Maria Rubini Gimenez1, Raphael Twerenbold2, Cedric Jaeger2, Christian Schindler3, Christian Puelacher2, Karin Wildi2, Tobias Reichlin2, Philip Haaf2, Salome Merk2, Ursina Honegger2, Max Wagener2, Sophie Druey2, Carmela Schumacher2, Lian Krivoshei2, Petra Hillinger2, Thomas Herrmann2, Isabel Campodarve4, Katharina Rentsch5, Stefano Bassetti6, Stefan Osswald2, Christian Mueller7. 1. Department of Cardiology and Cardiovascular Research Institute Basel, University Hospital Basel, Switzerland; Servicio de Urgencias y Pneumologia, CIBERES ISC III, Hospital del Mar-Institut Municipal d'Investigació Mèdica, Barcelona, Spain. 2. Department of Cardiology and Cardiovascular Research Institute Basel, University Hospital Basel, Switzerland. 3. Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, University Basel, Switzerland. 4. Servicio de Urgencias y Pneumologia, CIBERES ISC III, Hospital del Mar-Institut Municipal d'Investigació Mèdica, Barcelona, Spain. 5. Laboratory Medicine, University Hospital Basel, Switzerland. 6. Department of Internal Medicine, University Hospital Basel, Switzerland. 7. Department of Cardiology and Cardiovascular Research Institute Basel, University Hospital Basel, Switzerland. Electronic address: Christian.Mueller@usb.ch.
Abstract
OBJECTIVE: We aimed to prospectively derive and validate a novel 1h-algorithm using high-sensitivity cardiac troponin I (hs-cTnI) for early rule-out and rule-in of acute myocardial infarction. METHODS: We performed a prospective multicenter diagnostic study enrolling 1811 patients with suspected acute myocardial infarction. The final diagnosis was centrally adjudicated by 2 independent cardiologists using all available information, including coronary angiography, echocardiography, follow-up data, and serial measurements of hs-cTnT (but not hs-cTnI). The hs-cTnI 1h-algorithm, incorporating measurements performed at baseline and absolute changes within 1 hour, was derived in a randomly selected sample of 906 patients (derivation cohort), and then validated in the remaining 905 patients (validation cohort). RESULTS: Acute myocardial infarction was the final diagnosis in 18% of patients. After applying the hs-cTnI 1h-algorithm developed in the derivation cohort to the validation cohort, 50.5% of patients could be classified as "rule-out," 19% as "rule-in," 30.5% as "observe." In the validation cohort, the negative predictive value for acute myocardial infarction in the "rule-out" zone was 99.6% (95% confidence interval, 98.4%-100%), and the positive predictive value for acute myocardial infarction in the "rule-in" zone was 73.9% (95% confidence interval, 66.7%-80.2%). Negative predictive value of the 1h-algorithm was higher compared with the classical dichotomous interpretation of hs-cTnI and to the standard of care combining hs-cTnI with the electrocardiogram (both P < .001). Positive predictive value also was higher compared with the standard of care (P < .001). CONCLUSION: Using a simple algorithm incorporating baseline hs-cTnI values and the absolute change within the first hour allows safe rule-out as well as accurate rule-in of acute myocardial infarction in 70% of patients presenting with suspected acute myocardial infarction.
OBJECTIVE: We aimed to prospectively derive and validate a novel 1h-algorithm using high-sensitivity cardiac troponin I (hs-cTnI) for early rule-out and rule-in of acute myocardial infarction. METHODS: We performed a prospective multicenter diagnostic study enrolling 1811 patients with suspected acute myocardial infarction. The final diagnosis was centrally adjudicated by 2 independent cardiologists using all available information, including coronary angiography, echocardiography, follow-up data, and serial measurements of hs-cTnT (but not hs-cTnI). The hs-cTnI1h-algorithm, incorporating measurements performed at baseline and absolute changes within 1 hour, was derived in a randomly selected sample of 906 patients (derivation cohort), and then validated in the remaining 905 patients (validation cohort). RESULTS: Acute myocardial infarction was the final diagnosis in 18% of patients. After applying the hs-cTnI1h-algorithm developed in the derivation cohort to the validation cohort, 50.5% of patients could be classified as "rule-out," 19% as "rule-in," 30.5% as "observe." In the validation cohort, the negative predictive value for acute myocardial infarction in the "rule-out" zone was 99.6% (95% confidence interval, 98.4%-100%), and the positive predictive value for acute myocardial infarction in the "rule-in" zone was 73.9% (95% confidence interval, 66.7%-80.2%). Negative predictive value of the 1h-algorithm was higher compared with the classical dichotomous interpretation of hs-cTnI and to the standard of care combining hs-cTnI with the electrocardiogram (both P < .001). Positive predictive value also was higher compared with the standard of care (P < .001). CONCLUSION: Using a simple algorithm incorporating baseline hs-cTnI values and the absolute change within the first hour allows safe rule-out as well as accurate rule-in of acute myocardial infarction in 70% of patients presenting with suspected acute myocardial infarction.
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