Your browser has javascript turned off or blocked. This will lead to some parts of our website to not work properly or at all. Turn on javascript for best performance.

The browser you are using is not supported by this website. All versions of Internet Explorer are no longer supported, either by us or Microsoft (read more here: https://www.microsoft.com/en-us/microsoft-365/windows/end-of-ie-support).

Please use a modern browser to fully experience our website, such as the newest versions of Edge, Chrome, Firefox or Safari etc.

Mattias Ohlsson

Mattias Ohlsson

Professor

Mattias Ohlsson

A simple statistical model for prediction of acute coronary syndrome in chest pain patients in the emergency department

Author

  • Jonas Björk
  • Jakob Lundager Hansen
  • Mattias Ohlsson
  • Lars Edenbrandt
  • Hans Öhlin
  • Ulf Ekelund

Summary, in English

Background

Several models for prediction of acute coronary syndrome (ACS) among chest pain patients in the emergency department (ED) have been presented, but many models predict only the likelihood of acute myocardial infarction, or include a large number of variables, which make them less than optimal for implementation at a busy ED. We report here a simple statistical model for ACS prediction that could be used in routine care at a busy ED.



Methods

Multivariable analysis and logistic regression were used on data from 634 ED visits for chest pain. Only data immediately available at patient presentation were used. To make ACS prediction stable and the model useful for personnel inexperienced in electrocardiogram (ECG) reading, simple ECG data suitable for computerized reading were included.



Results

Besides ECG, eight variables were found to be important for ACS prediction, and included in the model: age, chest discomfort at presentation, symptom duration and previous hypertension, angina pectoris, AMI, congestive heart failure or PCI/CABG. At an ACS prevalence of 21% and a set sensitivity of 95%, the negative predictive value of the model was 96%.



Conclusions

The present prediction model, combined with the clinical judgment of ED personnel, could be useful for the early discharge of chest pain patients in populations with a low prevalence of ACS.

Department/s

  • Centre for Economic Demography
  • Division of Occupational and Environmental Medicine, Lund University
  • Medicine, Lund
  • Computational Biology and Biological Physics
  • Nuclear medicine, Malmö
  • Cardiology

Publishing year

2006

Language

English

Publication/Series

BMC Medical Informatics and Decision Making

Volume

6

Issue

28

Document type

Journal article

Publisher

BioMed Central (BMC)

Topic

  • Other Health Sciences

Status

Published

Project

  • AIR Lund Chest pain - More efficient and equal emergency care with advanced medical decision support tools

Research group

  • Nuclear medicine, Malmö

ISBN/ISSN/Other

  • ISSN: 1472-6947