A Method to Describe Physician Decision Thresholds and Its Application in Examining the Diagnosis of Coronary Artery Disease Based on Exercise Treadmill Testing

Abstract

The authors developed a method that utilizes logistic regression analysis to 1) calculate the disease probability with confidence intervals at which any specified proportion of physicians reaches a clinical decision, 2) statistically test whether factors other than disease probability affect this clinical decision, and 3) statistically test whether physician decision making in relation to disease probability varies by other factors. They apply the method to analyze the relationship between disease probability and the proportion of physicians who diagnosed coronary artery disease (CAD) in 127 consecutive subjects who completed the treadmill exercise tolerance test (ETT) at two hospitals. Twenty-five percent of the physicians decided that CAD was possible or definite at a post-ETT disease probability of 0.24 (95% CL = 0.07-0.35); 50% at 0.54 (95% CL = 0.43-0.70); and 75% at 0.82 (95% CL = 0.67-1.0). Multivariate logistic regression analysis revealed three factors significantly and independently related to the diagnosis of CAD: post-ETT disease probability, positive ETT result, and cigarette smoking. The proportion of physicians who reached a diagnosis of CAD did not differ by hospital setting (VA versus university), level of training (attending versus housestaff/ fellow), or diagnosing service (cardiology versus other internal medicine). It is concluded that factors other than disease probability may affect physician diagnostic decisions. Key words: medical decision making; decision threshold; logistic regression analysis; stochastic threshold model; exercise treadmill test; ischemic heart disease. (Med Decis Making 1992;12:204-212)