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Am J Cardiol ; 203: 240-247, 2023 09 15.
Artigo em Inglês | MEDLINE | ID: mdl-37506670

RESUMO

Many algorithms for emergency department (ED) evaluation of acute coronary syndrome (ACS) using high-sensitivity troponin assays rely on the detection of a "delta," the difference in concentration over a predetermined interval, but collecting specimens at specific times can be difficult in the ED. We evaluate the use of troponin "velocity," the rate of change of troponin concentration over a flexible short interval for the prediction of major adverse cardiac events (MACEs) at 30 days. We conducted a prospective, observational study on a convenience sample of 821 patients who underwent ACS evaluation at a high-volume, urban ED. We determined the diagnostic performance of a novel velocity-based algorithm and compared the performance of 1- and 2-hour algorithms adapted from the European Society of Cardiology (ESC) using delta versus velocity. A total of 7 of 332 patients (2.1%) classified as low risk by the velocity-based algorithm experienced a MACE by 30 days compared with 35 of 221 (13.8%) of patients classified as greater than low risk, yielding a sensitivity of 83.3% (95% confidence interval [CI] 68.6% to 93.0%) and negative predictive value (NPV) of 97.9% (95% CI 95.9% to 98.9%). The ESC-derived algorithms using delta or velocity had NPVs ranging from 98.4% (95% CI 96.4% to 99.3%) to 99.6% (95% CI 97.0% to 99.9%) for 30-day MACEs. The NPV of the novel velocity-based algorithm for MACE at 30 days was borderline, but the substitution of troponin velocity for delta in the framework of the ESC algorithms performed well. In conclusion, specimen collection within strict time intervals may not be necessary for rapid evaluation of ACS with high-sensitivity troponin.


Assuntos
Síndrome Coronariana Aguda , Infarto do Miocárdio , Humanos , Troponina , Síndrome Coronariana Aguda/diagnóstico , Infarto do Miocárdio/diagnóstico , Estudos Prospectivos , Valor Preditivo dos Testes , Serviço Hospitalar de Emergência , Troponina T , Biomarcadores , Algoritmos
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