The Single-Cutoff Trap: Implications For Bayesian Analysis of Stress Electrocardiograms.

Quantitative analysis of exercise electrocardiograms has been emphasized by many investigators. Specific problems have been found when a single cutoff is used to define a positive or a negative test: a single cutoff does not distinguish stress electrocardiography results that are slightly positive from those that are markedly positive. This may lead clinicians to underweigh strong evidence for or against coronary artery disease. This study evaluated clinicians\u27 quantitative analysis of stress electrocardiograms. Two hundred and thirty-five physicians interpreted the results of mildly positive (1.2 mm ST-segment depression) and strongly positive (2.2 mm ST-segment depression) stress electrocardiograms. Their posttest probability estimates were too high for a mildly positive test (0.62 +/- 0.02 versus actual of 0.38; p less than 0.001) and too low for a strongly positive test (0.77 +/- 0.01 versus actual of 0.98; p less than 0.001). Physicians should understand decision aids and should use multiple rather than single cutoffs to interpret the results of stress electrocardiography

Do Cardiologists Have Higher Thresholds for Recommending Coronary Arteriography Than Family Physicians?

The purpose of this study was to use a new model of decision making to understand variability in physicians\u27 utilization of diagnostic tests. We studied physicians\u27 recommendations for coronary arteriography in hypothetical patients with chest pain by analyzing responses of 235 cardiologists and family physicians. Thresholds for testing were derived by obtaining estimates of the probability of disease and recommendations for coronary arteriography before and after an exercise test. We found that cardiologists compared with family practitioners had a significantly higher decision threshold and recommended coronary arteriography in fewer patients. These findings suggest that analyzing physicians\u27 decision-making thresholds may be used to characterize differences in the practice behavior of groups of physicians

Comparing Aggregate Estimates of Derived Thresholds for Clinical Decisions.

Thresholds for medical decision making are the probabilities of disease at which clinicians choose to initiate testing or therapy. A descriptive analysis of clinicians\u27 decision making can derive their test and test-treatment thresholds and has the potential to explain variations in test utilization. A previously described method summarizes thresholds for a group of clinicians by determining the range of probability which includes the maximum number of clinicians\u27 individual thresholds. However, there is no statistical procedure to compare the summary measure of thresholds that is derived from the distribution of clinicians\u27 thresholds. We describe two alternative methods of developing a summary measure of the thresholds for a group of clinicians. These alternative methods enable the analyst to apply standard statistical tests when analyzing the decision-making behavior of groups of clinicians. For the Unweighted Mean of the Midpoints method, confidence limits of means and standard t-tests can be used to compare different groups. For the Weighted Mean of the Midpoints method, a weighted standard error of the mean can be calculated to determine confidence intervals, and a weighted t-test or weighted regression can be used to compare weighted means of the midpoints of threshold ranges

Variation in Physicians\u27 Decision-Making Thresholds in Management of a Sexually Transmitted Disease.

OBJECTIVE: To gain insight into the variation in physicians\u27 clinical decisions and further understand the factors that influence physicians\u27 thresholds for testing and treating. DESIGN: Written clinical scenarios were mailed to two groups of physicians who were asked to provide probability estimates of syphilis, how these estimates might change with new information, and when a diagnostic test would be ordered or treatment begun. A model was then used to calculate the probabilities at which physicians ordered tests or initiated treatment. PARTICIPANTS: Group 1 comprised 126 board-certified internists from metropolitan Philadelphia responding from a sample of 360 such physicians randomly selected from a directory. Group 2 consisted of 31 experts in sexually transmitted disease responding from a sample of 50 experts selected by the authors. MEASUREMENTS AND MAIN RESULTS: Experts were willing to obtain a serologic screening test at a lower likelihood of syphilis (0.013%) than were internists (0.034%), and they were willing to obtain a lumbar puncture at a lower likelihood of neurosyphilis (0.165%) than were internists (0.393%). The difference in the groups\u27 thresholds to begin neurosyphilis treatment was not significant. A multivariate model showed that group differences were created by individual characteristics (years in practice, subspecialty board certification, and full-time nonacademic practice) that were associated with higher thresholds for serologic screening. CONCLUSIONS: There are differences in the diagnostic testing practices for syphilis between national experts and internists. Although status in one of these groups alone did not predict the threshold for obtaining syphilis tests, certain individual characteristics were predictive. Examination of physician characteristics helps to explain the variation observed in their practice patterns, and determination of physicians\u27 thresholds aids in analyzing these variations

Assessing Physicians\u27 Estimates of the Probability of Coronary Artery Disease: The Influence of Patient Characteristics.

The authors assessed physicians\u27 probability estimates of coronary artery disease (CAD) in 250 patients undergoing a screening exercise stress test. True likelihood of disease (prevalence) was derived from the literature. Discrimination and calibration were assessed by comparing physicians\u27 probability estimates and prevalence using pairwise comparisons, rank correlation, and linear regression. There were differences in the discriminative abilities of the physicians based on patient characteristics. For example, the physicians had better discriminative ability for patients with typical cardiac chest pain compared with atypical chest pain. The physicians were able to predict the prevalence of CAD in broad groups of patients. However, they overestimated probabilities for patients with low prevalence of disease and underestimated probabilities for patients with high prevalence of disease. The authors conclude that physicians make consistent errors in the use of probability estimates. The quality of these estimates depends on patient characteristics such as type of chest pain and true likelihood of disease