One-hour High Sensitivity Cardiac Troponin I Algorithm Diagnoses Non-ST Elevation Myocardial Infarction in Patients With Chest Pain Presenting to the Emergency Department
Resident Focus - Volume 11, Issue 2
Approximately 10% of all emergency department evaluations include work-up for acute myocardial infarction (AMI), with only 15%-20% of those patients eventually diagnosed with AMI. Timely diagnosis and management is important for improving outcomes in patients with AMI, and may spare patients without evidence of AMI the anxiety and cost of additional assessments and help address the issue of overcrowded emergency departments. Currently, standard of care for diagnosing acute coronary syndrome typically consists of checking an electrocardiogram (ECG) and serial troponins (J Am Coll Cardiol 2012 Aug 14;60(7):645), which may keep patients in the emergency department for many hours waiting for the diagnosis. Given the high incidence of patients presenting to emergency departments with chest pain, many patients and clinicians may wonder if it is possible to streamline the current diagnostic practice to allow for more timely and accurate diagnosis of AMI and disposition of those without MI.
A recent diagnostic cohort study designed with independent derivation and validation cohorts assessed the efficacy of a 1-hour high-sensitivity cardiac troponin I (hs-cTnI) algorithm for identifying AMI. This algorithm was designed to increase the sensitivity of cardiac troponin testing for detecting AMI, given that the positive predictive value of troponins typically used is about 50%-60%. Study sites included 9 emergency departments in Switzerland, Spain, and Italy. Investigators randomly assigned 1,811 patients presenting with chest pain (not receiving dialysis for end-stage renal disease and without ST-elevation MI on ECG) to derivation and validation cohorts. The derivation cohort included 906 patients and the validation cohort contained 905 patients with suspected AMIs. All patients were clinically assessed with the 1-hour hs-cTnI algorithm as well as standard care, which included medical history, physical exam, troponin (cTn), 12 lead ECG, pulse oximetry, standard blood tests, and chest radiography.
The algorithm included a baseline measurement and absolute hs-cTnI change in one hour. Based on analysis of the derivation cohort, patients with a baseline hs-cTnI < 5.2 ng/L and one-hour interval change < 1.9 ng/L were considered “ruled-out” for AMI. Patients were “ruled-in” if they had an absolute hs-cTnI change ≥ 5.7 ng/L in one hour. If neither criterion were met, the patients were placed in an observation group and further testing was completed. The diagnostic reference standard was the consensus of 2 cardiologists, blinded to the hs-cTnI result, who reviewed the patients’ medical charts from presentation through a 90-day follow-up period.
By reference standard, 18% of enrolled patients were diagnosed with AMI. Using the hs-cTnl algorithm in the validation cohort, AMI was ruled out in 50.5% of patients, with 98.8% sensitivity and 99.6% negative predictive value. AMI was detected in 19% of patients using the hs-cTnl algorithm with a specificity of 93.8% and 73.9% positive predictive value. Of the 30.5% of patients placed in the observation group, 13% had AMI at final diagnosis. The 30 day mortality in the validation cohort was 0% in the “rule-out” group, 1.4% in the “observe” group, and 4.7% in the “rule-in” group (p < 0.001 for trend). Compared to the usual standard care (combining hs-cTnI with findings of ischemia on ECG), the 1-hour hs-cTnI algorithm had significantly higher negative and positive predictive values for AMI in the “rule-out” and “rule-in” groups, respectively (p <0.001 for both).
This prospective study provides new evidence for a timely and reliable 1-hour algorithm using changes in hs-cTnl that better detects and rules out patients with AMI compared to standard care. Use of this algorithm may improve patient safety, increase the efficiency of the management of these patients, and provide tremendous cost savings to the health care system.
Carol Tran, MD, attended medical school at Virginia Commonwealth University School of Medicine, and she is currently a second year Family Medicine resident at the University of Virginia. She holds a strong interest in women’s health, international medicine, and health policy, and would like to integrate these into her future career.
Faculty contribution by Katharine C. DeGeorge, MD, MS.