Date of Award
Master of Science in Biomedical Engineering - (M.S.)
Biomedical Engineering Committee
Andrew Ulrich Meyer
Rose Ann Dios
David S. Kristol
In the present climate of quality assurance policies, rigorous requirements for informed consent, and a constantly changing patient population, a system of preoperative risk assignment for cardiovascular surgery was developed to monitor and evaluate surgical outcomes. The goal of this work is to estimate the preoperative risk associated with cardiac bypass surgery for patients in different risk categories. These risk categories are determined by the Parsonnet model which is based upon studying the severity of illness. The Parsonnet model assigns a risk value to a range of risk factors consisting of patient attributes and disease parameters. The aggregate of these risk factors is the mortality number in this thesis which is the subjective risk. After attaining posterior risk values for different risk classes we select a piecewise linear model which best estimates the risk for low, moderate and high risk cases. Confidence bands for posterior risk are also presented.
This thesis will utilize a database comprised of preoperative risk categories and their respective surgical outcomes in order to uniformly rate patient survival rates.
Zafar, Arifa, "Preoperative risk assessment for cardiac surgery using piecewise linear regression model" (1994). Theses. 1207.