Optimizing stress-only myocardial perfusion imaging: a clinical prediction model to improve patient selection

Abstract

Background Stress-only single photon emission computed tomography myocardial perfusion imaging (MPI) offers numerous advantages in terms of improved workflow, cost and radiation reduction but is currently not widely utilized due to challenges in selecting appropriate patients for this technique. Methods Data from 5959 individuals were used to derive (N = 4018) and validate (N = 1941) a binomial logistic regression model to predict normal stress MPI studies (stress total perfusion deficit  < 4%, ejection fraction ≥ 50%). Model performance was analyzed using receiver operator characteristic curves. A simplified point-scoring system was developed and its impact on imaging workflow was assessed. Results Significant predictors of abnormal vs. normal stress MPI included male sex, age > 65 years, cardiomyopathy, congestive heart failure, myocardial infarction, angina, and pharmacological stress. The final model and simplified scoring system were associated with areas under the curve of 0.81 (95% CI 0.79–0.83) and 0.80 (95% CI 0.79–0.82) in the validation group, respectively. Use of the scoring system was estimated to result in a decrease of 56.5% in the number of non-contributory imaging studies acquired with minimal patient rescheduling. Conclusion A prediction tool derived from simple clinical information can identify candidates for stress-only MPI studies with a beneficial impact on departmental workflow.