How to improve the management of chest pain: hospitalists and use of prediction rules

BACKGROUND: Three percent of patients with acute myocardial infarction are still missed despite the excess number of admissions with chest pain. The purpose of this study was to review the characteristics of patients who were admitted with chest pain, to evaluate the appropriateness of admissions and the outcomes. We also discuss whether use of a prediction rule could have made a difference in the management of these cases. METHODS: We performed retrospective chart review on all patients admitted to the hospitalist service with a diagnosis of chest pain. Each patient was risk stratified using Diamond and Forrester algorithm for probability of coronary artery disease (CAD), retrospectively. Results were analyzed using chi2 test or exact test and Student's t test. RESULTS: Of 260 patients admitted with chest pain to the hospitalist service, only 24 (9.2%) received the final diagnosis of acute coronary syndrome (ACS). The patients in the ACS group were older and more likely to be male and to have a history of hyperlipidemia, CAD, peripheral vascular disease, cerebrovascular disease and percutaneous coronary intervention (PCI). Of 34 patients who underwent cardiac catheterization, 20 (58.8%) had occlusive CAD and 14 of them received PCI. Risk stratification of patients, retrospectively, revealed 28.3% of the total patient population was high risk, while 6.6% of them were low risk. The number of ACS cases was highest in the high risk group, while none was detected in the low risk group. CONCLUSIONS: Our study demonstrated that using a prediction rule could have prevented about 6% of the chest pain admissions. Therefore, the use of risk stratification methods should be encouraged to decrease cost and improve efficiency of care.