The Reliability of Probability Analysis in the Prediction of Coronary Artery Disease in Two Hospitals

Kern H. Guppy, Robert Detrano, Nabil Abbassi, Andras Janosi, Sarbjit Sandhu, Victor Froelicher

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To assess the accuracy of the Bayesian computer program CADENZA for the prediction of coronary artery disease, the authors examined the probabilities generated by the application of this program to the clinical and noninvasive test results of 303 patients in a private referral center and 199 patients in a veterans’ hospital. These probabilities were compared with those produced by applying a six-variable discriminant function derived by logistic regression at the private referral center. Two statistical approaches were employed in evaluating the relative performances of the Bayesian program and the discriminant function. The first of these involved the sorting of patients in both test groups into ascending deciles of probability and comparing expected probability with observed angiographic disease prevalence in each decile. The second involved the calculation and comparison of a standardized reliability measure. The lafter was significantly lower for the discriminant function both at the private hospital (0.200 for the discriminant function versus -17.5 ± 1.96 for the Bayesian program) and at the veterans’ hospital ( - 0.8 ± 1.96 for the discriminant function versus -11.3 for Bayesian program). This suggests that the discriminant function is significantly superior to the Bayesian algorithm CADENZA for predicting coronary artery disease probabilities in subjects who have relatively high pretest disease probabilities.

Original languageEnglish
Pages (from-to)181-189
Number of pages9
JournalMedical Decision Making
Issue number3
Publication statusPublished - Aug 1989



  • Key words: Bayesian algo rithm
  • coronary artery disease
  • discriminant function. (Med Decis Making 1989;9:181-189)

ASJC Scopus subject areas

  • Health Policy

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