Acute coronary syndromes diagnosis, version 2.0: Tomorrow's approach to diagnosing acute coronary syndromes?

Richard Body1; 2; 3

doi:10.1016/j.tjem.2018.05.005

Richard Body^1,2,3

¹Division of Cardiovascular Sciences, The University of Manchester, Oxford Road, Manchester, M13 9PL, United Kingdom
²Emergency Department, Manchester University Foundation NHS Trust, Oxford Road, Manchester, M13 9WL, United Kingdom
³Healthcare Sciences Department, Manchester Metropolitan University, John Dalton Building, Oxford Road, Manchester, United Kingdom

Keywords: Acute myocardial infarction, Acute Coronary Syndromes, Diagnosis, Sensitivity and specificity, Troponins, Troponins, high sensitivity, Emergency medicine

Abstract

Chest pain accounts for approximately 6% of Emergency Department (ED) attendances and is the most common reason for emergency hospital admission. For many years, our approach to diagnosis has required patients to stay in hospital for at least 6–12 h to undergo serial biomarker testing. As less than one fifth of the patients undergoing investigation actually has an acute coronary syndrome (ACS), there is tremendous potential to reduce unnecessary hospital admissions.

Recent advances in diagnostic technology have improved the efficiency of care pathways. Decision aids such as the Thrombolysis in Myocardial Infarction (TIMI) risk score and the History, Electrocardiogram, Age, Risk factors and Troponin (HEART) score enable rapid ‘rule out’ of ACS within hours of patients arriving in the ED. With high sensitivity cardiac troponin (hs-cTn) assays, approximately one third of patients can have ACS ‘ruled out’ with a single blood test, and up to two thirds could have an acute myocardial infarction ‘ruled out’ with a second sample taken after as little as 1 h.

Building on those recent advances, this paper presents an overview of the principles behind the development of the Troponin-only Manchester Acute Coronary Syndromes (T-MACS) decision aid. This clinical prediction model could be used to ‘rule out’ and ‘rule in’ ACS following a single blood test and to calculate the probability of ACS for every patient. The future potential of this approach is then addressed, including practical applications of artificial intelligence, shared decision making, near-patient testing and personalized medicine.