Jack Ven Tu
Formerly Senior Scientist and Program Leader, Cardiovascular Research, Institute for Clinical Evaluative Sciences, University of Toronto Staff Cardiologist, Sunnybrook Health Sciences Center, University of Toronto
Professor of Medicine and Health Policy, Management and Evaluation, University of Toronto Canada Research Chair in Health Services Research Tier I
Dissertation Title： "Quality of Cardiac Surgical Care in Ontario, Canada"In this dissertation, a comprehensive study was undertaken to study a number of issues related to the quality of cardiac surgical care in Ontario, Canada.
In the first paper, "Coronary artery bypass mortality rates in Ontario: A Canadian approach to quality assurance in cardiac surgery", a study was undertaken to assess the overall in-hospital mortality rate and the amount of inter-hospital variation in risk-adjusted mortality rates following coronary artery bypass graft (CABG) surgery in Ontario between 1991 and 1993. The overall mortality rate was 3.01%, and no hospitals had risk-adjusted mortality rates significantly higher than expected during the three-year study period. The outcomes in this study are probably attributable to regionalization of CABG surgery and a very low prevalence of low-volume cardiac surgeons in Ontario.
In the second paper, "Coronary artery bypass surgery in Ontario and New York State: Which rate is right?", the clinical characteristics of patients and rates of CABG surgery in Ontario and New York State in 1993 were compared. Patients in New York were more likely to be older, female, and have had a recent myocardial infarction while patients in Ontario were more likely to have had left ventricular dysfunction and severe coronary artery disease. It was concluded that there is no single right rate of CABG surgery but rather trade-offs between higher rates of surgery and the clinical severity of patients receiving the procedure.
In the third paper, "Predicting mortality after coronary artery bypass surgery: What do artificial neural networks learn?", artificial neural networks (ANNs) and logistic regression (LR) statistical models were developed for predicting in-hospital mortality after CABG surgery in Ontario. The predictions from the ANN model were very highly correlated (r=0.95) with those of a main effects LR model with both models having similar areas under the receiver operating characteristic curve. The results of this study suggest that ANNs do not offer any significant advantages over LR modeling techniques since both methods "learn" similar relationships between patient characteristics and mortality after CABG surgery.