Diagnostic accuracy of cardiologists compared with probability calculations using Bayes' rule.

Probability analysis has provided insights into the use of diagnostic tests in coronary artery disease, and recent developments may permit clinical application of individual patients. To validate independently two available methods of probability calculation, their diagnostic accuracy was compared with that of cardiologists. Ninety-one cardiologists participated in the study; each evaluated the clinical summaries of eight randomly selected patients. For each patient, the cardiologist assessed the probability of coronary artery disease after reviewing the clinical history, physical examination and laboratory data, including complete results from a treadmill exercise test. The probability of coronary artery disease was also obtained for each patient, using the identical information, from two methods employing Bayes' rule: (1) from a published table of data based on the patient's age, sex, symptoms and degree of S-T segment change during exercise; and (2) from a computer program using the age, sex, risk factors, resting electrocardiogram and multiple exercise measurements. Diagnostic accuracy was assessed on a scale from 0 to 100 with the coronary angiogram as the diagnostic standard. The average diagnostic accuracy on this scale was: 80.2 for the cardiologists' estimates, 78.0 for the estimates based on tables (difference from cardiologists' estimates p less than 0.05) and 83.1 for the estimates based on computer calculations (p less than 0.01). Thus probability analysis incorporating sufficient detail can achieve a diagnostic accuracy comparable with that of cardiologists. Studies of the efficacy of probability analysis in patient care are warranted.