Impact of advanced exercise ECG analysis on cost of coronary artery disease management

BACKGROUND: Conventional analysis of exercise electrocardiogram (EX-ECG) has limited accuracy. This study aims to evaluate the potential impact of improving EX-ECG accuracy on costs of diagnosis and number of misdiagnoses of coronary artery disease (CAD). METHODS: A decision-tree model was simulated including sequential application of diagnostic procedures for suspected CAD. The model was structured in two main branches (presence or absence of CAD). A probabilistic sensitivity analysis was then performed for several combinations of improvement in test sensitivity and specificity. RESULTS: A clear trend in cost reduction was observed at improving EX-ECG specificity (about 8-8.5 million dollars, corresponding to a 5.6-7.6% reduction according to the prevalence level). Wrong diagnoses counted for 9-13% of test. Improvements in test parameters lead to reductions in wrong diagnoses, especially when increasing specificity (8.8-12.5%). CONCLUSIONS: A proper improvement in EX-ECG sensitivity and specificity would have a relevant impact on the costs of CAD management, while reducing the number of misdiagnoses.