Ergoscore: proposed diagnostic algorithm for ischemic heart disease in the chest pain unit

Abstract

Assessment of chest pain in the emergency room can be complex. We propose a diagnostic algorithm that allows classifying patients in groups of high and low risk of coronary etiology for chest pain units. Methods: a historical observational cohort study with prospective follow-up of patients who came to the emergency room for chest pain suspected of ischemic etiology with normal electrocardiogram and biomarkers that underwent conventional stress test was analyzed. The mean follow-up was 15 months. Coronary catheterization showing significant lesions was considered as gold standard of coronary etiology, while catheterization without lesions and/or absence of recurrence of pain during follow-up were considered as negativity data of coronary etiology. A logistic regression was used to obtain predictors of coronary origin and therefore a diagnostic algorithm was elaborated, based on the Ergoscore (score made with the selected factors) and on a stress test. The discriminant power of the algorithm was evaluated by Area Under the ROC curve (AUC). Results: 100 patients were analyzed. 17% had significant coronary lesions. Diagnostic accuracy of the ergometry was 89%. Cardiovascular risk score and pretest probability were the selected factors. Ergoscore results less than or equal to 3, and greater than or equal to 7 presented the best behaviour with respect to the validity parameters. The results of the algorithm were: sensitivity = 82.4%, specificity 89.2%, accuracy 88%. No false negatives were observed. The AUC was 0.915.
Conclusions: this algorithm presents an adequate diagnostic accuracy detecting cases of coronary origin. Its application would avoid performing a stress test to 32% of patients.