Identifying acute coronary syndrome is a difficult task in the emergency department because symptoms may be atypical and the electrocardiogram has low sensitivity. In this prospective cohort study done in a tertiary community emergency hospital, we developed and tested a neural diagnostic tree in 566 consecutive patients with chest pain and no ST-segment elevation for the diagnosis of acute coronary syndrome. Multivariate regression and recursive partitioning analysis allowed the construction of decision rules and of a neural tree for the diagnosis of acute myocardial infarction and acute coronary syndrome. Predictive variables of acute coronary syndrome were: age > or =60 years (odds ratio [OR] = 2.3; P = 0.0016), previous history of coronary artery disease (OR = 2.9; P = 0.0008), diabetes (OR = 2.8; P = 0.0240), definite/probable angina-type chest pain (OR = 17.3; P = 0.0000) and ischemic electrocardiogram (ECG) changes on admission (OR = 3.5; P = 0.0002). The receiver operating characteristic curve of possible diagnostic decision rules of the regression model disclosed a C-index of 0.904 (95% confidence interval = 0.878 to 0.930) for acute coronary syndrome and 0.803 (95% confidence interval 0.757 to 0.849) for acute myocardial infarction. For both disorders, sensitivities of the neural tree were 99% and 93%, respectively, and negative predictive values were both 98%. Negative likelihood ratios were 0.02 and 0.1, respectively. It is concluded that this simple and easy-to-use neural diagnostic tree was very accurate in the identification of non-ST segment elevation chest pain patients without acute coronary syndrome. Patients identified as low probability of disease could receive immediate stress testing and be discharged if the test is negative.
Management of chest pain patients in the emergency department has been a dilemma because of difficulty in identifying those who can be immediately discharged and those who need to be hospitalized. We assessed the efficacy of a probability stratification model and a systematic diagnostic strategy in 1003 consecutive chest pain patients prospectively evaluated and stratified for acute coronary syndromes according to chest pain characteristics and admission electrocardiogram. Patients with no suspicion of acute coronary syndromes (n = 224) were immediately discharged, whereas those with very-high probability (n =119) were admitted to the coronary care unit. Remaining patients were evaluated in a Chest Pain Unit and investigated during a 9-hour period (intermediate-probability, n = 433) (route 2) and a 6-hour period (low-probability, n = 277) (route 3). Sensitivity and negative predictive value of chest pain type for the diagnosis of acute myocardial infarction (94% and 97%, respectively) was much better than the admission electrocardiogram (49% and 86%, respectively) and admission creatine kinase-MB (46% and 86%, respectively). Serial creatine kinase-MB determinations ruled out acute myocardial infarction by the third-hour postadmission in all route 3 patients but only at the ninth-hour in route 2 patients. For patients with no ST-segment elevation, chest pain type was the strongest independent predictor of acute coronary syndromes. It is concluded that chest pain type is the best single diagnostic tool to rule in/out acute coronary syndromes on admission to the emergency department. Patients with suspicious chest pain must have serum creatine kinase-MB measurements up to 9 hours postadmission to rule out acute myocardial infarction.
Purpose -To evaluate the efficacy of a systematic model of care for patients with chest pain and no ST segment elevation in the emergency room. Methods -From 1003 patients submitted to an algorithm diagnostic investigation by probability of acute ischemic syndrome. We analyzed 600 ones with no elevation of ST segment, then enrolled to diagnostic routes of median (route 2) and low probability (route 3) to ischemic syndrome. Results - Efficacy of a Diagnostic Strategy for Patients with Chest Pain and No ST-Segment Elevation in the Emergency Room Original ArticleThe diagnostic management of patients arriving at the emergency room with chest pain is one of the great challenges of medical practice. This is due not only to the fact that several thoracic and nonthoracic diseases can be the cause of the symptom but also because some of these pathologies may have a very high mortality rate, as is the case with aortic dissection, pulmonary embolism and acute myocardial infarction. Therefore, emergency physicians usually are extremely cautious when they see these patients and try to identify and hospitalize those with high-risk diseases. Although aortic dissection and pulmonary embolism are infrequently seen in the emergency room (less than 1% of chest pain patients), acute myocardial infarction and unstable angina are more common (approximately 10% and 20%, respectively) [1][2][3][4] .Acute coronary insufficiency has the electrocardiogram as its diagnostic method of choice. However, several studies have demonstrated that this tool has low sensitivity for the diagnosis of this syndrome (about 50%) 5,6 . The present study tries to establish a rapid and accurate diagnostic strategy for patients seen in the emergency room with chest pain who do not have the typical electrocardiographic feature of acute myocardial infarction ( ST segment elevation ). MethodsPro-Cardiaco Hospital is a primary-and tertiary-care private institution for clinical and cardiologic patients located in the center of the city of Rio de Janeiro, Brazil. It has an active 9-bed emergency room and a cardiologist-staffed ambulance service for house-calls.To improve care of patients with chest pain and to make the diagnostic and therapeutic management uniform between attending physicians and house-staff, a diagnostic strategy was created according to the pretest probability of acute coronary insufficiency 6 . A systematic model was developed
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