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Authors :
Peter L. Hammer
Eugene H. Blackstone
Michael S. Lauer
Hemant Ishwaran
Claire E Snader
Sorin Alexe
Source :
Annals of Operations Research. 119:15-42
Publication Year :
2003
Publisher :
Springer Science and Business Media LLC, 2003.

Abstract

The objective of this study was to distinguish within a population of patients with known or suspected coronary artery disease groups at high and at low mortality rates. The study was based on Cleveland Clinic Foundation's dataset of 9454 patients, of whom 312 died during an observation period of 9 years. The Logical Analysis of Data method was adapted to handle the disproportioned size of the two groups of patients, and the inseparable character of this dataset – characteristic to many medical problems. As a result of the study, we have identified a high-risk group of patients representing 1/5 of the population, with a mortality rate 4 times higher than the average, and including 3/4 of the patients who died. The low-risk group identified in the study, representing approximately 4/5 of the population, had a mortality rate 3 times lower than the average. A Prognostic Index derived from the LAD model is shown to have a 83.95% correlation with the mortality rate of patients. The classification given by the Prognostic Index was also shown to agree in 3 out of 4 cases with that of the Cox Score, widely used by cardiologists, and to outperform it slightly, but consistently. An example of a highly reliable risk stratification system using both indicators is provided.

Details

ISSN :
02545330
Volume :
119
Database :
OpenAIRE
Journal :
Annals of Operations Research
Accession number :
edsair.doi...........fc6644411cf7aec8838b6f6ca6f449f0
Full Text :
https://doi.org/10.1023/a:1022970120229