Identification of chest pain patients appropriate for an emergency department observation unit.

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2001-02

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Abstract

There are no perfect tests or algorithms to exclude ACI. Because acute coronary occlusion often occurs in patients with low-grade coronary stenosis, the diagnostic goal of a chest pain diagnostic protocol is not to identify patients with CAD, but rather to identify patients who may be safely discharged home without the development of complications such as MI, unstable angina, death, shock, or CHF over the next 1 to 6 months. There is an advantage to evaluating patients at the time of their symptoms. Patients who have a small plaque that is ruptured, leading to intracoronary thrombosis and ischemia, will manifest ischemia on diagnostic testing that could missed in routine outpatient testing when their plaque were stable. The diagnosis and risk stratification of acute coronary ischemia in the ED depends on a careful history and interpretation of the ECG. Multiple regression models using readily available data (e.g., history, physical examination, and ECG) provide the best tools for risk stratification. If one is deciding how to select patients for an EDOU chest pain evaluation, diagnostic tools that have previously been tested and validated in this setting are preferable. These include the Multicenter Chest Pain Study derived tools (i.e., Goldman, Lee), the ACI and ACI-TIPI tools, and sestamibi risk stratification tools. This is not to say that other tools may not play a role at individual institutions. It is probably better to select a consistent approach and evaluate its performance, rather than to allow random variation to dictate practice. The future direction probably will involve standardization of the ED chest pain population. This allows outcome and cost-effectiveness comparative research of various strategies for patients with normal or nondiagnostic ECGs and normal biomarkers. Although this approach allows more precise stratification, the risk will never be zero, meaning that there will never be a substitute for good clinical judgment and close follow-up care.

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Humans, Coronary Disease, Chest Pain, Treatment Outcome, Risk Assessment, Emergency Medicine, Observation, Clinical Competence, Emergency Service, Hospital, Hospital Units, United States, Female, Male, Guidelines as Topic

Citation

Published Version (Please cite this version)

10.1016/s0733-8627(05)70167-x

Publication Info

Wilkinson, K, and H Severance (2001). Identification of chest pain patients appropriate for an emergency department observation unit. Emergency medicine clinics of North America, 19(1). pp. 35–66. 10.1016/s0733-8627(05)70167-x Retrieved from https://hdl.handle.net/10161/26220.

This is constructed from limited available data and may be imprecise. To cite this article, please review & use the official citation provided by the journal.

Scholars@Duke

Severance

Harry Wells Severance

Adjunct Assistant Professor in the Department of Medicine

Site Principle Investigator: PROspective Multicenter Imaging Study for Evaluation of Chest Pain (PROMISE) prospective, randomized, multi-center clinical trial:

Principle Investigator - Duke E.D. Site - "Speed" Study. Pilot phase of Gusto IV. Investigating Abciximab (a GP IIb-IIIa inhibitor) in combination with rapid access to cardiac cath. Funded through Duke Clinical Research Institute. Multi-center trial.

Principle Investigator - Project: proposed mechanisms for afferent pain transmission from myocardial cells to pain centers. Purpose is to identify potential biochemical markers for early anginal presentations. Funded: grants received from Merck & Co. and Roche-Boehringer-Mannheim. Pilot phase paper - in preparation.


Other Interest Areas:
Blasts/Ballistics
Wounding and medical management of penetrating injuries derived from firearms and blast-related injuries.

Impact of Observation/short-stay strategies on clinical care and inpatient/outpatient systems.

Impact of Emerging Viral Threats on clinical management and social/economic/political systems,

Acute Cardiology - Chest Pain Presentations 
Evolving Technology and AI in improving clinical care/management


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