A new discriminant function model overestimated coronary disease probability significantly less than the CADENZA algorithm (10% vs 16% and 5% vs 11%, p<0.001) in international test groups.
Observational (n=768)
Yes
Does a new discriminant function model improve the diagnostic classification of coronary artery disease compared to the CADENZA Bayesian algorithm in patients undergoing angiography?
A new discriminant function model provides reliable and clinically useful estimates of coronary disease probability in patients with chest pain syndromes and intermediate disease prevalence.
p-value: p=<0.001
A new discriminant function model for estimating probabilities of angiographic coronary disease was tested for reliability and clinical utility in 3 patient test groups. This model, derived from the clinical and noninvasive test results of 303 patients undergoing angiography at the Cleveland Clinic in Cleveland, Ohio, was applied to a group of 425 patients undergoing angiography at the Hungarian Institute of Cardiology in Budapest, Hungary (disease prevalence 38%); 200 patients undergoing angiography at the Veterans Administration Medical Center in Long Beach, California (disease prevalence 75%); and 143 such patients from the University Hospitals in Zurich and Basel, Switzerland (disease prevalence 84%). The probabilities that resulted from the application of the Cleveland algorithm were compared with those derived by applying a Bayesian algorithm derived from published medical studies called CADENZA to the same 3 patient test groups. Both algorithms overpredicted the probability of disease at the Hungarian and American centers. Overprediction was more pronounced with the use of CADENZA (average overestimation 16 vs 10% and 11 vs 5%, p less than 0.001). In the Swiss group, the discriminant function underestimated (by 7%) and CADENZA slightly overestimated (by 2%) disease probability. Clinical utility, assessed as the percentage of patients correctly classified, was modestly superior for the new discriminant function as compared with CADENZA in the Hungarian group and similar in the American and Swiss groups. It was concluded that coronary disease probabilities derived from discriminant functions are reliable and clinically useful when applied to patients with chest pain syndromes and intermediate disease prevalence.
Detrano et al. (1989) conducted an observational in Coronary artery disease (n=768). New discriminant function model (Cleveland algorithm) vs. Bayesian algorithm (CADENZA) was evaluated on Average overestimation of disease probability (p=<0.001). A new discriminant function model overestimated coronary disease probability significantly less than the CADENZA algorithm (10% vs 16% and 5% vs 11%, p<0.001) in international test groups.