Why the study?
Does a 0/1h-algorithm using a novel hs-cTnI assay accurately rule-out and rule-in acute myocardial infarction in patients presenting to the emergency department?
Does a 0/1h-algorithm using a novel hs-cTnI assay accurately rule-out and rule-in acute myocardial infarction in patients presenting to the emergency department?
A novel 0/1-hour algorithm using the Centaur hs-cTnI assay demonstrates excellent negative predictive value and sensitivity for the rapid rule-out of acute myocardial infarction in the emergency department.
Supports rapid ED triage using this specific hs-cTnI assay; leaves.
Background: The European Society of Cardiology suggests the use of a 0/1h-algorithm for rapid rule-out and rule-in of acute myocardial infarction (AMI) when using high-sensitivity cardiac troponin (hs-cTn). Purpose: We aimed to derive and validate a new 0/1h-algorithm for a novel hs-cTnI assay. Methods: In a prospective international multicentre diagnostic study enrolling patients presenting with suspected AMI to the ED, hs-cTnI was determined at baseline and after one hour using Centaur hs-cTnI. Patients presenting with STEMI were excluded. The final diagnosis was centrally adjudicated by two independent cardiologists using all available data including coronary angiography, echocardiography, follow-up data, and serial measurements of hs-cTnT (but not hs-cTnI). The hs-cTnI 0/1h-algorithm, incorporating measurements performed at baseline and absolute changes within 1 hour, was derived in a randomly selected sample of 672 patients (derivation sample) using classification and regression tree (CART) analysis, and then validated in the remaining 675 patients (validation sample). Results: Acute myocardial infarction was the final diagnosis in 18% of the 1347 recruited patients. After applying the hs-cTnI 0/1h-algorithm developed in the derivation cohort to the validation cohort, 46% of patients could be classified as “rule-out”, 18% as “rule-in”, and 36% as “observe” (Figure 1). In the validation cohort, the derived rule-out strategy resulted in a negative predictive value for AMI of 99.7% (95% confidence interval (CI), 97.8–100%) and a sensitivity of 99.1% (95% CI, 95.3–100%) while the derived rule-in strategy resulted in a positive predictive value for AMI of 72.5% (95% CI, 63.6–80.3%) and a specificity of 94.1% (95% CI, 91.8–95.9%).
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Twerenbold et al. (2017) studied this question.
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