Risk prediction models for binary response variables for the coronary bypass operation

• Main Author: Zhang, Hongbin
• Language: English
• Published: 2008
• Online Access: http://hdl.handle.net/2429/1571

The ability to predict 30 day operative mortality and complications following coronary artery bypass surgery in the individual patient has important implications clinically and for the design of clinical trials. This thesis focuses on setting up risk stratification algorithms. Utilizing the binary feature of the response variables, logistic regression analyses and classification trees (recursive partitioning) were used with the variables identified by the Health Data Research Institute in Portland, Oregon. The data set contains records for 18171 patients who had coronary artery bypass surgery in one of several hospitals between 1968 to 1991. Statistical models are setup, one from each method, for six outcome variables of the surgery: 30 day operative mortality, renal shutdown complication, central nervous system complication, pneumothorax complication, myocardial infarction complication and low output syndrome. The risk groups vary across different outcomes. The history of cardiac surgery has strong association with operative mortality and patients who suffer from a central nervous system disease tend to have higher risks for all the outcomes. Further study is necessary to consider the differences among hospitals and to divide the population according to the type of previous cardiac surgery.