Back to Search Start Over

Enhanced triage for patients with suspected cardiac chest pain: the History and Electrocardiogram-only Manchester Acute Coronary Syndromes decision aid

Authors :
Alghamdi, A
Howard, L
Reynard, C
Moss, P
Jarman, H
Mackway-Jones, K
Carley, S
Body, R
Publication Year :
2019
Publisher :
Lippincott, Williams & Wilkins, 2019.

Abstract

OBJECTIVES: Several decision aids can 'rule in' and 'rule out' acute coronary syndromes (ACS) in the Emergency Department (ED) but all require measurement of blood biomarkers. A decision aid that does not require biomarker measurement could enhance risk stratification at triage and could be used in the prehospital environment. We aimed to derive and validate the History and ECG-only Manchester ACS (HE-MACS) decision aid using only the history, physical examination and ECG. METHODS: We undertook secondary analyses in three prospective diagnostic accuracy studies that included patients presenting to the ED with suspected cardiac chest pain. Clinicians recorded clinical features at the time of arrival using a bespoke form. Patients underwent serial troponin sampling and 30-day follow-up for the primary outcome of ACS. The model was derived by logistic regression in one cohort and validated in two similar prospective studies. RESULTS: The HE-MACS model was derived in 796 patients and validated in cohorts of 474 and 659 patients. HE-MACS incorporated age, sex, systolic blood pressure plus five historical variables to stratify patients into four risk groups. On validation, 5.5 and 12.1% (pooled total 9.4%) patients were identified as 'very low risk' (potential immediate rule out) with a pooled sensitivity of 99.5% (95% confidence interval: 97.1-100.0%). CONCLUSION: Using only the patient's history and ECG, HE-MACS could 'rule out' ACS in 9.4% of patients while effectively risk stratifying remaining patients. This is a very promising tool for triage in both the prehospital environment and ED. Its impact should be prospectively evaluated in those settings.

Details

Language :
English
ISSN :
14735695
Database :
OpenAIRE
Accession number :
edsair.core.ac.uk....4ef1d7205bbd8b2f6803edebfa2dc57f