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

被引:10
作者
Body, Richard [1 ,2 ,3 ]
机构
[1] Univ Manchester, Div Cardiovasc Sci, Oxford Rd, Manchester M13 9PL, Lancs, England
[2] Manchester Univ Fdn NHS Trust, Emergency Dept, Oxford Rd, Manchester M13 9WL, Lancs, England
[3] Manchester Metropolitan Univ, Healthcare Sci Dept, John Dalton Bldg,Oxford Rd, Manchester, Lancs, England
关键词
Acute myocardial infarction; Acute Coronary Syndromes; Diagnosis; Sensitivity and specificity; Troponins; high sensitivity; Emergency medicine;
D O I
10.1016/j.tjem.2018.05.005
中图分类号
R4 [临床医学];
学科分类号
1002 ; 100602 ;
摘要
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. Copyright (C) 2018 The Emergency Medicine Association of Turkey. Production and hosting by Elsevier B.V. on behalf of the Owner. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
引用
收藏
页码:94 / 99
页数:6
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