PURPOSES: To identify bedside variables that aid in diagnosis of acute coronary syndrome (ACS) and might facilitate rapid triage of patients aged > or = 65 years. BASIC PROCEDURES: Prospective, observational study of consecutive patients aged > or = 65 years with suspicion of ACS presenting to our emergency department (ED). Patients' medical characteristics were collected at baseline and during a 1-month follow-up period. We identified variables independently associated with ACS by multivariate analyses and bootstrapping techniques. MAIN FINDINGS: Among 399 patients, 124 (31.1%) received a diagnosis of ACS (61 acute myocardial infarction, 63 unstable angina). We surveyed multiple clinical and ECG variables to develop a predictive model which included the following variables: male sex, history of coronary artery disease, typical chest pain, dyspnea, epigastric pain, pathological Q-wave, ST-segment elevation (area under the receiver operating characteristic curve (AUC) 0.79, 95% confidence interval 0.71 to 0.82). With the addition of cardiac troponin I to the model the AUC increased to 0.83 (0.79 to 0.88). We used these findings to create the Heart Attack Risk for aged Patient (HARP) scale. Our data suggest that patients with a low HARP score might be safely managed without further testing. PRINCIPAL CONCLUSIONS: A model based on variables easily available at ED presentation from history, physical examination, and electrocardiography, can help ED physicians to identify seniors at very low risk of ACS.
PURPOSES: To identify bedside variables that aid in diagnosis of acute coronary syndrome (ACS) and might facilitate rapid triage of patients aged > or = 65 years. BASIC PROCEDURES: Prospective, observational study of consecutive patients aged > or = 65 years with suspicion of ACS presenting to our emergency department (ED). Patients' medical characteristics were collected at baseline and during a 1-month follow-up period. We identified variables independently associated with ACS by multivariate analyses and bootstrapping techniques. MAIN FINDINGS: Among 399 patients, 124 (31.1%) received a diagnosis of ACS (61 acute myocardial infarction, 63 unstable angina). We surveyed multiple clinical and ECG variables to develop a predictive model which included the following variables: male sex, history of coronary artery disease, typical chest pain, dyspnea, epigastric pain, pathological Q-wave, ST-segment elevation (area under the receiver operating characteristic curve (AUC) 0.79, 95% confidence interval 0.71 to 0.82). With the addition of cardiac troponin I to the model the AUC increased to 0.83 (0.79 to 0.88). We used these findings to create the Heart Attack Risk for aged Patient (HARP) scale. Our data suggest that patients with a low HARP score might be safely managed without further testing. PRINCIPAL CONCLUSIONS: A model based on variables easily available at ED presentation from history, physical examination, and electrocardiography, can help ED physicians to identify seniors at very low risk of ACS.