No abstract

Objectives This article presents evidence from a systematic review of the effectiveness of four practices (assay selection, decision point cardiac troponin (cTn) threshold selection, serial testing, and point of care testing) for improving the diagnostic accuracy for Non-ST-Segment Elevation Myocardial Infarction (NSTEMI) in the Emergency Department. Design and Methods The CDC-funded Laboratory Medicine Best Practices (LMBP™) Initiative systematic review A6 Method for Laboratory Best Practices was used. Results The current guidelines (e.g., ACC/AHA) recommend using cardiac troponin assays with a 99th percentile upper reference limit (URL) diagnostic threshold to diagnose NSTEMI. The evidence in this systematic review indicates that contemporary sensitive cTn assays meet the assay profile requirements (sensitivity, specificity, PPV, and NPV) to more accurately diagnose NSTEMI than alternate tests. Additional biomarkers did not increase diagnostic effectiveness of cTn assays. Sensitivity, specificity, and negative predictive value (NPV) were consistently high and low positive predictive value (PPV) improved with serial sampling. Evidence for use of cTn point of care testing (POCT) was insufficient to make recommendations, though some evidence suggests cTn POCT may result in reduction to patient length of stay and costs. Conclusions Two best practice recommendations emerged from the systematic review and meta-analysis of literature conducted using the LMBP™ A6 Method criteria: Testing with cardiac troponin assays, using the 99th percentile URL as the clinical diagnostic threshold for the diagnosis of NSTEMI and without additional biomarkers, is recommended. Also recommended is serial cardiac troponin sampling with one sample at presentation and at least one additional sample taken a minimum of 6 hours later to identify a rise or fall in the troponin level. Testing with high-sensitivity cardiac troponin assays, at presentation and again within 6 hours, is the recommended evidence-based best practice testing algorithm for optimized NSTEMI diagnosis.