CLAS, a randomized, controlled clinical trial testing niacin/colestipol plus diet therapy in non-smoking men with previous coronary bypass surgery, has demonstrated treatment benefits using both a) a coronary endpoint determination by a panel of human readers, the global coronary change score, and also using b) quantitative coronary angiographic (QCA) measures (percent stenosis, roughness, percent involvement, and minimum diameter). However, in correlating these QCA measures with the global coronary change score using linear regression techniques, only 36% of the variability in the global coronary change score was explained. In this proposal, we apply the technique of adaptive neural networks (ANN) to these QCA measures, as well as measures describing the geometric and hemodynamic relationship between arterial and bypass graft segments, in predicting the panel-based assessment of coronary status. The resulting ANN will be compared to traditional statistical models. In other applications, ANNs have a) demonstrated the potential to recognize complex patterns (such as changes in lesions due to treatment); b) are able to generalize from the available data, enabling appropriate responses to new combinations of input data; and c) are tolerant to missing input data.

Agency
National Institute of Health (NIH)
Institute
National Heart, Lung, and Blood Institute (NHLBI)
Type
Small Research Grants (R03)
Project #
5R03HL048532-02
Application #
2224597
Study Section
Special Emphasis Panel (SRC (MK))
Project Start
1992-07-01
Project End
1995-06-30
Budget Start
1993-07-01
Budget End
1995-06-30
Support Year
2
Fiscal Year
1993
Total Cost
Indirect Cost
Name
University of Southern California
Department
Public Health & Prev Medicine
Type
Schools of Medicine
DUNS #
041544081
City
Los Angeles
State
CA
Country
United States
Zip Code
90089
Lapuerta, P; Azen, S P; LaBree, L (1995) Use of neural networks in predicting the risk of coronary artery disease. Comput Biomed Res 28:38-52