Predicting Major Adverse Events in Patients With Acute Myocardial Infarction

Nestelberger, Thomas and Boeddinghaus, Jasper and Wussler, Desiree and Twerenbold, Raphael and Badertscher, Patrick and Wildi, Karin and Miró, Òscar and López, Beatriz and Martin-Sanchez, F. Javier and Muzyk, Piotr and Koechlin, Luca and Baumgartner, Benjamin and Meier, Mario and Troester, Valentina and Rubini Giménez, Maria and Puelacher, Christian and du Fay de Lavallaz, Jeanne and Walter, Joan and Kozhuharov, Nikola and Zimmermann, Tobias and Gualandro, Danielle M. and Michou, Eleni and Potlukova, Eliska and Geigy, Nicolas and Keller, Dagmar I. and Reichlin, Tobias and Mueller, Christian and Apace Investigators, . (2019) Predicting Major Adverse Events in Patients With Acute Myocardial Infarction. Journal of the American College of Cardiology, 74 (7). pp. 842-854.

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Official URL: https://edoc.unibas.ch/76998/

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Early and accurate detection of short-term major adverse cardiac events (MACE) in patients with suspected acute myocardial infarction (AMI) is an unmet clinical need.; The goal of this study was to test the hypothesis that adding clinical judgment and electrocardiogram findings to the European Society of Cardiology (ESC) high-sensitivity cardiac troponin (hs-cTn) measurement at presentation and after 1 h (ESC hs-cTn 0/1 h algorithm) would further improve its performance to predict MACE.; Patients presenting to an emergency department with suspected AMI were enrolled in a prospective, multicenter diagnostic study. The primary endpoint was MACE, including all-cause death, cardiac arrest, AMI, cardiogenic shock, sustained ventricular arrhythmia, and high-grade atrioventricular block within 30 days including index events. The secondary endpoint was MACE + unstable angina (UA) receiving early (≤24 h) revascularization.; Among 3,123 patients, the ESC hs-cTnT 0/1 h algorithm triaged significantly more patients toward rule-out compared with the extended algorithm (60%; 95% CI: 59% to 62% vs. 45%; 95% CI: 43% to 46%; p < 0.001), while maintaining similar 30-day MACE rates (0.6%; 95% CI: 0.3% to 1.1% vs. 0.4%; 95% CI: 0.1% to 0.9%; p = 0.429), resulting in a similar negative predictive value (99.4%; 95% CI: 98.9% to 99.6% vs. 99.6%; 95% CI: 99.2% to 99.8%; p = 0.097). The ESC hs-cTnT 0/1 h algorithm ruled-in fewer patients (16%; 95% CI: 14.9% to 17.5% vs. 26%; 95% CI: 24.2% to 27.2%; p < 0.001) compared with the extended algorithm, albeit with a higher positive predictive value (76.6%; 95% CI: 72.8% to 80.1% vs. 59%; 95% CI: 55.5% to 62.3%; p < 0.001). For 30-day MACE + UA, the ESC hs-cTnT 0/1 h algorithm had a higher positive predictive value for rule-in, whereas the extended algorithm had a higher negative predictive value for the rule-out. Similar findings emerged when using hs-cTnI.; The ESC hs-cTn 0/1 h algorithm better balanced efficacy and safety in the prediction of MACE, whereas the extended algorithm is the preferred option for the rule-out of 30-day MACE + UA. (Advantageous Predictors of Acute Coronary Syndromes Evaluation [APACE]; NCT00470587).
Faculties and Departments:03 Faculty of Medicine > Bereich Medizinische Fächer (Klinik) > Kardiologie > Klinische Outcomeforschung Kardiologie (Müller)
03 Faculty of Medicine > Departement Klinische Forschung > Bereich Medizinische Fächer (Klinik) > Kardiologie > Klinische Outcomeforschung Kardiologie (Müller)
UniBasel Contributors:Nestelberger, Thomas and Müller, Christian and Boeddinghaus, Jasper
Item Type:Article, refereed
Article Subtype:Research Article
Note:Publication type according to Uni Basel Research Database: Journal article
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Last Modified:08 Dec 2020 08:12
Deposited On:19 Aug 2020 13:30

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