Literature DB >> 19452341

Risk stratification for suspected acute coronary syndromes and heart failure in the emergency department.

W Frank Peacock1, Karina M Soto-Ruiz.   

Abstract

Many professional societies publish acute intervention guidelines, and most are predicated on the knowledge of an accurate diagnosis. In the emergency department patients do not arrive with a diagnosis, rather they present with symptoms that must be evaluated in the context of their estimated illness severity. Unique to emergency medicine practice, and within a relatively short time frame, all emergency patients must go somewhere else. Appropriate dispositions may be home, admission to a chest pain center, hospitalization to a regular medical floor, or transfer to an intensive care unit, but they cannot stay in the emergency department. This disposition process must occur, even in the setting of great diagnostic uncertainty. Since an accurate diagnosis is a time dependent event, requiring data collection and analysis, emergency department disposition decisions may be based on risk estimates rather than an established diagnosis. Owing to the subjective nature of the early evaluation process, biomarkers currently determine much of the risk stratification process. In this manuscript, we discuss the value of biomarkers as an adjunct to the diagnosis and risk stratification process for patients presenting to the emergency department with suspected acute coronary syndromes and acute heart failure.

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Year:  2009        PMID: 19452341     DOI: 10.1080/17482940902989068

Source DB:  PubMed          Journal:  Acute Card Care        ISSN: 1748-2941


  2 in total

1.  Poor performance of the modified early warning score for predicting mortality in critically ill patients presenting to an emergency department.

Authors:  Le Onn Ho; Huihua Li; Nur Shahidah; Zhi Xiong Koh; Papia Sultana; Marcus Eng Hock Ong
Journal:  World J Emerg Med       Date:  2013

2.  Prediction of cardiac arrest in critically ill patients presenting to the emergency department using a machine learning score incorporating heart rate variability compared with the modified early warning score.

Authors:  Marcus Eng Hock Ong; Christina Hui Lee Ng; Ken Goh; Nan Liu; Zhi Xiong Koh; Nur Shahidah; Tong Tong Zhang; Stephanie Fook-Chong; Zhiping Lin
Journal:  Crit Care       Date:  2012-06-21       Impact factor: 9.097

  2 in total

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