I Lozina; Michael Reiss; Peter L Hammer Journal: Breast Cancer Res Date: 2006 Impact factor: 6.466
Authors: Ersoy Subasi; Munevver Mine Subasi; Peter L Hammer; John Roboz; Victor Anbalagan; Michael S Lipkowitz Journal: Front Med (Lausanne) Date: 2017-07-19
Authors: Anupama Reddy; Honghui Wang; Hua Yu; Tiberius O Bonates; Vimla Gulabani; Joseph Azok; Gerard Hoehn; Peter L Hammer; Alison E Baird; King C Li Journal: BMC Med Inform Decis Mak Date: 2008-07-10 Impact factor: 2.796